Data Analysis & Modeling

Visual analysis models provide a powerful set of tools that let business analysts depict system information at various levels of abstraction. These models serve as an aid to understanding, as well as an aid to communicating. Alas, I fear that modeling is somewhat of a neglected practice. I believe modeling is an essential skill every BA should master. Here’s why.

After some research, I was taken back with so many machine learning applications already in use: weather forecasting, medical diagnoses, law enforcement, and self-driving vehicles. Also, I did not realized that it was the advancements of big data and faster computing that allowed the break-thru of AI in our daily lives. Most of us, I believe, think that artificial intelligence is still science fiction. Not so! We as business analysts need to pursue AI education and recognize the many business opportunities opening up to all of us.

It’s important for business analysts to recognize that there is a significant amount of non-technical (i.e. business) detail associated with a system interface capability. The interface is either importing data that’s needed and available in electronic format from another system, or exporting data in electronic format when it’s needed by some other system or organization. The data is either needed in real time or can be processed as a batch job.

The previous article in this series discussed ensuring that high-level requirements (HLRs), within the context of an IT-based project, were properly high level. The remainder of articles in the series will look at detail requirements and the need for them to be sufficiently detailed. The objective of this article is to demonstrate how a data dictionary (DD) can be used as a tool for capturing the appropriate level of detail representing data-specific business needs.

We live in a time when business in many industries offer similar products and use comparable technologies. One of the last points of differentiation are processes, and the evidence is clear, in sector after sector: companies that figure out how to combine business domain expertise with advanced analytics to improve their internal and customer-facing processes are winning the market.  Let’s take a look at three of the many opportunities that the advanced analytics technologies developed over the past decade are creating for business analysts..

While many business analysts may be able to get by without ever writing a single line of code, the ability to write and interpret SQL queries can greatly increase your effectiveness as a BA. The purpose of this article is not to provide a tutorial on learning SQL, however, it is to demonstrate how SQL can be used in various business analysis techniques without having to rely on more technical roles such as data analysts or developers (they have plenty of other things to do).

Strategists, architects, process experts, software developers, data managers and other professionals involved in changing the enterprise often put substantial effort in creating all kinds of useful models of their designs. In many cases, such business models, enterprise architecture models, business process models, software models, or data models are only used to specify some design, i.e. to describe what should be built. 
But there is much more value to be had from these models, by using powerful analysis techniques to elicit new insights. In the following pages I will cover 7 of these analyses, discussing the business outcomes you can achieve with their help.

When it comes to Predictive Analytics, several algorithms can allow you to use the available data by constructing a prediction model. Previously, we looked at a few algorithms designed to calculate probability. Another popular predictive analytics and AI algorithm is a Decision Tree known as C4.5.

With the help of visualization tools, data can be converted easily to more accessible form hence making it much simpler to understand. Data Visualization is available for both developers and for presentation ensuring ease of work for both.

There is much to say about the often challenged relationship between IT and “the business” that has existed since IT became IT. Centralization, decentralization, self-service tools and applications, enterprise tools and applications – the pendulum swings again and again.  You’d think by now that we’d get it. There is no one all-encompassing data management or BI solution that will satisfy all of your data related requirements.

Somebody inquired to me in one of the professional networking site if I can suggest what the difference between a Business analyst and data analyst is.

This is a dilemma that is common in the minds of numerous professionals who are new to Business analysis or intending to get into this space.

As the name proposes a first hand analysis by any layman will state that the business analyst role includes analysis from a business perspective, though the data analyst role deals with primarily analyzing data.

Chaos! Stress! Everyday mess! Isn’t this an everyday situation for a business analyst? If not, either you’ve job satisfaction or you’re not being introduced to the real world of business analysis.

A person might possess great skills, however, (s)he might not be able to utilize skills without the right mix of tools and environment. A toolbox enables a person to implement the skills in the most efficient way. Possessing necessary tools is just the one part of it. Another is the knowledge to utilize the right tools at the right time to cater the solution and ensure timely committed delivery.

What are these tools? How do we map the usage of tools to the given circumstance? How can we efficiently utilize the tool? Does it depend on the solution or the approach?

It your responsibility to make sure that you understand what the impact of GDPR are going to be for the data that you work with. There is no doubt that this is going to shake things up in the data protection world for both organisations and individuals. We will publish another blog that will tell you what steps as a business analyst you need to make sure that you and your organisation do not breach GDPR regulations.

I don’t know how many articles I’ve read where the author states requirements should be “what” the user/client needs, not “how” to deliver the solution. They say “A requirement should never specify aspects of physical design, implementation decisions or system architecture”... In my humble opinion, every requirement, even the business level needs, goals and objectives, are just the start of a long march to a solution.

State Models include two RML Data models (State Tables and State Diagrams) that show the transition of an object through various states or statuses, including which transitions are valid and what triggers an object to transition state. A state is a short-form description of a stage in a data object’s life that influences the behavior of the system.