Many BAs struggle to produce ‘normalized’, function-independent data models (or don’t produce them at all). Very few business stakeholders can appreciate such models as “… a picture worth a thousand words.” This article describes an easy-to-create, simple-to-understand view data model. The view is of just those records involved in an information system capability supporting a specific business activity.
NOTE: This article uses the business-friendly terms record and field rather than the usual data modeling terms entity (or class) and attribute.
In this fun piece, Ron examines the connection between rules and counts, such as KPIs. Ever wonder why different people can count the very same things and come up with different answers? Fear the numbers you’re going by aren’t telling exactly the right stories? In viewing a measure, how far the truth might have been stretched? Come along on this short travel story and let’s explore the matter together.
The practical applications of data science are multiplying. From predicting if a delivery will arrive late to recommending how much herbicide to use to save money and protect the ecosystem, there are endless examples of organizations harnessing data science solutions to improve the efficiency and quality of business decisions.
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.
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