Business Analyst Community & Resources | Modern Analyst

Business Analysis in the Age of AI

Pulkit Singhal — Fri, 01 May 2026 19:24:00 GMT

Business analysis work has become faster and more efficient over the past few years. Requirements are documented more quickly, discussions are summarized sooner, and solution options are produced earlier in the delivery cycle than ever before. Yet many Agile and product teams are discovering an unexpected truth: as delivery accelerates, the importance of human judgment increases rather than diminishes.

The central question facing business analysts today is no longer whether tools and automation belong in analysis work, but where judgment must take precedence. That distinction matters because the most serious failures in delivery rarely come from obvious mistakes. They emerge from reasonable decisions that appear correct at the time and gradually move teams off course.

Where Acceleration Helps and Where It Falls Short

Modern analysis practices are excellent at speeding up work that is inherently mechanical:

Converting discussions into draft requirements
Identifying patterns across large volumes of data
Refining user story language
Summarizing customer or stakeholder feedback

When used well, this removes low‑value effort from the analyst’s workload. When relied upon uncritically, it creates the illusion of progress.

The challenge is not poor quality output. The real risk lies in outputs that are clear, structured, and confident enough to pass surface review, while subtly reinforcing incorrect assumptions. This is where judgment becomes decisive.

Judgment Gap #1: Determining Whether a Requirement Is Worth Building

Clear and complete requirements do not guarantee meaningful outcomes.

In day‑to‑day delivery, analysts encounter familiar patterns:

A requirement addresses a visible symptom rather than the underlying problem
Stakeholders agree on wording but diverge on expected results
A feature meets acceptance criteria yet produces no behavioral change

Experienced analysts pause to ask questions that artifacts alone cannot answer:

What decision or behavior is supposed to change as a result of this work?
If this is delivered perfectly and nothing improves, what are we missing?

Strong analysis is not just about expressing requirements well, but about challenging their intent.

Judgment Gap #2: Interpreting Context That Never Appears in Documentation

Business environments contain layers of context that rarely make it into requirements or datasets:

Organizational dynamics and power structures
Regulatory concerns driving risk‑averse behavior
Legacy failures that shape stakeholder trust
Competing incentives across teams

Analysts recognize these signals not because they are documented, but because they have seen the downstream effects:

Solutions that are functionally correct but poorly adopted
Processes that are bypassed in practice
Reports and dashboards that exist but are ignored

Judgment here is not guesswork. It is pattern recognition developed through exposure to real consequences.

Judgment Gap #3: Recognizing When Clarity Creates False Confidence

Early clarity is often welcomed as momentum. Detailed backlogs, well‑defined flows, and polished models can make teams feel aligned and confident.

Seasoned analysts remain cautious.

They ask whether clarity is reducing uncertainty—or simply hiding it:

Are assumptions being locked in too early?
What would invalidate this design once it is tested?
Are open questions being resolved, or quietly deferred?

Sometimes the most responsible decision is to leave things deliberately unresolved, even when tools and processes encourage premature finalization.

What This Means for Business Analysts

As delivery mechanics become faster, the value of business analysis shifts away from producing artifacts and toward exercising judgment:

Framing the right problems
Interpreting conflicting signals
Evaluating consequences under uncertainty
Challenging assumptions before they harden

These capabilities are not procedural skills. They are developed through experience, reflection, and exposure to real outcomes especially failure.

Closing Thoughts

Modern tools and practices have made business analysis more efficient, but efficiency does not replace responsibility. The most effective analysts are not those who produce the most artifacts in the shortest time. They are the ones who know when clarity is helpful, when it is premature, and when the best contribution is to pause and ask a different question altogether.

That work remains deeply human and central to successful delivery.

Reinventing the Annual Member Survey: A Business Analyst’s Role in Delivering Actionable Insights

Pulkit Singhal — Sun, 25 Jan 2026 02:38:00 GMT

In a competitive and rapidly evolving financial landscape, understanding member needs is vital to maintaining strong relationships and delivering meaningful value. Yet for many institutions, especially those with legacy processes, collecting structured member feedback can be surprisingly underdeveloped. This was the case at the Federal Home Loan Bank of Chicago (FHLBank Chicago), where — despite its extensive engagement with member institutions — the Bank had never before conducted a structured, enterprise-wide Annual Member Survey.

Recognizing the need for a formalized feedback mechanism, the Bank launched an initiative to design and implement its first-ever Annual Member Survey, leveraging Salesforce as the foundational platform. As the Lead Business Analyst, I was responsible for envisioning, architecting, and orchestrating this new capability from the ground up.

This initiative ultimately became a defining example of how strategic business analysis can create net-new organizational capability, not just improve existing processes.

The Challenge: Creating a Strategic Feedback Framework from Scratch

Unlike most process-automation projects, this effort did not begin with an existing workflow to analyze or improve. Instead, the Bank faced a unique challenge:

No prior survey process existed
No historical data or response structures were available to benchmark against
No distribution, tracking, or reporting mechanisms had been established
No governance model existed for how results should be consumed
Stakeholders possessed varying assumptions about what the new survey should accomplish

This meant the project required not only systems expertise but also conceptual design, stakeholder alignment, and strategic framing.

My Role as Lead BA: Designing a New Enterprise Capability

The absence of an existing process meant that Business Analysis would shape the entire direction of the initiative. My responsibilities included defining the business problem, creating the process architecture, establishing data structures, and ensuring Salesforce could support a sustainable and scalable survey model.

1. Establishing the Vision and Framing the Purpose

Through interviews and collaborative workshops with Member Strategy, Sales, Analytics, and Leadership teams, I led discussions to answer foundational questions:

What insights should the Bank gather annually?
How should “member satisfaction” be defined in measurable terms?
What KPIs would create genuine value for leadership?
How should results be tied back to member institutions in Salesforce?

This work produced the Bank’s first Survey Vision and Strategy Framework, guiding all subsequent design decisions.

2. Building the End‑to‑End Survey Workflow in Salesforce

Because no prior workflow existed, I architected a brand‑new process designed around clarity, automation, and scalability:

Designed the survey creation and distribution model
Built logic for survey-to-member linking
Defined the response-collection data structure
Modeled the end‑to‑end visibility lifecycle, including assignment, participation, reminders, and results
Ensured dashboards would give leadership real-time insights

The process not only captured survey responses but also embedded insights directly into the Bank’s member management ecosystem.

3. Translating Ambiguity Into Clear, Actionable Requirements

Given the lack of precedent, requirements had to be derived through deep analysis rather than comparison. I authored:

Detailed user stories
Acceptance criteria
Process maps
Data models
Reporting definitions

This documentation became the foundational blueprint for developers, testers, and end-users — eliminating ambiguity and creating shared understanding.

4. Leading UAT and Validating a New Capability

Because the Bank had never conducted a survey like this, UAT required additional rigor:

I designed test scripts covering every stage of the survey lifecycle
Trained business stakeholders on how to test a process that was entirely new
Triaged defects and clarified user expectations
Ensured the system was intuitive and future-proofed

Through this, the Bank gained confidence not just in the technology, but in the process itself.

5. Supporting Rollout, Adoption, and Governance

Beyond system delivery, I worked closely with:

Member Strategy teams to formalize interpretation of results
Analytics teams to align on scoring and reporting methodologies
Change management teams to ensure smooth onboarding
Salesforce admins to embed long‑term maintainability

This ensured the survey became an annual, repeatable, institution-wide capability—not a one‑off project.

Conclusion: This project shows that Business Analysts are not just process improvers—they are capability creators.By clarifying needs, defining strategy, architecting processes, aligning teams, and ensuring quality, the BA function enabled FHLBank Chicago to establish a powerful new insight mechanism that will shape strategy for years to come.

The Annual Member Survey is now more than a project deliverable.
It is a permanent intelligence asset for the Bank — built on a foundation of Business Analysis leadership.

Which Business Strategy is a Priority Today?

Andry — Thu, 31 Oct 2024 11:11:00 GMT

In today's ever-evolving market, businesses must adapt swiftly to remain competitive and meet the needs of a fast-paced digital economy. Among the various business strategies available, digital transformation, customer-centricity, and sustainability have emerged as top priorities. Let’s explore why these strategies are critical for businesses today and how they shape competitive advantage.

Putting the Customer First
Customer-centricity is now at the heart of every successful business strategy. Companies that prioritize understanding and meeting their customers' needs can build trust and foster loyalty, essential for long-term growth. Today’s consumers expect personalized services, quick responses, and seamless experiences across multiple channels. By gathering customer insights, leveraging AI-driven personalization, and providing high-quality customer service, businesses can differentiate themselves and enhance customer satisfaction

Focusing on Sustainability
With environmental concerns on the rise, sustainability has become a strategic priority for many organizations. Consumers increasingly choose brands that are committed to environmental responsibility, leading companies to adopt eco-friendly practices. Whether it’s reducing carbon emissions, adopting circular economy models, or sourcing sustainable materials, businesses that integrate sustainability into their core operations can boost their brand reputation and attract conscious consumers.

Innovation and Agility
Innovation and agility allow businesses to pivot quickly and meet the changing demands of the market. Agile companies can quickly test and implement new ideas, allowing them to respond to shifts in customer behavior or technological advances. Innovation drives the development of unique products, services, and solutions that can position a business as an industry leader. Companies that prioritize a culture of innovation empower employees, encourage collaboration, and invest in R&D to stay ahead of competitors.

Conclusion
In a world of constant change, a successful business strategy involves a blend of digital transformation, customer-centricity, sustainability, and innovation. Businesses that prioritize these strategies are not only prepared for future challenges but are also equipped to thrive and lead in their industries.

Unlocking the Power of Data: Mastering Power BI

Wheeler55 — Wed, 26 Apr 2023 16:11:00 GMT

If you're looking to visualize, analyze, and share data insights with ease, Power BI might be the solution you need. In this article, we'll cover what Power BI is, why it's important, and how to get started with it.

What is Power BI?

Power BI is a business analytics service provided by Microsoft that helps users visualize and analyze data. It offers a range of features such as data preparation, data modeling, data visualization, and collaboration tools. It is a cloud-based service that allows users to access their data from anywhere, on any device.

Why is Power BI Important?

With the rise of big data, businesses need to be able to make sense of large amounts of data in order to make informed decisions. Power BI offers an easy-to-use platform that allows businesses to turn their data into insights. By using Power BI, businesses can quickly gain a deeper understanding of their data and make better decisions.

Benefits of Power BI

There are many benefits to using Power BI, including:

Easy to Use: Power BI has a user-friendly interface that makes it easy for even non-technical users to create and share reports.
Real-Time Data: Power BI can be configured to automatically update data, ensuring that users always have access to the most up-to-date information.
Customizable Visualizations: Power BI offers a range of visualizations that can be customized to suit the needs of each user.
Collaboration: Power BI allows users to share and collaborate on reports with colleagues, making it easy to work together on data insights.
Integration with Other Tools: Power BI integrates with a range of other tools such as Excel, SharePoint, and Azure, making it easy to work with existing data sources.

How to Get Started with Power BI

To get started with Power BI, follow these steps:

Installing Power BI Desktop

Power BI Desktop is a Windows application that can be downloaded for free from the Microsoft website. Once you've downloaded and installed the application, you can start creating reports.

Connecting Data Sources

To create a report in Power BI, you need to connect to a data source. Power BI can connect to a range of data sources such as Excel spreadsheets, databases, and cloud-based services like Google Analytics. Once you've connected to your data source, you can start creating visualizations.

Creating a Report

To create a report in Power BI, drag and drop the data fields you want to use onto the report canvas. You can then choose from a range of visualizations such as tables,Creating a Report (continued) bar charts, and line charts to display your data. You can also customize the appearance of your report by changing the colors, fonts, and layout.

Sharing and Collaborating on Reports

Once you've created your report, you can share it with colleagues by publishing it to the Power BI service. This allows others to view the report and interact with the data. You can also collaborate on reports by allowing others to edit the report or by creating a shared workspace where you can work together on data insights.

Best Practices for Using Power BI

To get the most out of Power BI, it's important to follow best practices. Here are some tips to help you get started:

Keep Your Data Clean

Before you start creating reports, make sure your data is clean and organized. This will make it easier to create accurate and meaningful reports.

Use the Right Visualizations

Choose visualizations that are appropriate for the type of data you're working with. For example, if you're working with time-series data, a line chart may be more appropriate than a bar chart.

Be Consistent with Naming Conventions

Use consistent naming conventions for your data fields, visualizations, and reports. This will make it easier to navigate and understand your reports.

Use Filters to Focus on Key Information

Use filters to focus on the most important data. This can help you identify trends and insights more quickly.

Power BI Tips and Tricks

Here are some tips and tricks to help you get the most out of Power BI:

Keyboard Shortcuts

Learn some of the keyboard shortcuts for Power BI to save time and work more efficiently.

Formatting Data

Use the formatting options in Power BI to make your reports more visually appealing and easier to understand.

Working with Time Intelligence

Learn how to use Power BI's time intelligence features to analyze data over time.

Using Bookmarks for Interactivity

Use bookmarks to create interactive reports that allow users to explore data insights in more detail.

Power BI vs. Other Data Visualization Tools

Power BI is just one of many data visualization tools available. However, it stands out for its ease of use, range of features, and integration with other Microsoft tools. Other popular data visualization tools include Tableau, QlikView, and Domo.

Conclusion

Power BI is a powerful tool that can help businesses make sense of their data. By learn Power BI, businesses can gain insights into their data and make better decisions. To get the most out of Power BI, it's important to follow best practices and learn how to use its features effectively.

Salesforce CRM Implementation- FAQs

Harry Johnson — Fri, 15 Apr 2022 05:42:00 GMT

Salesforce has established itself as one of the most reputable CRM platforms, providing important customer data to assist businesses in effectively managing their operations.

Salesforce is the world's best CRM platform that helps businesses to keep up the data in an arranged or structured manner.

Salesforce is the world's most popular CRM software, and it's designed to help businesses expand their business faster. It provides a variety of CRM Cloud Solutions, having the freedom to build and execute them according to your business requirements. Salesforce CRM is a comprehensive Customer Relationship Management (CRM) tool that manages all aspects of customer relationships. In this article, we'll go over the best practices for implementing Salesforce in your company.

Salesforce is a cloud-based customer relationship management technology that supports all available technologies to help businesses improve business processes and satisfy customers. It is one of the market's leading cloud-based CRM systems. It covers all aspects of your organisation, including customer engagement, service, sales, commerce, and marketing.

Salesforce's services enable organisations to engage with customers, partners, and potential customers using cloud technology. The software has become the number one for customer success and helps companies to track customer activity, market to customers, and many more services.

Let's discuss the below FAQs on Salesforce CRM Implementation:

What is Salesforce CRM Implementation?

The process of implementing customer relationship management (CRM) software so that an organisation may manage current and potential customer interactions is known as CRM implementation. And Implementation of Salesforce product with the existing CRM known as Salesforce CRM Implementation. CRM implementation that goes well can help firms/companies/enterprises analyse their client interactions, monitor leads efficiently, and streamline operations to make a successful business.

Why Should You Implement Salesforce?

Salesforce's infrastructure provides a number of native solutions for sales and marketing professionals, such as Sales, Service, and Marketing Cloud. Aside from that, it has thousands of third-party solutions on AppExchange (Salesforce's software market) that are meant to improve the operational power of the core platform.

Because of Salesforce's popularity, there are plenty of experts available to assist you with the setup and customization of your solution. So, if you're new to CRM or don't have the necessary tech skills, you may easily assign these chores to skilled Salesforce implementation consultants who are well-versed in the platform.

How do I prepare for Salesforce CRM Implementation?

The following steps listed below will help you prepare for Salesforce CRM Implementation.

BE SURE OF YOUR OBJECTIVE & VISION

Everyone recognises the importance of having a clear vision for success. Regrettably, not everyone takes the time to make things as plain and understandable as possible. To achieve success, be clear and focused on your vision.

COMBINE/GATHER YOUR TEAM

On what team is this project being worked on? Who is in charge of this project? Who is in charge of this project? Make it clear to your team who is in charge of what, and divide tasks according to the team's skills. Make your intentions clear from the start.

START RECORDING YOUR PROCESSES

Spend some time getting your documentation and workflow in order. Once the documentation is complete, you'll have a clear path to success.

FORM AN ELECTRONIC COMMUNICATION PLAN

The key to success is communication. Communication is the single thing that separates a good team from a great one. As a result, communicate as needed, clear all doubts before advancing, keep team members up to date on tasks completed, and so on.

SET REASONABLE OBJECTIVES

Your objective must be measurable and achievable. Create a plan that completely describes your vision and goal, as well as how to attain them and the metric to use to calculate them.

GET TO KNOW YOUR INFORMATION

Analyse your data - your data must be error free, concise and measurable.

What are the steps for Salesforce CRM Implementation?

Following are the steps involved in Salesforce CRM Implementation.

Consult a Salesforce Consultant

Decide who will be in charge of the project.

Establish a Process Timeline?

Determine the most important decision-maker/objective

Obtain information from end-users

Monitor/Analyse The Data

Begin the implementation process in stages.

Review Implementation on a Regular Basis

How does a company implement Salesforce?

The following steps are involved while Implementing Salesforce CRM:

Define Stakeholders

Define Goals

Collect Requirements/Data

Analyse Data

Prioritise Work

Start Building

Testing & Implementation

Finalise the Implementation.

Where do I get the best Salesforce CRM Implementation Services?

If you are looking for the best Salesforce Implementation Services that can assist you, make a plan for you, prepare a roadmap, and help you succeed, then Emorphis Technologies is the best in it.

Because proper Salesforce installation is critical to any firm's success before we implement the solution for your company, we will first learn about every area of your business and your Salesforce expectations, and then we will perform the implementation services for you.

Top 10 Mistakes in Requirements Elicitation

Trividh Patel, CBAP — Tue, 27 Apr 2021 05:47:00 GMT

Elicitation involves bringing out or drawing out information. Elicitation is a key task in business analysis as without proper elicitation the requirements for the solution to the business needs cannot be identified.

1. Not understanding underlying business need

Organization’s business environment keeps changing with respect to Customers, Marketplace, Technology and Marketing function. It is these changes in business environment that leads to identification of business needs at the strategic level in terms of problem or opportunity faced by the organization.

Defining business needs is the most important step in business analysis. Without understanding and defining underlying business needs, it would not be possible to identify all affected stakeholders and elicit appropriate requirements.

2. Not identifying all affected stakeholders

It is important to identify all the stakeholders affected by the given business need. If any stakeholder is identified late (or worst not at all!) may lead to incomplete set of requirements and could require a revision to requirements increasing project cost and time.

3. Treating elicitation as a phase

I have found many Business Analysts consider elicitation as a phase after planning (and before requirements analysis). But this is not true. If you think little more deeply, information gets elicited whenever we interact with stakeholders such as sponsor, domain subject matter experts (SMEs), implementation SMEs, users etc.

Elicitation is performed to understand the current state and elicit business requirements. Business requirements are used when eliciting stakeholder, solution and transition requirements. During requirements analysis, we may identify gaps which would require further elicitation. Information is also elicited from the stakeholders about solution performance after implementation of a new solution.

So elicitation is performed on an ongoing basis as long as business analysis work is performed.

4. Not asking probing questions to elicit requirements

Many novice Business Analysts assume stakeholders can proactively provide all the detailed information required for the business analysis work. Such a passive approach can be called requirement gathering but not an elicitation. Such an approach can only lead to identification of shallow requirements.

It is the job of the Business Analyst to extract or draw out the detailed requirements from the minds of the stakeholders. Business Analyst need to ask probing questions to elicit detailed requirements.

5. Not setting stakeholder’s expectations

In your career as a Business Analyst, at times you would find some stakeholder who would state their wants (whims and wishes!) as if they are their needs and expect them to be in the solution. You may find their expectations not only difficult but impossible. If you capture their wants as requirements it would be difficult later on to deliver to their expectations.

With your interpersonal and negotiation skills you need to communicate and set the right expectations.

6. Not using combination of complementary elicitation techniques

I have seen many Business Analysis teams often rely only on one technique such as interviews for elicitation. While interviews is the most effective elicitation technique but its effectiveness depends on the skills of the Business Analyst such as business domain knowledge and ability to ask probing questions.

So, apart from interviews, a Business Analyst should have knowledge of other commonly used fundamental requirements elicitation techniques such as Document Analysis, Observation and Prototyping. While a senior Business Analyst should have knowledge of advanced elicitation techniques such as Brainstorming, Focus Groups, Requirements Workshops and Surveys.

A Business Analyst should be able to understand the given situation and use combination of complementary elicitation techniques.

7. Not eliciting assumptions and constraints

Requirements are often stated (knowingly or unknowingly) based on certain assumptions which are believed to be true at that time. Requirements get impacted if those assumptions are later found to be false.

Constraints are limitations or restrictions (such as regulatory restrictions, budgetary restrictions, time restrictions etc) that restrict potential solutions to requirements. Identified potential solutions may change if there are any changes in the constraints.

If underlying assumptions and constraints are not captured for requirements, it would be difficult to assess impact on requirements if certain assumptions are later found to be false and/ or on potential solutions if constraints are changed.

8. No plan to elicit requirement iteratively

In order to elicit requirements, a Business Analyst contacts a stakeholder and requests their time. Many Business Analysts do not plan to elicit requirements iteratively and assume that stakeholders will provide all the information required for the business analysis work.

However, most of times, stakeholders are not aware why they are being contacted. After their initial meetings, stakeholder will have some idea what is expected out of him/ her. In the subsequent meetings, stakeholder is likely to give bit more detailed information. So, in order to elicit detailed information, Business Analyst needs to plan to elicit requirement iteratively.

9. Not confirming the elicited information

Work of elicitation is not over once Business Analyst is done talking to stakeholders. Business Analyst has to organize the elicited information and send it to the stakeholders for review. The purpose is to check if discussion has been properly documented and confirm the elicited information.

10. Not collaborating with stakeholders to have common understanding of requirements

When the elicited requirements are shared with stakeholders, there can be difference of opinions and conflicts between stakeholders. A Business Analyst has to collaborate, mediate and resolve conflict between stakeholders to reach a common understanding of requirements. Business Analyst should identify the stakeholder’s problems and help to identify solutions to satisfy those problems.

About Author - Trividh Patel, CBAP

Trividh Patel has about 20 years of experience in Business Analysis and Consulting in IT services industry.

Currently, he is working as Facilitator and Mentor - Business Analysis providing self-paced online courses in Business Analysis. Previously, he has worked for leading IT Services companies as Business Architect, Lead/ Sr. Business Analyst, and as IT Project Manager. He has executed several business analysis projects for reputed organizations from USA, UK, Europe, Middle East, Japan and India. He has good track record of leading team of Business Analysts to deliver business analysis projects.

Trividh Patel has done MBA in Systems and Bachelor of Engineering from University of Mumbai (India) and is Certified Business Analysis Professional (CBAP) by International Institute of Business Analysis (IIBA), Canada since March 2012. He is also Certified Six Sigma Black Belt.

For Consulting, Coaching or Guidance on IIBA Certification (or just to connect!), Trividh Patel can be reached on LinkedIn: http://www.linkedin.com/in/trividhpatel-cbap

What is TOGAF and How to Get a TOGAF Certification

Lori Smith — Sun, 11 Apr 2021 04:01:00 GMT

TOGAF is a certification that is handed over by The Open Group. It is an open corporate architecture means used to improvise upon the business effectiveness across the world’s leading business set-ups. Aspirants wishing for a successful career in corporate architecture must go for the TOGAF certification in order to explain their ideas as well as knowledge of architecture.

TOGAF came into existence in the early 1990s as a method to enhance the technical architecture and later got modified by The Open Group into a different as well as promising architecture chain of systems. The first prepared version of TOGAF was launched into the market in the year 1995.

The TOGAF 9 certification is made accessible to the candidates in basically two levels, namely the Foundation (which is Part 1) and the Certified (which is considered as Part 2). However, there are several different techniques or paths that are explained by the Open Group in order to achieve these advanced certifications

Eligibility sector and the requirements for the same

For TOGAF Foundation (The Part 1) exam, there is absolutely no eligibility sector or needs for the same. All the desiring individuals are well eligible as well as can manage to hike up the examination.

For TOGAF Certified (The Part 2) exam, individuals must be verified as well as equipped in TOGAF Foundation or must have a minimum required mark for the TOGAF Foundation exam from the same supplier (which is also framed as the ATTC provider) that they wish to take up the Part 2 of the examination.

Exam methods or guide to the exam (Some basic tips)

As mentioned earlier in other sectors, individuals can either make up all by themselves or choose to attend several training at an improvised as well as a well-recognized institute.

Individuals who have already registered with the Prometric heads and are further looking to make ready all by themselves can avail certain advantages from the study guide that is available on the Open Group official website. It is always available for online teaching as well as gathering knowledge in the Open Group Bookstore (or the OGB) as well.

However, most of such candidates prefer getting enrolled in a training period with certain renowned institutes like the Sprintzeal. Apart from availing of such training courses, these organizations also include in their study material examination fee either on the official site or as a voucher will be presented in order to attend a Prometric test head.

Examination framework or chain of systems

The TOGAF certification is attained only after successfully completing the respective examinations, as mentioned below.

Part 1 of the exam (This has a base pass mark of at least 55 % for the same reason)

- 40 inquiries (which are all of the multiple-choice types)

- Each successful choice answer counts as one point

- A minimum of 22 points should be achieved.

Part 2 of the exam (This has a moderate pass percentage, which sums up to be around 60 % for the appropriate qualification)

- 8 inquiries which are purely based on hectic scenarios with subtle scoring

- Each correct answer sums up to be 5 points, the second-best solution scores to be 3 points, and the third-best outcome adds up to be 1 point.

- Open book system (An important point)

2 basic keys as well as beneficiary ideas to be well remembered

* Collect decisive information about the Agile Development program.

TOGAF depends heavily on the basic agile rules as well as techniques of continuous and statistical enhancement, including the development field. Having a solid grounding in agile deep-thinking methodology will assist you in understanding how to apply TOGAF as well as preferably the ADM as well.

* Be sure of the outcomes of the basic study guides.

The Open Group has given two such basic guides, one for each section of the two parts of the exam. You should at least grasp all the ideas from Part 1 because it covers all the possible ideas that could possibly appear in the Part 1 exam. And within some 180 to 200 pages, it is a far precise guide as well. If you have enough remaining time, you should also go for Part 2 of the same and give it a good read.

AI in Testing: Do You Need It? This Article Will Help You Decide!

Rajesh-N — Wed, 28 Oct 2020 07:03:00 GMT

What Everyone Must Know about AI in Testing

Artificial Intelligence is the buzzword that we frequently keep hearing. Its widespread popularity and influence can be understood from the way industries adopting AI in their organization. Whether it’s Healthcare, Automobile, Banking & Financial Services, or Airlines, many industries have started adopting AI.

Artificial Intelligence has not only impacted industries, it has also empowered the SDLC for faster development and deployment. The combination of automation testing and AI has numerous advantages.

Accelerates Timelines

Software testing needs a notable amount of time to validate the quality of an application. As manual testing takes more time, automation testing supports in handling repetitive tasks, with a greater focus on the new implementation to catch the defects faster. Similarly, in addition to automation testing, AI can support automation by creating faster tests (test generators), handling changes (Self-healing tests), and reducing the number of lines of code.

Increases Test Coverage

Automation testing handles repetitive tasks. But with the increase in the number of application technology, test automation tools have difficulty in adapting to those changes. As a result, the test coverage using automation has been moderate for a long time. Due to AI-Powered visual recognition, automation tools are now capable of handling the application, which in turn increases the test coverage.

Improves Accuracy

The quality of the product is ensured through testing. And the accuracy of testing is ensured by QA. But to err is human because even an experienced tester can commit mistakes while performing a repetitive task. Automation testing prevents those mistakes, and an increase in the volume of data for repetitive tasks never impacts the quality of the test. AI-generated data supports creating accurate data and reduces time.

AI and testing have come closer than one can imagine. The implementation of the new features in the existing automation tools or the evolution of new automation tools with AI features are its best proof.

Tricentis – AI Engine NEO
SmartBear – ReadyAPI - AI Driven Test Generation
SmartBear – TestComplete - Hybrid Object Recognition Engine
Parasoft – Smart Generator
Eggplant
Percy
Applitools
Mabl
Functionize
ReportPortal.io
Healenium

Does Artificial Intelligence (AI) Replace Traditional QA Testers?

A lot of thinking persists as to whether AI will replace the QA testers' job. However, that’s not the case. In fact, AI will never replace the role of a tester. Testers will be able to test better with the help of AI-powered tools.

There are few common things between traditional testing and AI,

Traditional Testing:

Prepare Test Cases
Prepare Test Data
Test Execution

AI-Based Software Testing:

Prepare Test Data
Analyze and Clean the Test Data
Train the Test Data by Creating AI Models by Running the Regression Suit

Key Areas of AI in Testing where Traditional QA Testers can work

Regression Optimization:

Modelling the Regression Suit
Solutions for:
- Test Suit Impact for CR
- Patchwork
- Upgrade on the applications
Automated prioritization of test cases/scripts-based on machine learning

Defect Analytics:

AI-based sentiment analytics to find the requirement/positive and negative scenarios for better decision making
Real-Time Dashboard for displaying the current status of the Bug
Analytics-driven workload modelling for
- Defect prediction
- Code coverage
- Response time
- Scalability prediction

Smart Automation Testing:

On every release, find the change detections in the object parameters across the scripts
Self-healing scripts, when there are new CR/Upgrade/Patches
The dashboard on the Requirement Coverage system

Performance Testing:

Workload modelling and Response Times Analysis by ML Modelling
Agent-based System Modelling will be handled for Performance Testing

Conclusion:

Being a QA, either we can use AI for testing, or we can start testing AI. For both of these, we need to understand AI. Let’s explore AI.

This article was originally published here.

Robotic Process Automation - Business Game Changer in 2021

Rajesh-N — Fri, 25 Sep 2020 11:53:00 GMT

Explore the Benefits of RPA's Adoption Across Segments

In the past few years, we have seen steady growth in Robotic Process Automation (RPA). It gathered momentum in 2019 and became a hot topic in IT and business circuits in 2020.

The power of process automation will be felt even more strongly, propelling its adoption as a replacement for legacy processes. McKinsey, in its article, ‘Harnessing automation for a future that works,’ estimates that about half of all the activities people are paid to do in the world’s workforce could potentially be automated by adopting technology. This would result in 60 percent of all occupations to have at least 30 percent technically automatable activities.

RPA's Adoption and Benefits across Segments

Banking and Financial Services

Banking and Financial services have become commoditized, and banks are mandated to focus on improved front-end and back-end services. Front-end has already seen the adoption of RPA. However, in the recent past, we have witnessed wide-scale adoption of RPA to manage back-end processes. Repetitive rules-driven tasks are now being managed by RPA, bringing down errors, time, and costs.

Insurance

Insurance companies typically have overwhelming processes, multiple applications, and regulatory requirements. RPA has helped to automate data entry enabling on-the-go calculations followed by validation which is extended to branches through self-service. It has helped to manage high volume processes related to claims and underwriting, quotes, and payments related to policy surrender, bringing down turnaround time significantly.

Retail

The retail industry has, in many ways, been at the forefront of RPA adoption. It is not uncommon to have physical robots working alongside humans in warehouses and retail outlets. Along with this, based on the fact that online sales in the US can grow to double its size by 2023, there are numerous functions that RPA has already infiltrated, such as order processing, procurement, inventory management, and payments. It is helping retailers to manage complex supply chain relationships by automating workflows between manufacturers, suppliers, and customers.

Manufacturing

The manufacturing industry has witnessed the widespread adoption of RPA to reduce errors across various functions. In Bills of Materials, for example, where various components such as sub-assemblies, components, and raw materials come into play, RPA reduces human errors. Similarly, with compliance, RPA has simplified the process while maintaining updated requirements for adherence. Data migration and automated communication are the other RPA empowered areas that are transforming traditional manufacturing companies into lean and nimble companies.

Education

As the education industry continues to transform, its surging need is to manage online and offline correspondence with students, staff, and stakeholders. RPA intelligently manages these relationships, ensuring faster and smarter turnaround times.

A Brave New Bot World for Businesses

In 2021, there will be more RPA bots deployed across industries, i.e., wherever repetitive manual actions are being performed. It will allow business leaders to improve their performance and venture into new territories. This would be a direct outcome of lowered labor costs, better processes, and improved quality.

To maximize the potential of RPA to turnaround their businesses and prepare for expansion, business leaders will have to start at the ground level. A thorough inventory of processes across the organization will be required to identify all the processes that can be automated.

The question that arises is, ‘how will process automation help to grow businesses?’ Robotic automation will result in labor displacement. Organizations and their leaders must review the opportunities for business growth and strategically deploy their workforce to achieve business goals. New roles, new activities, and new opportunities are the benefits of RPA that 2021 would bring along.

Humans versus Bots

RPA from being a buzzword will become a practical reality for workers who will need to familiarize themselves with the technology to maximize its potential. The ability to let go of their tasks and watch machines performing the same tasks will be challenging if it is not dealt with in the right way. Therefore, 2021 will see senior executives sharing their business visions more openly within their organizations to show their employees the new vistas of opportunities that lie ahead - opportunities that will require strategic thinking and smarter ways of working.

Organizations that view technology disparately will have to rethink their strategies. The human factor will play a pivotal role not only in the adoption of RPA but in how the displaced workforce uses their abilities to help achieve business visions. To simplify, the human factor will have to take precedence over automation, and careful evaluation of existing human resources is required to ensure that organizations are not just bot processes but ‘intelligent bot-enabled powerhouses.’

A Surge in Need for RPA Technology Solution Providers

As with any new technology innovation, there is likely to be a surge in demand for technology professionals with RPA expertise. This could create new work opportunities and even lead to outsourced demand for rpa service providers. Technology workers whose roles require repetitive programming will now have to retrain and prepare for roles that RPA will create. The need for new technology professionals will see a spurt in the education industry as it prepares to train and certify robotic automation professionals.

AI Enabled RPA

2021 will see businesses grappling with robotics and AI and trying to figure out the difference. Without a clear understanding of the difference, there will be breaks in processes that will adversely affect business development. Bots, as we know, perform repetitive tasks mindlessly. For tasks that require more intuitive predictions, AI will be the answer. To understand how and where to deploy AI versus RPA will require expert intervention from technology solution providers. Spending on RPA software is predicted by Gartner to hit $1.3 billion in the coming year. Will all of this result in business benefits, will be a question mark with the answer hidden in its maximized and correct usage.

Final Thoughts

To summarize, 2021 will see wider adoption of Robotic Process Automation across industries. It will help businesses to shift their expenses from process management to business empowerment, provided they can manage and maximize the existing workforce responsibly. It makes more sense to have bots managing customer queries and offering faster responses to complex questions. For employees, RPA promises to take the mundanity out of boring tasks, opening the space for creative thinking. 2021 will be a game-changing year for the IT world with more opportunities for humans to tap on innate skills that machines cannot replicate.

The companies that have already begun their RPA journey have witnessed significant benefits, and they will have an undue advantage over the competition in the upcoming year.

This article was originally published here.

WHAT IS THE IMPORTANCE OF USING PYTHON FOR DATA SCIENCE?

jainsaniya — Fri, 25 Sep 2020 07:06:00 GMT

Python is a flexible programming language that is primarily favoured by software engineers and tech organizations around the globe, from new businesses to the bigger ones. Data Scientists use it widely for data analysis and knowledge generation, while many organizations pick it for its convenience, extensibility, coherence, transparency, and the completeness of its standard library.

Python programming skills are in great demand and learning it can open ways to endless opportunities in Data Science, Machine Learning, Artificial Intelligence, web development, and much more. The Python for Data Science is applied to-

Candidates with no background of coding or want to explore their career in Data Science
Candidates who are currently or planning to enroll in Data Science field
Candidates who are currently or planning to enroll in Machine Learning industry
Candidates who are currently or planning to enroll in Artificial Intelligence industry

Python for Data Science is used to build strong skills in the basic concepts that are mandatory for Data Science. It includes data handling, feature engineering, statistical analysis, and Python programming. When you start building for the foundation in Python for Data Science, you build and implement predictive analytics algorithms (regression and forecasting), and classification and segmentation Machine Learning algorithms (K Means and Random Forest) using Python. Python with Data Science allows flexibility and makes integration of programming easy with complex systems. This is all because of the Python programming which is very dynamic and portable and due to this, the use of Python is wide in the industries.

Understanding the use of Python in Data Science

Consistently, around the United States, in excess of 36,000 climate estimates are given covering 800 unique locales and urban areas. If it starts raining in the middle of your outing but it was assumed to be a sunny day! Such forecasts around the country predict the usage of forecast models. The people at Forecastwatch.com did! Consistently, they accumulate every one of the 36,000 estimates, put them in a database, and compare them with the actual conditions experienced in that area on that day. Forecasters around the country at that point utilize the outcomes to improve their forecast models for the next round. All this collection of data, analysis, and reporting takes a lot of time but Forecastwatch.com does it with the use of a single programming language: Python.

Not only Forecastwatch.com but according to the industry analyst survey of O’ Reilly, 40% of the data scientists make use of Python for their day-to-day work. Trusted organizations such as Google, NASA, and CERN use Python for almost every programming purpose under the sun… including, in increasing measures of data science.

Python: the new life of Data Science

Algorithms or predictions made in Data Science are very tricky and mind-boggling. If the programming language will also be tricky then a data scientist would use his maximum time in coding and syntax only. Even Java can be used for Data Science. But if the comparison between two programming Languages is done then Python will be a winner. Because of the small code snippets, Python is the most loved language for Data Science and this is the primary reason for utilizing Python for Data Science. See the example below:

Hello world program in Java:

class Prog {

public static void main(String args[]) {

System.out.println(“Hello World, Java!”):

}

Hello world program in Python:

print(“Hello World, Python!”)

Python’s inherent readability and simplicity make it relatively easier to pick up. The number of dedicated analytical libraries available in Python means that data scientists in every sector will find packages already tailored to their needs freely available for download. Due to Python's extensibility and universally useful nature, it is unavoidable as somebody would inevitably begin utilizing it for data analysis. As a jack of all business sectors, many organizations as of now intensely invest in the language they saw preferences to normalizing on it and extending it to that reason.

Python: a bank of libraries for Data Science

There are a lot of programming languages available in the market. But Python has grown up with almost 72,000 Python Package Index (PyPi) and it is kept on growing constantly. With Python explicitly intended to have a lightweight and stripped-down center, the standard library has been developed with tools for each kind of programming task- a "batteries included" philosophy that permits language clients to rapidly get down to the stray pieces of tackling issues without filtering through and choose between competing function libraries.

Moreover, the Pandas Python library is used for the data analysis that is, doing everything from importing data from Excel spreadsheets to processing sets for time-series analysis. Pandas put basically every normal data munging tool readily available. This implies fundamental cleanup and some advanced manipulation can be performed with Pandas amazing dataframes. Pandas is based on NumPy, perhaps the earliest library behind Python's data science success story. NumPy's capacities are uncovered in Pandas for advanced numerical analysis.

If you are looking for advanced, then-

SciPy is the scientific equivalent of NumPy, offering tools and techniques for scientific data analysis
Statsmodels focuses on tools for statistical analysis
Scilkit-Learn and PyBrain are machine learning libraries that provide modules for building neural networks and data preprocessing

And here are the peoples’ favorites:

SymPy – for statistical applications
Shogun, PyLearn2 and PyMC – for machine learning
Bokeh, d3py, ggplot, matplotlib, Plotly, prettyplotlib, and seaborn – for plotting and visualization
csvkit, PyTables, SQLite3 – for storage and data formatting

It is called ‘Pythonic’ when the code is written in a fluent and natural style. Apart from that, Python is also known for other features that have captured the imaginations of the data science community.

How Python is the perfect fit

There are customized circumstances where it is the best data science tool for the activity. It is impeccable when data analytics undertakings include integration with web applications or when there is a need to incorporate statistical code into the production database. The full-fledged programming nature of Python makes it an ideal fit for implementing algorithms. Python’s packages rooted in explicit data science occupations. Packages like NumPy, SciPy, and pandas produce great outcomes for data analysis employments. While there is a requirement for graphics, Python's matplotlib emerges as a decent package, and for Machine Learning undertakings, scikit-learn turns into the perfect substitute.

Is Python used for Machine Learning?

With regard to data science, machine learning is one of the noteworthy components used to expand the value of data. With Python as the data science tool, investigating the essentials of ML turns out to be simple and powerful. More or less, ML is progressively about statistical, mathematical optimization, and probability. It has become the most favored ML tool in the manner it permits competitors to 'do maths' without any problem.

There is Numpy for numerical straight linear algebra, CVXOPT for convex optimization, Scipy for general scientific computing, SymPy for symbolic algebra, PYMC3, and Statsmodel for statistical modeling.

With the grasp on the nuts and bolts of ML algorithms including logistic regression and linear regression, it makes it simple to actualize ML frameworks for forecasts by the method of its scikit-learn library. It's easy to customize for neural systems and deep learning with libraries including Keras, Theano, and TensorFlow.

Career path for the use of Python in Data Science

As per the research, the journey to learn Python for Data Science will be a 4-week journey. You don’t need to understand Python like your own kid, simple basics will be sufficient. If you follow the below career path then you will attain a core level understanding of Python for Data Science.

Week 1:

Basic of Python Spyder
Introduction Spyder
Setting working Directory
Creating and saving a script file
File execution, clearing console, removing variables from environment, clearing environment
Commenting script files
Variable creation
Arithmetic and logical operators
Data types and associated operations

Week 2:

Data Structures
List
Tuples
Dictionary
Sets
Numpy
Array
Matrix and associated operations
Linear algebra and related operations

Week 3:

Pandas dataframe and dataframe related operations on Toyota Corolla dataset
Reading files
Exploratory data analysis
Data preparation and preprocessing
Data visualization on Toyota Corolla dataset using matplotlib and seaborn libraries
Scatter plot
Line plot
Bar plot
Histogram
Box plot
Pair plot
Control structures using Toyota Corolla dataset
if-else family
for loop
for loop with if break
while loop
Functions

Week 4:

Case Study
Regression
Predicting the price of pre-owned cars
Classification
Classifying personal income

Projects and Further Learning

To truly become acquainted with technology and to learn Python for Data Science, you should create something in it. Odds are, you will stall out on your way, and each time you stall out, you will discover out all alone. Start with issues accessible on the Internet, and build your skills. At that point, think of your own issues, characterize and illuminate them. We additionally recommend that you take a glance at deep learning. It is a subfield of machine learning utilized for algorithms roused by the structure and function of the mind called artificial neural networks.

How long will it take to learn Python for Data Science?

There are a ton of evaluations for the time it takes to learn Python. For data science explicitly, estimates a range from 3 months to a time of predictable practice. We've watched individuals travel through our courses at lightning velocity and other people who have taken it much slower. Truly, everything relies upon your ideal timeline and free time that you can commit to learning Python programming and the pace at which you learn. Codegnan’s courses are made for you to go at your own speed. Every way is loaded with missions, hands-on learning, and chances to pose inquiries so you can get a top to bottom dominance of data science essentials.

Additionally, Python is not the single thing you need to learn for Data Science, but there are the following couple of things that are required to learn:

Data visualization using Matplotlib
SQL with Python
Statistics with Python

Final thoughts

The width and height of Data Science are growing constantly and the tools used for value extraction from Data Science have also increased in numbers. With the advancement of technologies such as artificial intelligence, machine learning, and predictive analytics, the demand for experts with Python skills is rising significantly. It is widely used in web development, scientific computing, data mining, and others. Learning Python programming will give you versatility and competence as a data scientist. Good luck readers!

6 things to keep in mind before investing in your next mobile app idea

Deniz John — Fri, 14 Aug 2020 08:09:00 GMT

Got a brilliant mobile app idea and want to turn it into reality? Well, that sounds great! As the app market is booming like never before, and people are increasingly turning to mobile apps, it is certainly the right time to invest in the mobile app. Having thousands of downloads and making millions out of your app is definitely worth giving a shot. However, with thousands of new apps hitting the market, fierce competition has also led many applications to fail. Mobile app development is a big investment and requires immense efforts and resources.

Thus, before you decide to turn your app ideasinto reality, and invest your hard-earned money, here are six things that you must keep in mind:

1- Carry thorough market research:

Before you start with the app development process, it is vital that you go through detailed market research. By analyzing and researching the market you will gain knowledge about the existing competitors, similar apps in the market, their marketing strategies, customer base, failures, and more. This will help you deal with the concerns that may arise while you get your app developed. As you research similar apps in the market, you must also think of ways to make your app better and make it stand out. Similarly, if there are similar apps that failed, you must inquire about the reasons why they failed to make sure that you don’t commit the same mistake.

2- Set a tentative budget:

Mobile app budgeting is the most crucial part while you plan to get your own app developed. You must determine all your expenses that you would incur while creating the app. The cost of creating your mobile app primarily depends on the app features you want to incorporate, the number of developers required to build an app, developer rate, technology stack, and marketing strategies. After having a tentative budget in mind, you must think of ways to arrange the required capital by contacting the right investors or find other ways to generate funds.

3- Know your target audience:

While developing your app, always make sure to clearly define your target audience. Which age group is going to use this app? How will your app help them? Will your app be able to meet their expectations? You must be able to answer these questions and get started with your app accordingly. You must keep all the preferences of your target audience in mind and then plan to develop your app accordingly. The more clearly you understand your target audience, the more successful your app would be!

4- Put forward your app specifications:

As you plan to develop your app, you must lay the key features in the mobile app in advance. You must put forward what your app has to offer to attract its customers. For example, if you are looking forward to developing a Whatsapp like app, you must define what features you will incorporate in your chatting app like video calls, audio calls, themes, encrypted chat, location sharing, media exchange, among others. Thus, will clearly define what all will your app offer to its users. If you already know what all features you will have in your app, you can define your app goals with the development team. However, you should keep in mind that the more features you incorporate in your app, the more will be its development cost. If the specifications are farewell defined, the app will be used and recommended to others as well.

5- Choose your development team wisely:

The development process decides the fate of your application. Even the slightest bug in the app can turn down your mobile application and break the trust of the users. Thus, it is important that you choose your development team wisely and approach a renowned development company. Before finalizing the developer, you must also check the past work done by him/her and give a look at the feedback & reviews provided by the clients.

6- Chalk out the marketing strategy in advance:

Developing the app is not enough, if you do not have an excellent marketing plan, it can fail terribly. As thousands of apps launch in the app, only a few apps are successful. Apart from the app’s user interface and utility, the next thing that decides the success of the app is its marketing strategy. A solid market strategy will make sure that the app reaches its target audience and attracts the eyeballs of the audience. Leveraging on influencer marketing, app store optimization, affiliate marketing, among other marketing strategies will ensure that your app stays in the limelight and stays ahead of the competition.

Apart from the above-mentioned points, there are several other things that you should consider including the time frame to develop your app, app security, cross-platform technology, etc. Once you have the right development team on your side and have a clear vision of your next mobile application, you can be sure to turn your app idea into a full-fledged app and launch it with a bang!

Robotic Process Automation: Benefits of RPA to Power-Up Business Prospect

emorphistechno — Thu, 16 Jul 2020 12:28:00 GMT

Introduction

In today's world, most enterprises work aggressively to achieve a higher level of business growth, which is made possible by leveraging one of the best automation technologies. One such technology is Robotic Process Automation (RPA) that plays a vital role in streamlining the customer experience in the most profitable manner.

With the arrival of RPA software, there is an offering of new technological salvation to the companies across the globe to improve their efficiency and business productivity. Moreover, the combination of RPA solution with conventional business solutions has digitalized the business procedures and thus laying down the foundation for continuous process enhancement.

Working as a business leader, one must ask them a question-“What is the business value of enterprise RPA?" If you are one of them and want to find out the answer, then go through the given blog. It will help you to find out numerous benefits that have made the given automation tool the most valuable software for all the enterprises.

Benefits of RPA from Business Perspective

For every industry, it is an important piece of information in getting aware of the application of the RPA, and how it offers value to their business with respect to their operating model. The core benefit which comes with the given solution is automation, which frees up the valuable time of the organizations that require high human judgment.

Here, there are a few significant benefits that make robotic process automation software the most successful one. Let’s venture deep into them.

I.RPA Focus on Enhancing Productivity

As RPA works on a specific set of software called bots, it becomes easy for the whole system to have full focus on some specific tasks. One notable example is the creation of a monthly report, that RPA bots create in 20 minutes as compared to the workers, who endured four hours to complete it.

Besides, they have the capability to click, calculate, and navigate through the screen within seconds, and thus save a lot of time from the business point of view. Likewise, it also enhances the productivity of the employees that will make them more effective in their work. By it, employees can work for with great endurance and will also produce better result for the firm.

II.RPA Tools comes with Great Cost-Effectiveness

Along with workflow tools, the RPA software system has proven to be the biggest game-changer in the world of digital business. Both tools are incomplete without each other and instead of competing with each other; they tend to work in tandem.

The RPA bots wait for the signal to arrive from the workflow tool to do their job effectively. After receiving the message, the RPA software program accomplishes the given task. And after completing it, the message is processed back to the workflow tool.

In short, both the tools simultaneously work together to negate each other flaws, which are essential for accomplishing the whole circuit without wasting further time buying expensive software. Thus, RPA service solutions come with great cost-effective results much to the delight of the companies.

III.Obtaining High Efficiency is the Identity of RPA

The most interesting part of the RPA is that it works continuously for 24 hours a day without taking any break. Yes, it’s true! There is no need of going on vacation or falling sick as it is in the case of the employees. It is the best part of the given automation system.

In general terms, the working of a single RPA bot is equivalent to the two to five permanent workers, and it could extend more. Furthermore, they have proficiency in completing the same volume of work either at the same time or even less. It shows that they bring high efficiency in the working operation of any business enterprise.

IV.Upgraded Analytics for Effective Workflow Management

For proper monitoring of the human staff, there is the presence of a surplus tracking system, with a notable example of workflow analytics. However, it is nearly impossible to track every working person in an office environment. But with the arrival of the automated robotic process such as RPA, the monitoring process has become somewhat smooth.

It helps in the establishment of timelines and a clear network of activities that assists the management to track even the minute time increment and flaw in the working precisely. Also, using clear and precise metrics, RPA software tools can track all the activities and accomplishments successfully.

Even if the tremendous growth of RPA will make the analytics more complex, still the system is capable of tracking the performance of the employees with full power without even taking a break.

V.RPA Ensures Great Data Security at Each Level

One of the notable benefits of RPA is that it offers a secure version at each level of data processing. Bots perform a single task at one time that prevents the leakage of essential information from one part to another. In this way, accession of the data is documented in a controlled manner.

What’s more, it will generate more jobs for those experts who can handle the system well by applying various encrypting and firewall methods to make the system full-proof and tightly safe. So, be prepared to get better safety procedures with the given automation system.

VI.RPA Arrives with Advanced Governance

There are two levels where RPA governance offers advanced benefits at two levels which have been explained below:

A clear and accurate governance system awaits the working of an RPA system, and thus acts as a key step in creating an environment for effective deployment of the given RPA platform
It governs a much larger system that helps it to check, as well as, continuously recheck the existing system. In this way, the given automation system can check and flag activities that do not comply with the current working framework

VII.Design of RPA Solution Demonstrate Easy Scalability

RPA solutions are well-known to offer easy scalability for any given task. With the vast army of automated bots, they can perform those activities that even a team of expert professionals cannot conduct.

Furthermore, a group of automation processes can be programmed to duplicate similar jobs done manually in a different set of procedures efficiently. The given step undergoes continuous repetition that produces a platoon of active systems simultaneously.

Moreover, RPA bots have the proficiency to incorporate machine learning and artificial intelligence into their automation that again creates a path towards exponential scalability when compared to human resources.

At the End

The given blog reveals the benefits of adopting RPA solutions among various business enterprises. It helps them to have a clear insight into their business goals. Furthermore, it has become a user-friendly tool because of automation that meets all the business objectives successfully.

Likewise, if any organization is using outdated applications, the use of RPA will solve the given issue appropriately. The given time is bad for any business due to the COVID-19 pandemic as it has disrupted the smooth functioning of any business. So, opting for a perfect automation solution in the form of RPA is the way towards creating a healthy working environment. That’s it!

How is AI Complementing iOS App Development?

Jason White — Thu, 23 Apr 2020 06:23:00 GMT

New technology is in trend every day and striving to make its mark to be the best. It is not optional to stay exceptional, technology has made lives easy and uncluttered in so many ways that cannot be comprehended. With the latest technologies in the headlines every day, it is Artificial intelligence that is leading the race and being incorporated in every aspect of life.

From smart homes to smart cars, everything is impacted by artificial intelligence in a meticulous way. The world is really holding on to this technology and its ability to function like human beings is what makes it different.

Artificial intelligence is here to stay in the long run, out of every sector which is embracing this technology, the iOS industry is among the recognized ones. Collaborating AI in your smartphones is a big bonus as it adapts to your needs effortlessly and feeds data for future perspectives which you can easily have access to within a few seconds.

How can AI Make A Difference in iOS App

A primary focus of AI is to function and work like humans, and incorporation of this technology into mobile is helping individuals to solve problems, plan, learn, perform and whatnot at it’s best.

As said earlier, AI is all for trial and adaptation, with people bringing new inputs from their experiences and using their brain which is always in search to find answers to questions, AI apps have the ability to learn and perform according to your needs.

AI Is All About Learning

The best part about artificial intelligence is its ability to learn from user experience and save the data for future conditions. Based on your use, AI develops a solution to your every problem by building on your past experiences and searches. The strong ability of AI to memorize everything and stack it so easily is what makes it different from other technologies. If you are thinking of developing iOS mobile applications then amalgamating it with AI can give you a big shot.

A problem solver

To lift the weight of your shoulder, you rely on mobile applications, as you are just a click away to explore a variety of features tucked behind one icon. Mobile apps make life very simple and comfortable, of course. iOS App development companies, by integrating AI in the system can easily develop solutions that can look deep into the problem a customer faces while using apps.

AI has helped to predetermine the problems and found solutions to them easily. AI is all about understanding, once it is introduced to your problem, the solutions are automatically developed at a fast pace.

Oriented to observations

AI is very powerful when it comes to observing user behavior, this intelligence tries to study every action that you are making and develops according to your taste.

Personalized suggestions by AI can cut users’ time by giving them appropriate solutions just right on time. One of the basic examples of this is the ability to find a particular musical tune just by writing or saying a few of its lyrics to Siri, an intelligent assistant for your iOS device, which can take a potential amount of time if you opt for a manual search.

Contextual

People are very busy nowadays and don’t have time to search for multiple things and the fact that can bother everyone is the appearance of something irrelevant while you are looking for something that serves your need exactly at the same point of time.

Talking about time cutting and serving the best solutions at the earliest, AI has been able to serve its users, contents that are relevant according to the users’ expectations and doesn’t give vague information. This AI feature in iOS can enable users to find crisp and concise content that fits right to your needs and gets the job done without any problem.

Concluding Note

Out of all the technologies, the one that leads the pack is AI, new-age customers want their demands to be catered. While they search for the latest technologies that can make their lives better, iOS app development companies came up with Artificial intelligence that can understand, learn and process information just the way customers want it.

With advancements as brilliant as AI, iOS app development companies are taking in trained individuals who have already mastered this technology to build apps that are able to develop apps with AI, the core objective of AI is to understand the environment that they are presently in and learn from it to develop more around that knowledge.

Why UX and UI are important parts in the scaling of a website

Green Apex — Thu, 09 Apr 2020 07:26:00 GMT

Researches show that rejection or selection of a website is 94% design related. This points that UI/UX is the crucial factor determining the success or failure of a website. As online business transactions are gaining popularity now-a-days it seems to be critical to offer a good UI/UX for the website. Since different options are available for the users, they would look for those sites having good appearance and are easy to use.

UI focuses on visual aspects of your website. UI determines how information is presented and how visitors interact with it.

UX focuses on the overall experience of your website and determines how visitors get from point A to B while navigating through your website.

It is quite evident why User Experience Design is very important for a website. However, we give you 5 unmissable reasons to strengthen the fact.

Feedback Time and Response

Feedback Time & Response are the two best features of any social networking and social media sites. Feedback is a pointer to interaction design. It enables the user to communicate with the product whether the desired task is completed and what to do next.

The response time in feedback is also a key factor. It must be a real time. It should be within 1 and 10 seconds.

Simple interface — Usability

It is the prime feature in social networking and social media sites. The apps or websites should be simple in colors and graphic design. It should have minimum number of clicks.

For example Whatsapp is most popular social networking application. It has simple layouts, visual and User-centered Design.

Product Clarity — Better Navigation

Clarity is one of the important characteristic of any digital product. The sole purpose of your product is to facilitate users to interact with your system. To achieve this it must clearly communicate with the user. If the user cannot achieve this clarity he might feel distracted and may abandon your product. To improve clarity labels must be given for buttons and actions.

User Assistance

Another feature that makes your product easy to use is User assistance & Help. It provides information when something happens unexpected suddenly or when people get stuck. Help will guide the user through the necessary steps towards a solution to the issue he is facing.

Forgiving Interface — Enhanced Quality

Nobody is perfect and users are bound to make mistakes when using your digital product. How well you can handle such mistakes is an indicator of your product’s quality.

Forgiving interface is the one that can save your users from costly mistakes. Undo is the most commonly used forgiving interface.

Conclusion: The Lasting Impact

For success in any online business, an user friendly website is a must as it will provide enhanced user experience to the online visitors. Any site that is too complex and difficult will definitely push away online traffic. Providing best user experience means you are delivering best services that help to boost your sales. User-centered Design Techniques are the most important aspect that impact the future of your business.

Good UI/UX you can win the confidence of your customers
Innovative features and attractive designs create and enhance your brand value
The aim of UI/UX is to present an optimized design and experience to the users for their satisfaction
Your business can get a boost for future growth if you get your UI and UX aspects right

RPA and Chatbots: Two Intersecting Parts of AI

Jason White — Mon, 20 Jan 2020 11:30:00 GMT

With time, we have evolved from oiled lamps to ever-present electricity, printing every page manually to now producing thousands of copies a day and so on.

But what is the one thing these distinct things have in common? Yes, technology.

One such branch of technology that has crossed leaps and bounds and turned into a working technology from a mere fiction is Artificial Intelligence.

No amount of words would suffice the capabilities of AI that are evident in every other industry. In fact, AI has constantly been leveraged in startup ideas, adding to its growth. Due to this rising popularity, AI has also given birth to numerous other technologies.

In this article, we will be discussing two of them all, which are being mistaken for each other, creating confusion among many - Chatbot and RPA.

Chatbot

AI-based chatbots are software applications developed using Natural Language Processing (NLP). They are designed to analyze and replicate human conversations on the basis of understanding what humans want. They are programmed in a way as to respond to any query entered by the end-user and provide them effective customer support, eliminating the need for the human element.

Here are some of the characteristics of chatbots that are a solid reason for businesses to opt for it -

Chatbots can be used for any user-centric process initiated by voice, i.e., phone, physical or voice-enabled interfaces, and others like messages, i.e., web, email, text, and more.
It is fundamentally based not based on pure automation, rather on transformation.

Chatbots are capable of and primarily focus on simulating and replicating human conversations and understanding human intent to implement automated actions and tasks.
Chatbots are able to simulate conversations as they are fed tons of data, guided conversations and are governed by free form conversations that are less structured in nature.
A chatbot is a data and conversation-centric technology

Because it is independent of human intervention, it really decreases friction across process tasks.
This technology is highly adaptable, versatile and intelligence-induced with capabilities inherited by data, knowledge, and experience.
Chatbots are useful for both business-led as well as IT-focused.

Robotic Process Automation

Aimed at automating business processes, RPA is an implementation of technology that is governed by business logic and structured inputs like in the areas of finance, operations, production, HR, and distribution departments. With the help of RPA tools, organizations can configure software in order to capture or even interpret applications for manipulating data, processing a transaction, initiating and promoting responses, establish communication with other systems, etc.

To understand RPA more accurately, let’s have a look at the characteristics -

RPA is essentially based on automating already-existing business processes
One does not require programming knowledge to operate RPA. This has an edge over traditional automation methods as employees only need a little bit of training and everything is smooth-sailing from there.
It is applicable to all the repeatable, routine, rule-based, or predictable business processes
It is aimed at eliminating the manual processes.

As opposed to traditional automation methods that mostly include complex processes for transformation, RPA’s transformation processes are pretty straightforward.
It is directed by structured data inputs instead of human inputs or chat.
It is process-centric as opposed to chatbots.
It decreases friction across process tasks by limiting human handoff
It is not flexible in nature to adapt fast to changes or exception-handling.
RPA implementations are IT-led with input from the business

Given all these terrific features, more and more CIOs and organizations are adopting RPA in order to reduce menial and tedious tasks and promote corporate workers towards higher-value work. However, one thing to note here is that RPA needs to be properly designed, planned, and governed in order to derive optimum functionality and benefits.

A question has surfaced regarding whether RPA will be replaced by Chatbots or not. Well, whether something like this will happen or not is doubtful from the point we see it, as there are always use cases that validate and support RPA more than chatbots. However, there are solid arguments that hint towards chatbots becoming more popular in the future.

But, what if they work together? Well, this is something we are still waiting to see. Without a doubt, these two AI-powered technologies together can benefit all the industries in unimaginable ways.

Is ETL Still Relevant?

Limor Wainstein — Wed, 29 Nov 2017 17:23:00 GMT

The title of this article poses a pertinent question for modern enterprises that increasingly make use of powerful high-end analytic data engines, such as Hadoop clusters and cloud-based data warehouses (see this article by Forbes, which deals with similar questions).

The challenge for enterprises that use analytic engines is one of data movement—what is the best way to get data from operational systems into data warehouses and other data platforms so that it can be queried for reporting and analytics purposes?

ETL (Extract, Transform, Load) is one such method for moving data, and it is one of the oldest data movement methods. However, debate continues as to how relevant ETL still is.

Below you'll find out exactly what ETL entails, some use cases for ETL tools, and whether ETL tools are still relevant in a world where enterprises can leverage powerful modern data platforms to transform data inside those systems to be ready for use with their business-critical applications, instead of requiring a separate transformation engine.

What is ETL?

ETL (Extract, Transform, Load) is a process of moving data from source systems, transforming data in a transformation engine for use with target applications by applying a set of rules to the data (standardizing, aggregations), and finally, loading the transformed data into the target systems, which are usually data warehouses or other data repositories. The end goal of using ETL is to enable businesses to make data-driven business decisions.

Some ETL use cases and examples are:

Pulling data from different transactional systems into ODS (operational data stores) for operational reporting and decision making.
Retrieving data from transactional databases, transforming it for data warehouse use, and loading the transformed data into the data warehouse.
Extracting data from XML files, structuring it, and loading to a data mart to serve a particular community of knowledge workers within organizations.

What Are ETL Tools Used For?

ETL tools are used to manage the flow of data between source systems and target systems with ETL processes. Organizations can create their own ETL tools using scripts—this approach is suitable for a small number of data sources with similar types of data.

For most use cases, ETL tools created by other companies offer more functionality by implementing data transformations easily and consistently across various data sources, such as XML files and relational databases. ETL tools use transformation engines to sort, join, merge, reformat, and aggregate data.

Enterprises and organizations can avail of either commercially available ETL tools or open-source tools.

Useful ETL Tools

There are several different ETL tools on the market—here are some examples of the most useful ETL tools currently available:

Apatar—this open source ETL tool comes with a practical user interface that can reduce R&D costs. Apatar is written in Java, and organizations can use the tool to easily populate data warehouses and data marts.
Scriptella is another open source ETL tool written in Java that can use SQL or any other scripting language to perform data transformations. You can work with multiple data sources ETL file in Scriptella, and the tool integrates with any integrates with any JDBC/ODBC-compliant driver.
Stitch is a commercial self-servicing data pipeline that loads data into data warehouses using ETL processes. Stitch requires no API maintenance or scripting, and it can handle both bulk and incremental data updates.
Fivetran is a commercially available fully managed data pipeline solution that integrates data from all your cloud services and databases into a single data warehouse using ETL processes.
Camel is Apache's open source data integration framework built using Java. Camel has ETL functionality through its available libraries, which can be used to build programs that perform ETL operations.

Wait a Minute. Is ETL Still Relevant?

Enterprises are increasingly leveraging cloud-based solutions for their data needs, in particular, for BI and analytics. This 2017 report found that cloud BI adoption increased in respondent companies from 29% to 43% from 2013 to 2016, and that 78 percent of organizations plan to increase their cloud use for BI and data management through 2017.

With powerful modern solutions such as cloud-based data warehouses, it's becoming more common and more practical to take an ELT approach to data movement. ELT (Extract, Load, Transform) is an alternative way of moving data from source systems to centralized data repositories without transforming the data before it's loaded into the target systems.

With ELT, all extracted raw data resides in the data warehouse, where powerful architectures can transform the data as needed to serve business needs. In other words, the transformation is performed when the analytic queries are run.

The major upside to ELT is that there is no waiting—all data is accessible at all times. This is in contrast to the traditional ETL approach to data movement, in which analysts and BI users must wait for the full ETL process to complete before accessing data.

ETL, therefore, is outdated for most use cases. Cloud infrastructure makes data more accessible than ever before, without the need to maintain complex scripts that transform the data for analytic use, as in ETL.

However, ETL still has a place in legacy data warehouses used by companies or organizations that don't plan to transition to the cloud. With the adoption of cloud-based data solutions skyrocketing, it won't be long before ETL completely loses its relevance.

Closing Thoughts

All companies or organizations that want to analyse their data to make business decisions are faced with a common challenge—how to move data from source systems, such as transactional databases, to the target systems used for data analysis and BI.
ETL provides one way of moving data by pulling it from source systems, shaping it to be ready for use with analytic applications, and finally loading the data to the target systems.
There are several open source and commercial ETL tools available that can make the ETL process more functional and practical.
However, the transition to cloud-based data solutions makes ETL processes less relevant, because there is no need to transform data before it moves to the warehouse or other analytic repository—cloud infrastructures have the resources to efficiently transform raw data as it is needed.
ETL is outdated, but it's still useful for legacy data warehouses used by companies that don't plan to move to the cloud or don't foresee much future data growth.

The Need for a Recipe

Dahlia Biazid — Thu, 13 Sep 2012 07:50:00 GMT

Everyone knows what use cases are and their use in the software requirements process. We all know there are many ways to write them: logical, physical, to describe a manual procedure or an automated procedure. The purpose of State Diagrams is well-known to everyone in the field. They help us define the transitional lifecycle of an object moving and manipulated on within a system. Data dictionaries are important to describe the elements of each data entity or class. And so on.

It is like a toolbox to choose from. I could use the hammer to bang on a nail (its original use), or to open something (an innovative use). In my requirements projects (or rather sub-projects form software development for that matter), I can use any or all of these techniques to analyze the system at hand. The question is when? What is the best strategy to employ these tools and get results most efficiently and effectively?

Nevertheless, this question did not seem answerable; even worse, it was not even asked! Instead, I looked around for a sample deliverable or a template to follow, with the hope that they would give me a picture to visualize and help me put the pieces together; and hopefully relieve me from the need to have a strategy. The tactic worked to an extent, but not completely. I still did not know for sure which step to make at each time I went to see the client or sit to think about the requirements plan. Only until the project was complete was I able to see the picture. So naturally, I took longer than I should to complete requirements; and the results were not as satisfactory as I as well as everybody involved wished for.

What was missing was the method, the strategy, the cunning of a chef who knows which ingredient to add and the exact timing and portion. What was really missing - and seems to be still missing - is the "recipe".

Upside down Procedures and Processes

Mike Cunningham — Wed, 27 Jul 2011 12:00:00 GMT

Are we looking at it all wrong?

Do you ever get the feeling that you are seeing something very different than the other guy? I don’t just mean the glass half full/empty outlook on life, but fundamentally are we looking at things the wrong way?

I sometimes get that feeling when dealing with projects that involve processes, and all my projects are about processes! Take the following scenario, the stakeholder or client has a problem; they know they need to improve some processes in the organization, but are not sure exactly where to begin.

As the designated Agent of Change we provide them with guidance to determine goals and objectives to scope out the next steps. For example:

· Need to document existing processes accurately

· Train existing staff how to follow these processes

· Be able to standardize the process for quality and efficiency

Before I continue, let’s go back in time …

Back to the Future

In 1770, James Watt pioneered the use of drawn-to-scale engineering plans for the manufacture and installation of industrial products1[. These were used to assist in the successful deployment of his products; steam engines for factories driving the industrial revolution. Over time these plans evolved from crude drawings to assist an experienced engineer to more detailed plans that were created by a junior draftsman. This was the forerunner of today’s Computer Aided Design ]2[ world. Detailed, precise instructions permitted products to be manufactured without the need for hugely skilled labor, and a bridge was created between design and production engineering.

In many ways the Subject Matter Expert/Project Manager takes the same role today as those early engineers. Understanding in detail what needs to be done, and translating these complex processes by way of underlying skills, experience and well established methodologies.

The time may be right to go back and look at a different way of doing things; one that may change industry again.

Looking at the pie instead of the ingredients?

When an expert looks at a problem they often want to show off their skills. Look at me I am smart, indispensible, letters after my name, better than you … got the idea. Now all project managers are not this way, but there is a tendency to want to show off our skills versus others in the project.

One way this happens is we often/always start looking at process FIRST. Because we are good at seeing the big picture, grasping the complex, overanalyzing the simple; so we habitually start with the complete process.

As a result we are often way ahead of where the client/stakeholder is in the project. Being ahead a few steps is probably OK; being so far ahead they can’t see your dust creates distance, misunderstanding and communication problems. They may not feel that you are on the same wavelength, and we all know where that leads. We don’t want to go there.

Start with the tasks, not the process.

Recently, I have been examining this problem with some precision. I know Business Process Management has the word process in the middle, and is therefore important. However, every process is full of tasks, activities and decisions. The client/stakeholder may not yet see how all these tasks are intertwined to make up their own work processes, but they understand the basis of all processes are tasks. So why don’t we start by capturing the tasks first? Good idea!

A task centric approach has several advantages over process first. These include:

1. It’s easier to get the client/stakeholder to capture precise information about roles, ownership, guidelines, resources, timeframes, frequency etc. on a task basis

2. Once you have all the important tasks documented it’s easier to then order them for the “as is” or “current state” of operations

3. Get everyone on the same page for the process documentation project

4. Builds a base of foundation data for the project

Once this task library has been created, we can return to the process view. However now we have the details we need to build on our “Discovery” phase of the project. If you have used Excel or a similar product to capture this information, it will be easily sorted and can be then categorized for the process capture stage of the project.

Conclusions

Just as we learn the alphabet, words, sentences, grammar before creating great prose; we need to apply the same principles to our Process Management activities. Taking logical steps to breakdown the “Discovery” process into tasks and then build the jigsaw puzzle that is the process makes sense.

Given the potential for re-use of tasks in best practice models, this approach may have much merit in Process Improvement methods.

]1[ NMSI. The Production Process. May 20, 2011. http://www.ingenious.org.uk/Read/Seeing/Drawings/Theproductionprocess/.

]2[ Wikipedia. Computer-aided Design. May 20, 2011. http://en.wikipedia.org/wiki/Computer-aided_design.

]

IT Black Ops

Seilevel — Sun, 22 Aug 2010 23:26:00 GMT

I’ve worked on at least one project now and heard of several others where a super-secret development team works in parallel to solve the same business problem as the “official” IT project team. A coworker of mine coined the term “IT Black Ops” to refer to these sorts of projects where the business, either out of frustration, arrogance, or ignorance, hires their own shadow development team to implement a competing solution, or an enhancement to an existing solution. I’ve never seen this go well. However, unless you are at an executive level, there is very little you can do to shut down the black ops team. In many cases, you won’t even realize the black ops team exists until the last minute when you are forced to integrate their spaghetti code solution with what the actual IT project team has built. Of course, this kind of surprise is to be expected, since the very nature of IT Black Ops is to operate stealthily, and for their business owners to neither confirm nor deny their very existence. However, once their presence is detected, a project and budgetary train wreck usually ensues.

I’ve done a little bit of thinking recently about why IT Black Ops projects are launched in the first place. It’s probably because of one or more of the following reasons:

1.Low confidence that IT will be able to build something that actually solves a business problem. Sometimes this low confidence is warranted.

2.No budget to build something the “right way” (i.e., gather requirements, manage the project, test it, and deploy it).

3.Business owner finds an extra million dollars in the budget and can finally implement his/her pet feature, even though it was initially shot down because there was no ROI for it.

Lack of understanding about why it takes so long to develop working software.
Even though the decision for the business to go undercover with their IT development will likely be disguised, there are a few ways you can help prevent them from wanting to do this in the first place:

1.Keep the business engaged throughout the development lifecycle. Giving the business partial ownership over the project by having them sign off on and review working prototypes is a great way to give them confidence in the system and make them feel as though the solution is a joint effort.

2.Sell them on the value of process. The ROI for good processes is difficult to calculate; however, turning a team of developers loose to write code with no requirements or process discipline is about as successful as hiring a room full of 1000 monkeys to develop your solution. Giving the business ownership of the process will help.

3.Each project should have a clear ROI. This sounds obvious, but too many projects have a vision statement akin to “you know it would be really cool if…” People also become emotionally invested in certain solutions, without taking the step back and evaluating how well the solution solves a business problem. Just watch out for pencil-whipped and contrived ROIs. Be suspicious of any feature which does not tie back to a quantifiable business objective.

4.Use comparable projects to set expectations. People who do not work in software development often find it difficult to understand how expensive and time-consuming software development can be. This can sometimes lead to the attitude of “This is simple–I can do it faster and cheaper myself”. In order to head this off, it helps to show the budget, resources, and time to execute for similar projects. It is natural to ask the question “Why is it so expensive/time-consuming/so resource-intensive?” You can use the lessons learned on other projects to help answer this question.
It never really helps to mention that the black ops team will always fail, that someone will get fired if the black ops project continues, or that it will end up being more expensive in the long run–even though these statements are almost always true. Once the black ops team is hired, you’ve already lost, so do what you can to prevent IT Black Ops projects from being launched in the first place

This blog post will self-destruct in 500 views.

By Jhulgan

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The Mendix Community Fully Embraces Open Source

Mendix.com — Fri, 13 Aug 2010 17:03:00 GMT

The idea of open source information gathering has been utilized in the scientific community for centuries. It is no secret that sharing information created by communities of experts provides the most productive basis for advancement, regardless of subject matter or field of study. Business engineers developing on the Mendix platform utilize our own MX forum and AppStore to discuss, learn, and share their thoughts for the betterment of their applications. Our Research and Development team use the forum to make the Business Agility Suite better for our users. It is, at its very heart, a cyclical philosophy that mutually benefits those who learn and those who create.

Again, the advocates for open source are vast and ever growing. The following anecdotes aim to portray our wholehearted belief in using open source in the advancement of our technology.

1. Sharing is Caring

The Mendix AppStore is a fantastic example of our own community embracing open source. Most of the content in our AppStore is open source. Our partners have been busy creating interesting software modules that can save other developers time and money. In return, they will have access to other developers applications, widgets, and themes. The AppStore is available to our partners and users now, but will be open to the public soon. Developers also share the ability to make the contents of our AppStore better, as discussed in the following…

2. Concealed knowledge is static knowledge

A century ago, intellectual property laws were all the rage. In many industries, they still serve a valid purpose that may very well foster innovation. For instance, pharmaceutical research must remain concealed in order to protect the value creation that finances it. (Then again, Andrew Witty of GlaxoSmithKline, is placing his bets on open source research to find a vaccine for malaria.) In many cases, however, when knowledge is concealed from the public eye, it remains static and unchallenged, and therefore compromised.

The often overlooked side of what makes an open source philosophy valuable is in the review of content. While creation of content by the masses demonstrates the initial benefit of open source, the ability to have innumerable judges critiquing that very content is equally valuable. Shared content is validated by peers, rather than say, the reputation of its creator. In fact, the MX Forum rates its users based on their contributions. This way, reputation is directly related to the reaction ones contributions receive.

3. Disseminating knowledge is just as important as creating it

It turns out that an open source philosophy contains within itself a similar relationship to that of the chicken and the egg. Which is more important: The supply of new knowledge, or the ability to communicate it? The Internet has given us the ability to communicate and create vast amounts of information simultaneously. Open source philosophy, as it has developed online, has become hugely more impactful as the Internet evolved into its current state. With more access to knowledge comes more knowledge, and with more knowledge comes more innovation. Again, a cyclical growth of intellect is fostered by open source.

My motivation for writing this post came about when our forum, the MX Forum, was featured on OSQA – The Open Source Question and Answer System – well, forum. This site is an interesting demonstration of open source philosophy, as it literally centralizes open source forums within its own open source forum. Since the team at Mendix applauds open source philosophy, we were happy to provide our forum to its visitors.

Creating Accurate Time Estimates

Seilevel — Fri, 25 Jun 2010 19:45:00 GMT

By Landerson

I recently joined a new project where I will be working as the person responsible for the developing and creating the requirements and documentation on a major development effort. As the person on the hook for a significant portion of work, I need to provide accurate time estimates for my portions of the project. I was concerned about providing accurate time estimates on a new project in a new environment. I am also very aware that deadlines are important and know that if I am unable to accurately estimate my deliveries, I will quickly lose credibility with the rest of the team. Underestimating my deadlines might also put other team members relying on my work at risk of missing their deadlines.

My concerns had me thinking that perhaps others might be in a similar situation. After a bit of research and analysis of my own process I compiled the following list of questions and suggestions to help when making time estimates.

How accurate do your time estimates need to be?
If an estimate needs to be very accurate, it is usually a good idea to take a longer period of time to consider and analyze the answer. It is not unreasonable to ask someone who is looking for a timeline for some “think time” in order to provide an accurate answer. However, when not immediately responding, it is a good idea to communicate a reasonable target for when you will have the estimates finished, even if it’s only 15 minutes of extra think time.
How well do you fully understand the project/tasks that you are being asked to estimate?
If a problem is complex, or if you do not completely understand all of the tasks you need to finish, it will be difficult to make accurate time estimates. Getting as much clarification as you can is necessary. Discussing the details of what you have been asked to accomplish with the person making the request might also provide them insight into the complexity of the request and your work process.
How long has a task of this type taken to accomplish in the past?
It is a good idea to maintain a personal log of tasks and an ongoing list of recorded time spent performing a task. I simply use an excel spreadsheet to record tasks I have finished on my projects and update it when I have a few moments at the end of the day or week. Having a realistic idea of the amount of time I spend on my tasks helps me to accurately predict future projects/tasks.
Are there any assumptions, conditions or constraints which might affect your time estimate?
It is impossible to predict in advance every detail of a project with certainty. It will be important to note your assumptions and constraints when you provide your time estimates to communicate your issues clearly. These could all be considered risks to the accuracy of your time estimate and should continue to be monitored as you begin the tasks/project.
Do you need to add any wiggle room?
You should consider adding contingency time if there is a lot of uncertainty about the tasks or many risks associated with your estimate. By increasing time to the estimate appropriately because a project is new and unfamiliar as a way to prevent underestimating your efforts.
Are there any other elements to the project/tasks that should be included in your time estimate?
One area I consistently forget when creating estimates is the amount of extra time I have to spend doing administrative tasks like organizing meetings, sending emails, or organizing documents. At times, these types of activities are not always predictable, but understanding how much of your work might be effected by other project duties is important. There is a small amount of extra administrative work in most tasks, and adding that into your work estimate will help your estimating efforts.

When I employ these methods they have lead me to more accurate time predictions that have also greatly reduced my anxiety over creating self imposed deadlines that are unrealistic. As I also have an intrinsic desire to please the person asking for my time, using some standard processes in producing my time estimates has lead me to win/win situations for both my project and myself.

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“So, what do you do for a living?”: A BA/Product Manager’s guide to surviving cocktail parties

Seilevel — Thu, 10 Jun 2010 13:52:00 GMT

By Jhulgan

I used to dread hearing the question, “So what do you do for a living?” This was often usually asked by a family member or other acquaintances not familiar with the software development lifecycle, IT, or business processes in general. For business analysts, product managers, and other software requirements types, it’s difficult to give an answer without being convoluted or sounding like Tom Smykowski:

This great post expresses a similar insight beautifully, but many of us struggle with good, one-sentence responses to what it is we do everyday in simple English. I thought I would plagiarize some ideas that I’ve heard from colleagues over the years and compile them here:

“We help businesses figure out what software they need to build to solve business problems.” This is a personal favorite of mine that I stole from Marc (who posts under mtalbot on this here blog). It’s especially relevant to IT consultants/contractors. Just about everyone knows what software is, and a lot of people understand that just about every business runs on some sort of software to solve business problems.
“We help communicate the business needs of a system in a language software developers understand.” A variation of this is, “We translate from business to geek”, although I am less fond of the second version because it veers into Smykowski land.
“We make sure the right software is being developed at the right time, for the right people.” The emphasis here is on the right software. This response is nice because it allows you to segue adeptly into a discussion about business objectives.
“We help the business get the most value out of their development dollar.” This response stresses the fact that you are not just a scribe or secretary writing things down, formatting them, and delivering them to the development staff. You are helping make the tough decisions on which features to cut and which bugs to fix, and quantifying those decisions in terms of dollars and cents.
“We work with the business to find the appropriate scope of a software development project and ensure that what is built is what the business expects.” Whenever I am asked the dreaded WDYDFAL question, this is what almost immediately comes to mind. Sometimes I fill in the details of working with pictures to help model current business processes (and desired business processes).

So how about all of our readers? What answers have you come up with? Do you dodge this question altogether, or do you sound like a fumbling Smykowski? Hopefully we can all help each other find good answers to this question. After all, you never know when you’ll be in a meeting with “The Bobs” and the pressure will be on!

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