Data Analysis & Modeling

In this article we focus on record name fields. These fields are intended to contain a user-recognizable value by which a person or thing is known, addressed, or referred to. Unlike a record business identifier field, a name field’s value may change over time. Also, there are ‘real world’ names for things (e.g. people, cities) for which valid duplicate values can exist. 

Having discussed fields intended to name record instances, we move on to fields intended to satisfy the need to say something quantitative about a record. A quantity field requires particular attention be paid to its unit of measure (UoM) and precision. 

Analytical techniques in process discovery and improvement, including process mining and simulation, have been available for many years. In the past, however, they were used primarily by people with a technical analytical background. The current business environment and technological advances have pushed these analytical tools into the mainstream, where they are being recognized as essential for all levels of business analysts.

Is your requirements approach friendly to vocabulary, policies and messages? I mean directly. Wouldn’t it be of great help to your organization in achieving its goals if they were? In our experience, policy sources almost always need interpretation and disambiguation to achieve an effective practicable form. As this column discusses, the rule message ‘Reserved for Green Car’ provides an excellent case in point.

As mentioned in my previous article Three Myths About Data Science Debunked, sooner or later business analysts will be involved a project with a machine learning or AI component. While BAs don’t necessarily need to know how statistical models work, understanding how to interpret their results can give them a competitive advantage.

This article discusses three concepts that can help analysts add value to data science projects (future articles will cover additional ones). Cultivating skills in these areas will increase your ability to build cross-functional alignment between business and data science teams and prevent bad decisions based on flawed analyses.

There’s so much buzz and interest about concept models these days, we asked Ron to summarize what they are, who they’re for, and why you need them. Here’s his response, short and readable. He’ll also touches on how you can get started, and where to find more information.

Despite the lack of consensus on the definition of data science, many organizations already have a data science team. And even in companies without data scientists, sooner or later business analysts will join a software or process improvement initiative with a machine learning or AI component. When that happens, good understanding of what data science is (and isn’t) can make a big difference in a BA’s ability to create value.

Master Data is a concept that most IT shops are familiar with; Master Rules is not.  Master Data cannot address the issue of data quality without pairing it with the rules that define and/or derive that data; that is, the Master Rules.  Sooner or later, all significant financial sector organisations (in particular) will confront an impending migration, regulatory pressure, M&A, commercial imperative, or other compelling need to improve the management of their business rules; then, it must be done – Master Rules must be implemented to provide the authoritive view of rules that their importance requires and deserves.

Data science and analytics is a dynamic world and anyone pursuing a career in analytics needs to stay on the cutting edge of the latest tools and conceptual approaches to advance their career. These certifications prove to any employer that you are a valuable candidate whose passion is matched by their knowledge, as well as a desire to keep learning. Don’t get left behind by your competitors, prove your worth with these certifications.

The majority of IT business analysts spend their careers in “reactive mode”. They are assigned to tasks like define the requirements for a new partner loyalty program, create user stories for an enhancement to a billing system, and go about delivering their artifacts.

Data-inspired analysts are those analysts who make a conscious decision to “go upstream” and find data to help their organizations identify the areas of value creation with the highest return on investment before jumping into “solution mode”.

As the pandemic continues to upend life around the world, data, big and small, takes a central role in mobilizing the right efforts to prevent a much greater calamity. And as people and organizations face unprecedented hardships, business analysts, data scientists, and data analysts are going to be integral to the solution. We have the skills that the world is counting on to arm our leaders with the best possible information as they are tasked with making immediate choices, allocating resources, and anticipating the next obstacles to overcome.

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.