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.

Ways to Use AI With Your Software Development Team for Maximum Efficiency

Patricia Smith — Thu, 26 Oct 2023 10:04:00 GMT

The software development industry requires productivity and performance to create a successful product and meet business objectives. That's why everyone in the software industry always looks for new ways to streamline their processes and create better software. Today's world is experiencing an increasing pervasiveness of artificial intelligence, making it crucial to utilize it to propel effective software solutions and reduce product development cycles. Therefore, understanding how to boost developer productivity by incorporating AI into software development is crucial.

AI is undergoing radical change in the software development industry. AI's ability to mimic human intelligence and overturn some of humanity's limitations has made it a useful tool for simplifying complex procedures, automating routine tasks, and increasing developer output.

This article demonstrates how a software development team can leverage AI to optimize operational efficiency, maintain competitiveness in the industry, and provide superb software solutions of exceptional quality.

Let's start by examining the tools that most AI programmers rely on.

What Are The 6 Best AI Development Software Platforms?

Businesses and programmers already use the most popular AI tools that incorporate machine learning and other forms of artificial intelligence. Programmers use these tools to produce better software and applications in less time.

Google's Cloud AI

Google Cloud AI Platform is a collection of cloud-based resources that allows users to create, train, and deploy AI models. The service's scalable and adaptable infrastructure supports data preparation, model training, hyperparameter tuning, and model deployment.

Microsoft Azure AI

Microsoft Azure AI, an enterprise-ready cloud computing platform, incorporates machine learning, deep learning, cognitive search, speech services, data bricks, custom vision, bots services, and numerous other tools for designing, implementing, and managing AI-driven solutions at scale.

IBM

IBM designed and developed IBM Watson as an advanced system to perform AI tasks. It integrates several technologies and capabilities, such as natural language processing, machine learning, data analytics, and cognitive computing, to deliver cutting-edge data analysis and cognitive computing services.

Developers use IBM Watson to incorporate cutting-edge artificial intelligence capabilities into their applications, enabling them to comprehend better, examine, and draw conclusions from unstructured data like text, images, and audio.

Infosys XtractEdge

Suppose you must extract information from documents such as legal agreements, commercial insurance policies, standard operating procedures, photographs, handwritten notes, PDFs, or emails. In that case, XtractEdge is the Document AI tool for you. This instrument can generate quantifiable value from companies' unstructured data.

Additionally, XtractEdge enhances human potential, creates connected customer journeys, and utilizes the power of value networks to help clients maximize the benefits of a connected enterprise.

H2O.AI

H2O.ai creates open-source platforms for machine learning and AI as a software company. The company's flagship product, H2O, is an open-source, distributed, and scalable machine learning platform. H2O's user-friendly interface and machine learning algorithms suite offer many uses, including data analysis, modelling, and predictive analytics.

AWS AI/ML Services

AWS offers various AI and ML services, collectively known as AWS AI/ML services. Businesses can use these services to analyze data, glean insights, and develop smart applications with the help of AI and ML.

AWS allows you to avoid making costly hardware and software investments upfront by paying for AI/ML services on a pay-as-you-go basis. AWS can benefit users of varying AI/ML skill levels because it offers intuitive interfaces and a library of pre-built models. It can be easily expanded, not to mention.

Top 8 ways to use AI to improve your software development team’s efficiency

Artificial intelligence tools boost programmers' efficiency the best. Discover our recommended developer productivity tools and integrate artificial intelligence in software development projects.

1. Prototyping

During the prototyping phase of a software project, it is especially important to have time management skills. During this pivotal stage, we develop software prototypes and test and validate their features.

AI-driven automated code generation can save developers time on prototyping, enabling quick iterations and improvements. Developers can rapidly test new design ideas, spot problems before they become major, and improve teamwork using AI.

Using AI in prototyping shortens the time it takes to create working software, encourages creativity, and ultimately delivers reliable, user-focused products.

2. Debugging

AI-assisted bug detection automatically detects software bugs by analyzing code patterns, data flow, and error logs with powerful algorithms. Complex algorithms can quickly and accurately spot outliers and deviations that may point to bugs in your code, data flow, and error logs.

Developers can find and fix issues before the issues have a significant effect on the project's schedule by being proactive. AI algorithms speed up bug identification and boost overall software quality by quickly scanning large codebases and learning from past bug data, preventing problems from ever reaching end users.

3. Code explanation

Artificial intelligence powers code explanation systems that use NLP and machine learning to analyze code and generate explanations humans can understand. Developers find these notes helpful for understanding the logic and reasoning behind code snippets, particularly when dealing with unfamiliar or complex code.

Many advantages exist for AI-driven code explanation.

We streamline complex codebases to accelerate the onboarding of new developers.
Enhancing teamwork and information sharing in software development is achieved by standardizing code discussions.
The AI learns about the development team's preferences and needs through its adaptive learning capacity and ability to process developer comments. This enables it to provide a customized explanation of the code.

4. Refactoring code

Code refactoring aids in pinpointing the analysis and improvement opportunities in a software development project. AI-powered code refactoring systems use machine learning algorithms and pattern recognition techniques to improve code quality and maintainability. These systems provide more precise recommendations for refactoring strategies such as code restructuring, variable renaming, duplicate code removal, and design pattern application.

5. Predictive Analysis

Artificial intelligence relies on predictive analytics tools to analyze past project information, train machine learning algorithms, and employ statistical models to predict what will happen next. These algorithms precisely estimate project durations, resource needs, and potential risks by analyzing patterns, trends, and dependencies.

6. Automated testing

AI-automated testing methods can help you quickly analyze code, find security holes, and simulate test cases. Executing test cases automatically speeds up and improves testing efficiency by artificial intelligence algorithms.

Importance of Data Visualization Tool For Businesses

James Millere — Wed, 07 Jun 2023 05:47:00 GMT

In today's data-driven world, businesses collect and generate vast amounts of data on a daily basis. This data holds valuable insights that can help organizations make informed decisions, identify trends, and drive business growth. However, raw data in its purest form can be overwhelming and difficult to comprehend. This is where data visualization tools come into play. They serve as a bridge between raw data and actionable insights, enabling businesses to effectively analyze and communicate information visually.

Data visualization has become an essential tool for businesses in today's data-driven world. It enables organizations to transform raw data into meaningful insights, empowering them to make informed decisions, uncover patterns, and communicate information effectively. Here are several key points highlighting the importance of data visualization tools for businesses:

1. Simplifying Complex Data:

Data visualization tools help businesses simplify complex datasets by presenting information in a visual format. Charts, graphs, and interactive dashboards make it easier to understand and interpret data, allowing users to quickly identify trends, patterns, and correlations. Visual representations enhance comprehension and enable stakeholders to grasp the significance of the data at a glance.

2. Enhancing Decision-Making:

Effective decision-making relies on accurate and timely information. Data visualization tools provide real-time visualizations of key performance indicators (KPIs), allowing businesses to monitor their progress, identify areas of improvement, and make data-driven decisions. By presenting data in a visually appealing and intuitive manner, these tools facilitate better understanding and enable stakeholders to take decisive action.

3. Facilitating Data Exploration:

Data visualization tools enable users to explore and interact with data, encouraging a deeper understanding of business insights. With the ability to drill down into specific details, manipulate visualizations, and ask questions, users can gain valuable insights and discover hidden trends or outliers. This promotes data exploration and empowers businesses to uncover valuable information that may have otherwise remained unnoticed.

4. Enhancing Communication and Storytelling:

Data visualization is a powerful storytelling tool. Visual representations of data make it easier to communicate complex concepts, trends, and findings to both technical and non-technical stakeholders. By creating engaging visual narratives, businesses can effectively convey information, present compelling arguments, and engage their audience. Visualizations enable stakeholders to grasp information quickly and make informed decisions based on the presented insights.

5. Encouraging Collaboration:

Data visualization tools facilitate collaboration by providing a shared platform for data analysis and interpretation. Team members can access and interact with the same visualizations, promoting a collaborative approach to data-driven decision-making. Visual representations foster a common understanding and enable teams from different departments to align their efforts, share insights, and work towards common goals.

6. Enabling Predictive Analysis:

Data visualization tools can support predictive analysis by displaying historical and real-time data trends. By visually representing data patterns and correlations, businesses can make informed predictions about future outcomes. This empowers organizations to anticipate market changes, identify potential risks or opportunities, and adjust their strategies accordingly.

7. Spotting Anomalies and Outliers:

Data visualization tools help businesses identify anomalies and outliers in their datasets. Visual representations make it easier to detect data points that deviate from the norm, indicating potential issues or exceptional occurrences. By spotting these outliers, businesses can investigate further, take corrective actions, and ensure the accuracy and reliability of their data.

8. Improving Data-Driven Presentations:

Presenting data in a visually appealing manner enhances the effectiveness of data-driven presentations. Data visualization tools allow businesses to create visually compelling and interactive presentations that capture the attention of the audience. By incorporating visualizations into presentations, businesses can convey information more effectively, leave a lasting impression, and facilitate better engagement and understanding.

Conclusion

Data visualization tools are crucial for businesses seeking to leverage the power of data to drive informed decision-making, communicate insights effectively, and gain a competitive edge. These tools simplify complex data, enhance decision-making processes, facilitate data exploration, encourage collaboration, enable predictive analysis, identify anomalies, and improve data-driven presentations. By adopting data visualization tools, businesses can unlock the full potential of their data, gain valuable insights, and thrive in today's data-driven business landscape.

6 Differences Between Data Exploration and Data Presentation

Zach Gemignani — Wed, 16 Mar 2022 18:55:00 GMT

There are big differences between data exploration versus data presentation. And you need to be aware of these differences as you're creating data stories and data presentations. Let’s start by defining our terms:

Data exploration means the deep-dive analysis of data in search of new insights.
Data presentation means the delivery of data insights to an audience in a form that makes clear the implications.

Your toolbox for data exploration tools is flush with technology solutions such as Tableau, PowerBI, Looker, and Qlik. "Visual analytics" tools give analysts a super-powered version of Excel for dicing data to facilitate the search for valuable insights. Flexibility and breadth of features is critical; the user needs to handle lots of data sources and doesn’t know in which direction she will go with the analysis.

Data presentation is a different class of problem with distinct use cases, goals, and audience needs. Think about the incredible data stories delivered by the The Upshot, Fivethirtyeight, and Bloomberg. These data journalists often demonstrate data presentation at its finest, complete with guided storytelling, compelling visuals, and thoughtful text descriptions. When compared to these examples, it becomes obvious that the best efforts by a data exploration tool cannot deliver high-quality data presentation.

You need a specialized solution if you really want to communicate data in ways that engage your audience. To understand the differences between data exploration and data presentation tools, let me offer six key ways that the activities are fundamentally different.

1. Audience — Who is the data for?

For data exploration, the primary audience is the data analyst herself. She is the person who is both manipulating the data and seeing the results. She needs to work with tight feedback cycles of defining hypotheses, analyzing data, and visualizing results.

For data presentation, the audience is a separate group of end-users, not the author of the analysis. These end-users are often non-analytical, they are on the front-lines of business decision-making, and may difficulty connecting the dots between an analysis and the implications for their job.

2. Message — What do you want to say?

Data exploration is about the journey to find a message in your data. The analyst is trying to put together the pieces of a puzzle.

Data presentation is about sharing the solved puzzle with people who can take action on the insights. Authors of data presentations need to guide an audience through the content with a purpose and point of view.

3. Explanation — What does the data mean?

For the analysts using data exploration tools, the meaning of their analysis can be self-evident. A 1% jump in your conversion metric may represent a big change that changes your marketing tactics. The important challenge for the analysts is to answer why is this happening.

Data presentations carry a heavier burden in explaining the results of analysis. When the audience isn’t as familiar with the data, the data presentation author needs to start with more basic descriptions and context. How do we measure the conversion metric? Is a 1% change a big deal or not? What is the business impact of this change?

4. Visualizations — How do I show the data?

The visualizations for data exploration need to be easy to create and may often show multiple dimensions to unearth complex patterns.

For data presentation, it is important that visualizations be simple and intuitive. The audience doesn’t have the patience to decipher the meaning of a chart. I used to love presenting data in treemaps but found that as a visualization it could seldom stand-alone without a two-minute tutorial to teach new users how to read the content.

5. Goal — What should I do about the insights?

The goal of data exploration is often to ask a better question. The process of finding better questions gets to new insights and a better understanding of how your business works.

Data presentations are about guiding decision-makers to make smarter choices. Much of the learning (through data exploration) should be done, leaving the equally difficult task of communicating the insights and the actions that should result.

In all these ways, data exploration and data presentation are different beasts. This is why we’ve chosen to focus on building the best possible data presentation tool, Juicebox.

6. Interactions — How are data insights created and shared?

Data exploration can be a lonely endeavor: Analysts work on their own to gather data, connect data across silos, and dig into the data to find insights. Data exploration is often a solitary activity that only connects with other people when insights are found and need to be shared. That is, when…

Data presentation is a collaborative, social activity. The value emerges when insights found in data are shared with people who understand the context of the business. The dialogue that emerges is the point, not a failure of the analysis.

Finding the Middle Ground: Data Storytelling

There is something between the extreme ends of data exploration and data presentation. We believe data storytelling lies in this intersection. Data stories aren’t entirely about “telling”, nor are they in the wilderness of “finding”. It is the opportunity to explain the data in a guided, narrative way where message meets exploration.

While there are tools for exploration (e.g. Tableau) and tools for presentation (e.g. PowerPoint), it is only recently that you’ve had the change to bring both together in one solution.

Zach Gemignani (zach.gemignani@juiceanalytics.com)

CEO, co-founder, author at Juice Analytics

www.juiceanalytics.com

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.

Product Evaluation Framework for a BA

Ashish Adike — Tue, 20 Oct 2020 22:36:00 GMT

As a Business Analyst, very often we get into a situation where the Project requires multiple IT Products to be evaluated before implementation and might seek Business Analyst’s recommendation for the same. With the ever-growing range of Products in the market and the marketing promotions associated with some of the products, it’s very easy to choose the Product with most features or the most famous one. While there are various benchmarking softwares that measure the IT products from a technical performance perspective, as a Business Analyst, it is our responsibility to make sure that the right product is chosen as we are the ones who know the Business Processes closely and at the same time are equipped to understand the features of a Product that would be well suited to address the Business Requirements. While a Business Analyst may not be the final decision maker, the BA’s analysis definitely impacts the decision-making process. A structured approach to analyze the available Products would help in choosing the best suitable IT product for the Business.

Features: It is recommended to choose a Product that has Features which meet most of the Business Requirements out of the box. Although it’s difficult to find a Product which would satisfy 100% of all the required features, it’s better to choose the one with as little customizations as possible. Customizations would often mean force fitting a Product for a Business Process and may have downstream impact in future. Also, it would involve high maintenance costs to maintain a highly customized Product. As a Business Analyst, we need to provide a Business Process vs Feature mapping for each of the IT Products in question, which would enable the Stake holders to choose the IT Product satisfying the most relevant and critical Business Processes.

User Experience: As a Business Analyst we generally work closely with the Business Users and can gauge the pulse of the Users. It is important that any IT Product chosen provides a good User Experience as it would drive the user adoption and hence the success of the Project. The Business Analyst needs to capitalize the demos provided by the respective IT Products to determine the User Experience levels. Also, many IT Products are available on a free trial version, which can be utilized to get a feel of the Product hands-on before actually implementing the same.

Support: Once an IT Product gets implemented and as the Business Users start adopting it, we see a lot of enhancement requests coming from the Business Users to improve the existing implementation. Also, there might be Bugs in the Product which got missed in the testing process but arise only when the users start using the Product. In order to resolve the issues and implement the enhancement requests, it is critical to have a good post implementation support from the respective IT Product Vendor.

Ease of access: The ease with which Users can access an IT product also plays a vital role in determining the right Product. A Product which can be accessed from any web browser (i.e. Cloud) would always rank better as compared to a desktop based (i.e. on premise). It would even be better if the Product can be accessed from a Mobile device. With the work-from-anywhere concept gaining prominence, the Cloud based, and Mobile enabled Products would see an upward trend in demand.

Product Roadmap: It is important to consider the roadmap the IT Product has planned for implementing various out of box features as that would help us in planning our implementation and make decision on the level of customization that needs to be done during the initial release. The IT Product with promising features suitable to the Business Requirements needs to be given a higher priority as compared to that without a suitable Roadmap.

Although the cost and performance of the IT Product might be considered by the Management, a careful analysis of the IT products with respect to above listed parameters would help the Stake Holders in choosing the right IT Product for long term.

About the Author:

Ashish Adike is a seasoned Business Analyst with more than 8 years of experience in Business Process Analysis, Requirements gathering, Consulting, Solutioning and Project Management. Experienced in working with multiple Business functions – Sales, Marketing, Finance, Operations. Strong research professional with a PGDM focused in Sales and Marketing from Indian Institute of Management, Indore. Awarded team player with excellent learning and adaptability skills.

Understanding Machine Learning Models

Arash — Thu, 01 Aug 2019 18:42:00 GMT

Machine Learning (ML) models are intended to positively impact business efficiency. By understanding how these models are created, how they function, and how they are put into production, one can fully utilize their potential to make a difference in every day scenarios.

What is a Machine Learning Model?

By creating cases within a narrow domain, such as a car insurance company assessing the risk of a particular vehicle being stolen based on known statistics, a machine learning model will use algorithms to determine probability and associate this probability with a particular outcome. While such algorithms are not necessarily limited to particular scenarios, they can be programmed to a higher degree of accuracy for specific types of questions. Below are some use cases that exemplify ideal machine learning models.

What are referred to as regression questions. These would include ‘How much’ and ‘how many’.
Classification questions that include ‘Type of object’ scenarios.
Questions that enable the model to group or cluster in order to resolve a particular scenario.
What are known as ‘abnormality detection questions’ that pinpoint unusual situations.

Engineers and data scientists use tools, frameworks and codes to build models, often from massive amounts of data.

In fact a really effective machine learning model uses enormous amounts of data that ideally has been cleaned and labelled. The process is iterative and involves both trial and error using tests and measures. Multiple steps and processes are used in creating a machine learning model. The finished model enables the computer to use different cases within a particular scenario in order to reach a viable resolution.

Using the answers to specific questions within an array of proven cases, the machine learning model provides users with guidance based on the probability that a particular solution is correct. For example, are particular symptoms indicative of a known medical problem, can this product be fixed, or is this a fraudulent financial transaction?

The Practical Utility of Machine Learning Models

Machine Learning models are intended to achieve the following outcomes:

Use on-the-fly or batch cases to integrate the model systematically
Combine several models to answer complex questions that require multi-step answers
Utilize models to assist with organizational decision making or with external contacts
Integrate workflows and processes that involve several participants
Use certain information system related algorithms with minimal code revision
Provide analytics as a service by sharing the model between multiple use cases.

Monitoring and measuring the machine learning models in a live environment is crucial. In so doing, a cycle of constant improvement is employed. While individual models are not as useful as those that are part of a more sophisticated deployment involving multiple scenarios. In such cases, the solutions suggested should be run against to a decision model that is based on a domain expert’s knowledge and consequently be implemented by using predefined business rules.

As mentioned above, a machine learning model may be designed by an insurance company using statistics that detail the likelihood of a particular car being stolen. The model will categorize a car as low, medium or high risk.

Consequently, calculating an insurance quote for a specific vehicle involves the system calling to a machine learning model which will then identify the likelihood of it being stolen. The result is then sent to the quote generation process to calculate the cost for an insurance policy.

Conclusion

Experience has shown that machine learning models need to be integrated as part of a business decision and process in order to be used effectively. These models must be able to execute requests on-the-fly and their performance within a particular knowledge domain must be monitored, measured, and improved over time.

For more information, please visit http://www.flexrule.com.

Does Agile need Architecture to be successful?

EA Learning — Wed, 24 Jan 2018 03:21:00 GMT

On a recent Agile training course, the instructor opened the session by saying “Agile without a plan is just chaos!” I would like to propose that Agile without effective Architecture will eventually lead to chaos, particularly if organisations try to scale their Agile practices without some form of guiding framework.

The fundamental reason for this is that we all operate within constraints, which can be financial, regulatory, technical or customer driven. While Agile practices have traditionally been confined to software development there is a significant push by organisations, particularly at the Enterprise end of the market, to use Agile practices to manage traditional business functions. This new trend is euphemistically referred to as New Ways of Working. The benefits of leveraging Agile practices are numerous, with the fundamental benefit that organisations see Agile practices as a way to deliver improved outcomes for their customers and stakeholders, more efficiently and consistently.

There are numerous case studies citing the achievement of these benefits at a project level, but very few examples (to date) of successful Agile Transformations at Enterprise Scale. Proponents of Agile practices will point to the Spotify Model as proof that Agile Practices can be used to build a $13 billion Enterprise. Which is true, however, they didn’t do it without Architecture. They did it by leveraging Architecture and its practices as an enabler and not a governing framework. The way that Architecture worked within Spotify is quite different to how Architecture currently operates within Traditional Brick and Mortar Enterprises.

It is very hard to find a clear definition of the role of Architecture in Agile. The SAFe (Scaled Agile Framework) framework has done the most to identify the role of Architecture within an Agile environment. As with all things Agile the focus is to create consistent value and Architecture is no different. In SAFe they define two distinct elements of Architecture:

Emergent Design
Intentional Architecture

Emergent Design provides the technical basis for development and the incremental implementation of initiatives. It helps Designers and Architects to be responsive to changing customer/ stakeholder needs to ensure the initiative continually delivers value. At this level, SAFe practitioner’s see Architecture as a collaborative and interactive exercise through which the design element can emerge.

Intentional Architecture is a much more structured approach and more aligned to what many would identify as being traditional Architecture, that is a set of defined and planned Architectural initiatives which will both support and enhance the performance and usability of the initiative. In effect, Intentional Architecture is a clear recognition that we all need to operate within certain constraints such as choice of technology platform, financial budget, etc. If these constraints can be identified and incorporated into the initiative then the probability of the initiative being successful and delivering value is increased.

SAFe practitioners proport that by balancing Emergent Design and Intentionality Agile practices can be scaled to deliver Enterprise level solutions. In Safe, this combination is referred to the Architectural Runway which provides the technical foundation for creating business value. Which is in complete alignment with traditional views of Architecture.

The key to the success of this approach is the level of abstraction at which the balance of Emergent Design and Intentional Architecture occur. The fundamental behaviour that will determine this is collaboration. Architects need to be able to work productively with Agile Teams to provide fast and local support to manage Emergent Design while also helping Agile Teams to appreciate and navigate the constraints defined by the Intentional Architecture. One of the key attributes of Agile Practices is the fact that Agile Teams are encouraged to provide constant feedback to their stakeholders. As emergent designs develop Architects can use this information to adapt and develop the Intentional Architecture to ensure that the overall Architecture of the Enterprise is evolving with the organisation in the medium to long-term.

So does “Agile need Architecture to be Successful?” I would say the better question is “What type of Architecture does Agile need to be successful?” Agile requires Architecture that supports the way the Agile Practices deliver of outcomes (value). The type of Architecture that will do this will be a combination of a nimble reactive style of Architecture supported by a more traditional structured approach to Architecture. The challenge as with many things is to get the mix right!

Written by Scott Comte, General Manager of EA Learning.

To find out more about the EA Learning Business Architecture or Agile training courses please fill out the below form or click here to view our course range.

What is good enough when it comes to a requirements management tool?

Candase Hokanson — Fri, 17 Jun 2016 15:02:00 GMT

At Seilevel, we updated and re-published our Requirements Management (RM) Tool Study this past year, looking at over 150 tools and over 200 criteria. We did this in phases with the subset of tools making it though each phase getting smaller and smaller until we ran 21 tools through the full evaluation criteria to rank them. This tool study is a great resource if your company is looking to implement an RM tool for the first time.

But what about if your company already has an RM tool in place or has multiple that you need to decide between? The tool study report doesn’t really address how a company can look at their current tool or tools to see how they compare and if they would meet the needs of the company. You could, of course, just take the total scores and ask which one is higher or how your tool’s score ranks, but that doesn’t necessarily mean that the tool you have in place wouldn’t be “good enough” for your company’s purposes.

So, what does it mean for an RM tool to be “good enough.” Well that depends. It depends on what your requirements methodology emphasizes and what is important to ensuring you minimize the risks to your projects through the use of the tool.

Because “good enough” varies from company to company, I took our RM tool study and categorized each of the 200+ criteria into one of ten categories, or features. With these features, I then created a capabilities dashboard that shows a heat map of how each of the top 21 tools fared in the 10 features as a percentage of the total points available in that feature. With this view, you can either find or add your RM tool and see how it compares in specific categories to the other tools we evaluated to determine if the tool you have is “good enough” for your process.

The 10 features are:

Requirements Specification and Prioritization: Can you add, edit, delete and prioritize requirements easily?
Traceability/Dependencies: Can you create relationships between requirements and change the data model to reflect the traceability needed in your organization?
Stakeholder Management, Review and Collaboration: Can you give feedback on requirements or initiate workflows to approve requirements?
Change Control: Can you baseline requirements, track changes after a baseline or revert a requirements set back to a baseline?
Visual Modeling: Can you create and edit models in the tool or link requirements to visual models?
Import/Export and Reporting: Can you import/export to and from Word, Excel, Visio or other sources and can you report on the requirements, models or subset of either group?
Requirements Process Support: Can you set up your own templates and object types to support a methodology with things like checklists, issues, risks or constraints?
Task/Iteration Management: Can you track development tasks on requirements, set release or iteration dates or create agile burndown charts?
Licensing, Support and Tool Administration: How flexible is licensing for the tool, are there adequate support materials and how difficult it is to maintain the tool?
Scalability, Integrations and Ease of Use: How intuitive is the tool, can it scale and what integrations does it offer?

With these ten buckets, the heat map is shown below (click to expand). Typically, you would want to narrow in on a few tools or a few features. For example, if you know you have JIRA in place today, but Traceability and Dependencies are the most important feature to your organization, you would see that JIRA only received 60.4% of the total traceability and dependency points. Or if support of visual modeling and requirements process is most important to your organization, you might narrow down your list to TopTeam Analyst, Modern Requirements Tool Suite and Blueprint.

With this view, you can add one more dimension to your RM tool quest and maybe answer the question of “Is our RM tool good enough?” If the answer is yes, you have the heat map to socialize that message and if the answer is no, you can use this to help build the case for a new RM tool.

Project Management with Mind Maps

Surbhi Mahnot — Thu, 21 Jan 2016 06:35:00 GMT

Prepared by MindMeister tool

Have you ever planned a project or brainstormed an idea? It takes a lot of effort to manage the collected information in a way you can easily traverse through.

On occasions you find yourself occupied with a lot of questions and confusion in scenarios such as

You have a lot of details to get started with but no clear picture of how it all fits together

You are prepared with the plan but find it difficult to explain to team members or co-workers or even to your boss
You are facing difficulty in tracking and mapping all the comments and discussions with the requirements

This is where mind maps can emerge as your savior.

Mind mapping is an effective project management technique to help visualize project features, to plan effectively and to enhance creative problem solving abilities.

Consider it as an empty canvas to draw your ideas in a structured manner centered on one single concept.

A “good” mind map shows the complete structure of the project with all the relevant details and emphasis set properly on all important aspects. It helps you break a consolidated idea in smaller pieces that makes it much easier to plan and manage.

Mind maps are often used to generate, visualize, organize and showcase anything related to personal or business tasks such as financial plan, travel plan, scoping, management processes, sales processes, etc.

Basic guidelines of a mind map

Start with one topic
All the ideas must be connected to the center topic
The ideas can further delve into sub ideas in any level of hierarchy, resembling with a tree that has multiple branches
Use images, symbols and icons to emphasize
Use color to distinguish ideas
Have connections, associations, dependencies in your map wherever required

Good rule(s) of thumb

Do not use lengthy texts to describe an idea. Try to use phrases or short words. This makes map more focused and clear. All the details could go into notes
Use simple language that is easy to understand
Don’t add extra complexities such as excessive images, icons, colors, connections that makes the map difficult to follow for others

Mind maps could easily be drawn on a piece of paper or on a board. Also, there are many tools available to help in creating quality maps quickly like Bubbl.us, Mindmeister, iMindMap, Mindjet and MindGenuis. A complete list of tools is available here.

Practical applications and benefits

Brainstorming

Mind maps come very handy in brainstorming requirements and organizing ideas.

They are an efficient way to break large plan into small manageable chunks.

Helpful Tips

List all your ideas centered on the topic initially, with no established relations or connections. Any feature that could add any value to the central idea, put them on map
Dig deep with each feature individually and list all functionalities that could be covered
Set priorities by adding numbering
Add icons, colors and images to uniquely identify features

All this helps in keeping track of all the requirements and help in preparing a quality scope for the project.

2. Planning

Mind mapping promotes effective planning without getting overwhelmed with the project requirements and complexities.

Helpful Tips

Identify dependencies or associations or connections between all the features of the project
List probable assumptions and risk factors either for every feature or for the overall project
Plan development or execution of project in Milestones/Iterations by identifying the priorities, complexities and efforts. This will help attain a broader picture on overall aspects and set detailed focus
Summarize all the information, revisions, discussions as “Notes” in the mind map against each module to keep a better track of everything

3. Decision Making

Well laid plans always help in taking better decisions.

It is always better to take decisions from a well-developed plan that can be visualized. The same decisions could probably be achieved in traditional manner using excel or word document with detailed description as well, but visuals always tend to make more impact than just plain text!

Decision trees could be easily plotted with mind maps to map all the scenarios, risks, complexities and connections and then decide the best way to go ahead.

4. Problem Solving

Mind mapping is a way to promote creative thinking.

It can bring following benefits in improving problem solving abilities

Mind maps allow quick reviews. As all the details are laid visually, it becomes easy to follow
It can act as mnemonics to help you remember the short phrases and words

5. Communicating details to your stakeholders

Visuals always aid in providing better explanation and bringing clarity in thought process.

With mind maps, it becomes very easy to share the on-going thoughts with all the involved stakeholders

The discussions could be saved to act as an important reference material at later stages
They minimize the chances of lack of clarity or misunderstanding which could save a good deal of time and arguments at later stages

6. Team co-ordination and discussion

Mind maps save a plenty of time in explanation. The details are well laid which makes it easier for individuals to understand and relate with the project better. It can provide

Complete documentation tracked at the same place
Clear representation of hierarchies and associations/dependencies
Recorded details, notes, references throughout the project life-cycle

These were just a few of the many possible applications of mind maps. If you do any kind of research, get stuck with a problem, want to brainstorm on any topic, need to prepare a plan, try Mind Maps. You’ll love using them!

References

Check out these mind map galleries and resources to learn more about mind maps

Learning Fundamentals — mind mapping tricks and trips, particularly for paper
About mind map — basics about mind maps
Mind map strategies — blog about mind maps
Mind-Mapping.Org — one of the most extensive pool for mind map resources, blogs and details
Mappio — contains thousands of mind maps
99 mind mapping resources, tools and tips — get a quick glance of all the resources, tools and tips

The article was originally published on Medium.com by @surbhimahnot

Three Great Tools for Managing Bulleted Lists

Martin@DocProd — Mon, 20 Oct 2014 06:19:00 GMT

This post describes three great tools for managing bulleted lists in Microsoft Word.

They allow you to automatically punctuate them , reorder them , and select them for further processing.

This post complements my earlier article Three Great Tools for Editing Tables that described tools for efficiently entering and numbering the content in Microsoft Word tables.

I use Microsoft Word a great deal in my work as a BA, it is my primary tool for formally communicating text based project deliverables.

I like to make frequent use of bulleted lists. The preceding statement that sets the context for the list removes the need for low value repetition, and allows the reader to focus on the key messages without surrounding clutter.

I often create bulleted lists by recording points as quickly as I can. By concentrating on the ideas, sometimes I don't punctuate the list properly as I go. On other occasions, I need to add entries or move the order of the entries around. It either case, there can be a tedious task of formatting the entries correctly.

Word lacks tools to help me in these situations, so I decided to develop my own using VBA, the development tool that comes with Microsoft Office.

Using these tools makes me much more efficient, and allows me to present consistent formatting with just a few keystrokes. The tools are free to use and modify. They can be downloaded from the link in the signature block at the end of this post.

Automatically Punctuate a Bulleted List

Many of my clients have different standards for punctuating bulleted lists. Some like to present them as fully formed sentences by using a semi-colon at the end of each entry, placing a “; and” at the end of second to last entry, and a “.” at the end of the final line. This is a real pain if you have to add entries or reorder them.

Other clients others like a simple “.” at the end of each entry, and I have encountered a number of other variations that apply in different situations, even for the same client.

To use the tool, place the cursor anywhere inside a bulleted list and select the shortcut key for one of the following supported punctuation styles:

The tool will select the entire list and apply the rule as shown.

You don’t have to select the list, just put the cursor anywhere in the text of a list and the tool does the selection for you.

After the list is selected, it removes all existing punctuation and reapplies the selected style. This means you can change the order of a list and the tool will correct the punctuation with just a key press. In the following example, note how the “, and” has been removed and correctly applied to the later line.

The tool is smart enough to handle bulleted lists inside tables, and bulleted lists in documents with numbered paragraphs.

There is even logic to handle track changes if it is switched on. The will tool temporarily switch off track changes, correct the punctuation, and switch track changes back on. This means that the tool does not create unwanted change alerts.

Microsoft Word has no equivalent tool.

Move Bulleted List Entries Up and Down

Word provides a tool to move paragraphs or bulleted list entries up or down (see Ctrl+Shift +Up/Down). This may not be well known, but works fine if the bulleted list is in standard text.

If you try this key combination inside a table, instead of moving paragraphs, it moves table rows up/down.

I have extended the standard tool so that, as well as its normal functions, you can also move entries in a bulleted list inside a table cell.

All you need to do is place the cursor anywhere in the text of a list entry and press the shortcut key. The entry will then move up/down. Keep hitting the key and the entry keeps moving until it gets to the top or bottom of the list, or the boundary of a table cell.

If you use tables frequently, you will find this is a very handy tool.

I assign the tool to the shortcut keys used by the standard capability so I can use the full capabilities when I need them.

Select a Bulleted List for Further Processing

If the cursor is placed inside the text of a bulleted list entry, the tool will then select the entire list, allowing you to process it further or apply a different style.

This tool uses the basic list select features of the first tool, and can therefore handle lists in table cells, and lists in documents with numbered paragraphs.

Microsoft Word has no equivalent tool.

Summary

I use these tools every day, without them, would find my productivity levels dropping, and my frustration levels rising. A good BA has a bag full of useful tools and I hope you find useful and add them to your toolkit.

About the Author

Martin Coomber is a BA based in Wellington, New Zealand. He is a Certified Business Analyst Professional, tutors IIBA study groups, and mentors other BAs. His Document Productivity website provides document editing tools to the BA community.

Three Great Tools for Editing Tables

Martin@DocProd — Sat, 23 Aug 2014 23:19:00 GMT

I use Microsoft Word a great deal in my work as a BA, it is my primary tool for formally communicating text based project deliverables.

Where I can, I like to use tables, so that I can quickly capture information and present it without having to write full, grammatically correct paragraphs.

Over the last few years, I have prepared a number of tools to improve the way Word handles tables. Using these tools makes me much more efficient, and removes some impediments to maximizing the benefits of table use.

These tools are written in VBA, Office’s macro development tool. Writing quick one-off macros can save a great deal of time in some situations, and something that a good BA should have some exposure to. Using these Tools and checking out how they work is a good way of getting started.

The tools are free to use and modify. They can be downloaded from the link in the signature block at the end of the post.

Number Table Rows

This tool numbers each row in a table. It is great for requirements listings, tasks lists and dozens of other uses.

It includes all the features a BA might look for, including multiple format options using prefixes and separators (e.g. NF.01, NF.02), and the ability break the table into subsections and having the option for these to have their own numbering sequence. It even allows you to carry the numbering from one table to another.

One of the things I like about this routine is that it begins by removing any blank rows. Cleaning up a table like this can be time consuming.

If you need to, you can lock the numbering once it is finalized. This is might be used when a set of requirements has been published.

Word does have some numbering tools, but these are clunky, and I find them frustrating to use. Being able to maintain a list of many entries, move, add, or delete rows, and then renumber the lot with just a quick keypress is are huge win for a busy BA.

Insert Table Rows

This tool inserts five empty rows at the current location and positions the cursor ready for entry in the first. This is great for capturing lots if information quickly in the right place in the table.

Word does allow you to insert like this but there are a few issues; it takes a few keystrokes and, depending on how you insert, can copy incorrect heading or subheading formats into the new rows.

With one keypress, this tool ensures that standard rows are inserted every time, and that the cursor is setup in the right place to start entry. The Number Table Rows tool can then be used to delete any unused rows.

Table Cell Formatting

This tool cleans up table cells by adding punctuation and removing empty paragraphs at the end of the cell. It operates on selected cells or the current cell if no range is selected.

When I capture information quickly in a table, I find I often forget to punctuate correctly and can leave empty paragraphs and the end of a cell.

Checking and correct all entries when I’m ready to publish a table is time consuming and mechanical, circumstances ideal for an automated routine.

The tool processes each paragraph in the selected cells. It skips paragraphs that already have some form of punctuation at the end, and for those that don’t, adds a “.” character at the end. If the paragraph is at the end of the cell, and it is empty, it gets deleted.

The tool skips the first column and first row even if you selected them. This is to preserve the numbering and headings. The tool is also a good introduction to the complexities of handling end of cell markers in Word table programming.

Summary

I use these tools every day, and without them, would find my productivity levels dropping, and my frustration levels rising. A good BA has a bag full of useful tools and these are at the top for me.

My next post will describe Three Great Tools for Bulleted Lists.

About the Author

New Axure Book on Amazon - Reviewers Needed

Anna Roosen — Mon, 24 Jun 2013 09:43:00 GMT

Hi all,

I have recently co-authored a new book on using Axure RP ("A Beginner's Guide to Axure 6.5"), and would like to hear from BAs/UXers/Interaction Designers who would be interested in writing an independent Amazon store review for the book, in exchange for a complimentary kindle copy. Please contact me at annaroosen@gmail.com.

Amazon link: http://www.amazon.com/Beginners-Guide-Axure-6-5-ebook/dp/B00DK8YZ3W/ref=sr_1_9?ie=UTF8&qid=1372070434&sr=8-9&keywords=axure

Blurb: “A Beginner’s Guide to Axure 6.5” is written for anyone involved in software design who needs to learn how to use Axure RP 6.5 in a minimal amount of time. Axure is a leading prototyping tool used to create rapid "drag and drop" designs for web, desktop and mobile applications, without the need for code.

No previous knowledge of the tool is required to use this book, and the two expert-level authors take you right from the basics and cut through to the essential skills you really need to create rich interactive designs. Many examples and practical real-world tips are included to help improve the quality of your designs and make your prototyping more efficient.

Concepts and techniques covered include: Getting started, the Axure interface, file management, the main menu, managing panels, the sitemap, page notes, the canvas, guidelines, widgets, annotations, designing using only images, modifying widgets (widget-by-widget formatting, global formatting and widget libraries), page formatting, masters, how to create consistent designs, simple widget interactions, simple page interactions, dynamic panels, simple and complex interactions, shortcuts, generating prototypes, generating page images, creating flowcharts, generating specification documents, providing prototypes to clients, and designing mobile applications.

Difference between a Brainstorm and Focus Group

Linda Erzah, CBAP — Wed, 22 Jun 2011 05:08:00 GMT

If you work in new product development or have participated in maintenance projects, then most likely you have used either the focus group or the brainstorming technique. The brainstorming technique is used to produce ideas and increase creativity. For example, after you’ve defined your problem and are looking for the different solution options, you gather a few folks from your project team (mostly the development team) and ask them about what they think a solution could be. The result of these gatherings could be ideas/solution that are obvious, or the so-not-obvious-out-of-the-norm options. Brainstorming is a way to ensure that your organization remains innovative.

Focus group can also be used to gather ideas; however, focus group goes a bit further.

For example, focus group is used after the development of a prototype, in order to get an idea of how the market will respond to certain features of the solution.

During a focus group, you are looking to gather the attitudes (in forms of comments and feedback) about an idea, a solution or even a process. The participants of this meeting should be outsiders; meaning folks who are most likely to use/consume to product. The result of a focus group would be the answers or feedback you’ve gathered during the meeting. This feedback can be compiled and organized by themes to present to the sponsors.

Here are the main differences between the two techniques:

	Brainstorming	Focus Group
Purpose	Generate ideas	Improve existing ideas
Trigger	A need to solve a problem	A need to study an existing idea, solution or process
Condition	Problem exist	Idea, solution or process exist
Setup
Number of participants	6 - 8	6 - 12
Participant types	Heterogeneous	Can be homogeneous or heterogeneous
Person running the show	Facilitator	Skilled moderator
Knowledge of topic of discussion	Not necessary	In depth knowledge of topic of discussion
Guide	Develop criteria for evaluating and rating ideas	Create a discussion guide and moderator scripts
Game Time
Ground rules	Must have	Nice to have
Duration	Restrict time to produce ideas 1 – 2 hrs	1 – 2 hrs and sometimes over several days
Type of questions to ask	Progressive closed-ended to generate and build on ideas	Can be open-ended to generate qualitative data or closed-ended to generate quantitative data
Observers	No	Yes
Result	List of ideas combined to form themes	Report of findings Could be - bullet list of information learned - comparative analysis between to solutions - summary of response collected for each question

Capacity review - assets planning

HSantanam — Fri, 19 Nov 2010 15:42:00 GMT

Capacity review

Why should Business Analysts be concerned about IT Asset Capacity planning / review - because business analysts, in my opinion, are all about optimizing business processes - in many cases, closely linked to IT. With that said, here is some food for thought:

IT Asset management - One of the core principles for Information Technology is "Confidentiality, Availability, Integrity". As business fluctuates and product and transaction cycles speed up ever more, it is very important, useful and in the vested interests of IT Management to perform a Capacity review periodically - due to the cost and complexity an annual review might be sufficient in most cases. What exactly is Capacity review anyway? In its simplest form, it is ensuring that IT assets - hardware, software and connected links (networks, telecommunication access to/from the internet, cloud etc) and storage - are sufficient to meet peak demand as defined by company policy or best practices. If one doesn't exist at your organization, review best practices at similar firms in your industry and define it and seek approval. As an example, one brokerage firm that I worked at had a policy that required that the main brokerage applications had to be capable of handling twice the capacity load of the busiest day of the year. So if the servers on the busiest day were at 45% of capacity for an extended period of time, then as per management's requirement, the capacity of those servers had to be able to work at 90% load.

One of my roles, in the many hats that I wore, was to do a capacity review for all the distributed applications at the end of the year. To prepare for this, I first went to our metrics site, where I sifted through reams of data on all the web servers, application servers, database servers etc. Then I organized the data into spreadsheets where I sorted through page views, server resource loads etc. Based on the data at hand, and using algorithms developed in-house, and by looking at the back-end (mainframe, database) data and connectivity analysis, I had a map of how much capacity was used on the distributed side and in-bound and out-bound feeds. That gave us an idea of whether the distributed servers could handle the capacity required for an unusually busy day (think of a very volatile day in the markets - major business collapse, terrorist attacks etc). This is only an example of a specific industry - but such capacity reviews are de rigueur in every industry - telecommunications, transportations, and retail are some that come to mind.

On the software side, analysts and reviewers could simulate many things - online transactional processing simulation, for example, is common. But it might be more useful to ask about the connectivity - sure, the retail front-end web site can take 10,000 orders a second, but can the back-end handle it? Can the connections to the credit checks work successfully and simultaneously at that level? How about order fulfillment - do the fulfillment centers/warehouses have the capacity to handle huge backlogs - if not or unsure, how long would it take and more importantly, can orders be tracked adequately? Can the supply chain handle it - can it be tested? Has it been tested? If these processes are outsourced, does the vendor make any explicit guarantees in the contractual agreements? How often do they test, and how willing and able would they be to do a simulated test (note: these are different from a disaster recovery test, which normally only simulate average loads at a backup site or offer an alternate way to do the same thing you already do).

Capacity reviews are also useful in incorporating forecasts into future budgeting and for justification purposes. Additionally, as technology moves forward at an ever faster pace, old IT assets can be updated, upgraded or replaced by incorporating these reviews ("These 25 servers operating at 90% capacity can be replaced with 5 new ones operating at an average of 50% - and a payback period of 1.5 years"). This will also show management that you and your team have done your homework with substantiated facts.

Another way such reviews can assist is in finding underutilized resources - and at a time of budgetary pressures, might come in handy. For example, for a new application project with limited budget, I was able to point to underutilized servers (which, of course, I knew from my capacity planning exercise) which could host the new app. By sharing resources (servers, existing software licenses on those servers, and storage and network charge backs as well as backup site servers) costs were mitigated.

Capacity Review is a tool that business analysts and business-IT process / operations managers can use to plan, streamline and optimize their assets and thereby provide more value to the business.

My blogs can also be viewed at:

http://business-it-knowledge.blogspot.com/

Moving to Agile Documentation – why ‘Pair Inspections’ make sense

FergalMcGovern — Wed, 03 Nov 2010 11:00:00 GMT

One of the more controversial techniques fostered by some in the agile community is ‘Pair Programming’. It is a practice that originates from Extreme Programming, a specific Agile process pioneered by Kent Beck.

It is controversial, particularly for larger corporates because it seeks to adjust human behaviour patterns. In Pair Programming, developers sit side by side, sharing one machine and working in teams of two at all times on a single code base. In reality, it is one of the agile techniques that is likely least adopted and most controversial among programmers for a variety of reasons, mostly cultural and behavioural in nature. Most fundamentally, for a team to be successful at pair programming takes a lot of hard work. It’s a bit like a marriage really, personality compatibility is a key pre-requisite and just like marriages, the best work well but not all will be successful.

Pair Programming in action

The point of this post is not to get into the specific argument as it relates to agile developer activities for code, but rather to propose something that Project Managers and Business Analysts should actively consider for documentation and that is what I will call ‘Pair Inspections’ or PI.

The issues I have found with larger document sets in lager initiatives, especially larger documents such as BRDs and Detailed functional specifications and Test Plans is that they are:

generally authored by one and only one person with one vantage point
are worked on for a concentrated period of time by one person
do not combine the considerations of other relevant stakeholders
if inspections are going on, they are at a particular point in time, typically at a phase gate and are not that effective at spotting real issues. Infrequent code inspections suffer the same fate in my experience, if I reflect on my time running engineering teams

Now, I am not suggesting that we have co-authoring sessions for a single document. The nature of MS Word and the fact that many people are distributed make this in many cases impractical.

What I am suggesting however is that documents are reviewed actively & informally as part of the authoring & document production cycle. Let me suggest some simple measures that would achieve this:

Frequency: Conduct a Pair Inspection once a week. This may be ‘analysis phase’ in waterfall, or pre-sprint stage in Agile
Alternate stakeholders: Every other week try to include a stakeholder who wears a different hat, e.g. pair a BA with a Test Lead, pair a BA with a PM
Distributed Teams: For distributed BAs working in remote locations, use collaborative tools such as webex, gotomeeting or livemeeting.
Consistency: Put a solid recurring meeting in your calendar every week at the same time and take it seriously
Informality: Make pair inspections a way to gel stakeholders. Don’t impose rules but let the inspection process ‘self-organise’

The net effect is that substantially better documentation results when active & collaborative inspections and reviews occur, regardless of whether you are in a waterfall or agile environment.

Applying the above simple steps tightens document quality in very material ways.

------

Fergal McGovern, Founder VisibleThread.

See our main blog at: http://www.visiblethread.com/blog/

Enter the Business Engineer: Part 2

Mendix.com — Tue, 19 Oct 2010 19:36:00 GMT

In a previous post regarding the emergence of the Business Engineer, I discussed the Who, What and Why of this new type of human capital. At Mendix, we see them growing in numbers, most likely due to the nature of our software. If you’re going to give business analysts the ability to develop software, or developers the ability to communicate business – you’re going to see doors open and walls collapse on either side of the business-IT equation.

The BA

In order to figure out exactly where BE’s come from, so that we can harness their powers for the greater good of business agility, we’ll have to know more about their closest relative – the BA. According to The International Institute of Business Analysis, the role of a business analyst involves: “the set of tasks and techniques used to work as a liaison among stakeholders in order to understand the structure, policies, and operations of an organization and to recommend solutions that enable the organization to achieve its goals.”

Well isn’t that something. All of those tasks and techniques are funneled down into a recommended solution and passed along to the technical wizards who turn requirements into reality. I’ve always wondered whether the less-than-technical BAs wish they could, perhaps just once, finish the job and deploy an application. Perhaps they’d hate to deal with code, I’m not sure. What I am sure of, however, is that business engineers dream about models (business-models, pervert!) and how great it would be if they could get requirements and deliver solutions, on their own.

The BE

Call it selfish; call it futuristic; you can even call it a pie in the sky. With the emergence of the business engineer, demand for agile software development, visual modeling tools and a revamped SDLC is truly explosive. Based on the definition above, the BE is not really related at all to who we now know as the business analyst. Though I mean not to offend nor aggravate business analysts or software developers, I must assume that they see the astronomies of their value propositions colliding.

So then, if the business analyst recommends a solution, it must be the business engineer who implements it. And if the business analyst plans the solution, the business engineer deploys it. I may even go as far as to say that business analysts identify the problem, where business engineers solve it. This fine, yet drastic and seemingly impossible, line – is growing in organizations everywhere.

What’s Next

As I mentioned in part one of this series of posts, the ambiguity of the business engineer is becoming more believable with every Mendix user. What was once called impossible by technologists is now championed as magic by audiences of our ‘proof’ of concepts, and we’re betting will one day be considered the norm in enterprise software development. In the meantime, collaboration between business and IT may remain segregated by occupational obstacles inherent to aging technologies and ancient practices.

The current generation of business and technology students is likely to pave the way for career paths in business engineering. Courses in business engineering will be taught by today’s business engineers, and until then, these heroes of enterprise software can only enlighten their organizations with a glimpse into the future of business agility.

Work Harder, Not Smarter

Seilevel — Thu, 22 Jul 2010 16:13:00 GMT

I am all for great tools and processes that increase efficiency of work, facilitate better accuracy, allow for repeatable results, so on and so forth. But sometimes they simply do not fit. Yes, I just said better is not always good. Why? Spending time trying to improve tools or processes on short turnaround projects or tasks is usually detrimental to the goals at hand.

I have known several individuals who focus more on the tool than the deliverable. In one of those instances they were almost obsessed with the tool, an excel spreadsheet in this case. Over the 3 week project I don’t believe they actually contributed to the content of the list. What was supposed to be a listing of the gaps by area between two processes became a quest to make it such that the excel sheet could be filtered and manipulated such that niche sets of data would align or show patterns. This functionality did not contribute to the project at hand, combine the two processes.

Despite trying to get him to complete the template and move on, he continued to tweak the tool. In the end I resorted to taking one of the iterations of the spreadsheet and working only from that copy, ignoring revisions he had made. By the end of the project I believe I was able to capture all of the gaps and was able to hold the necessary meeting to finalize what must, could and shouldn’t be implemented. This was despite the fact that my list did not have all of the ‘functionality’ of the still evolving spreadsheet.

It is not easy to try to influence people to not get sucked into these constant improvements. Also be careful not to let yourself or your colleagues start to use it as a procrastination tool. “Oh, I’ll start documenting that database with hundreds of fields right after I get this spreadsheet just right.” This is also not good. Big tasks such as data dictionaries are always a pain to get started, but the more you tweak that layout, the more you delay the celebration of no longer having to go through all those line items.

Simply put, what must be done should not be hampered by the lack of sophisticated or niche tools. When there isn’t a deadline breathing down your neck, feel free to start embracing those new best practices or updating the templates.

By JHeep at Seilevel Inc

Do you have comments? Do you want to read more?

Check us out here: http://requirements.seilevel.com/blog/

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.

You can check out our other blog posts here

SAP OnDemand BI

Marc Thibault — Thu, 25 Feb 2010 15:26:00 GMT

SAP has announced a SaaS BI service that looks ideal for SMBs. It seems to have all the tricks you'd expect, so it's worth learning about.

Problem: SAP is being cute about their pricing and I don't want to waste a lot of time on this if it turns out to be another overpriced SAP product.

Has anyone got any idea what SAP intends to charge for this thing?

Blurring the Lines between Business and IT

Derek Roos — Thu, 21 Jan 2010 10:51:00 GMT

A “What If” question for business analysts and IT professionals…

What if it suddenly became very easy for someone to do both your job and their own, at the same time? If history provides any forecast for the future of IT, we are likely to see some interesting changes in the way human capital is managed – especially for those of us involved in the emergence of cloud computing. Clouds push complexity to the background and allow users to focus on what really matters: functionality and costs.

Have you ever noticed how the education we receive often sets boundaries in our career aspirations? We are trained to do something, and do it well – but in doing so, we take for granted the fact that others are doing the same thing in a different field. Then, when we are faced with an inevitable change, we instinctively take a “That’s not what I’ve been trained to do, there are other people for that” mentality. Sure, there are the motivated few who push down boundaries and become renaissance men and women in their own right. But when everyone else is set in their ways, these people are often considered a risk… think: too many eggs in one basket.

Now, to regress from my pseudo-philosophical banter, this trend is becoming all the more apparent as business analysts become more involved in technical training. Most IT veterans would say that business analysts will never have the true know-how to implement their plans, requirements and recommendations. The modern business analyst usually considers themselves more of a problem solver than a programmer – hence the separation of labor in this function of any business. Having surveyed the blogosphere for opinions of business analysts and IT professionals, there seems to be a live (and even a bit emotional) discussion between those who say it is a natural, and therefore inevitable, progression and those who say it is a “pie in the sky” and that it will never happen.

Contrastingly, a growing population of believers has something to say about the segregation of business and IT. In a world of zeros and ones, the innumerable coding languages can only become more and more efficient. As coding languages are continuously created, survival of the fittest can account for the extinct languages of modern programming. An abstraction of these languages is an ongoing phenomenon with a light at the end of the tunnel. Some say that using abstract, visual and human-readable models instead of low-level code is a very important step towards commoditized coding.

I’ve come to think about this abstraction phenomenon as measure to increase efficiency. When our ancestors realized that making bricks was faster than packing sand, they were on to something similar. If someone else uses molds to make perfectly shaped bricks that can be built into any structure, the workers need different skills but can ultimately build more economically, the architect can plan more accurately, and the buyer can move in earlier. So, why deal with sand when we can get the bricks from vendors elsewhere. Why deal with code, when we can get software modules elsewhere? This, my friends, may be the future of today’s business analyst. In the future, what if business analysts had the skill set and the molds to create bricks that satisfy their requirements without the need to deal with code – or sand?

Derek Roos
CEO
www.mendix.com

Help your stakeholders review your requirements

Craig Brown — Fri, 13 Feb 2009 10:58:00 GMT