Data Analysis & Modeling

As a data analyst, you feel most comfortable when you’re alone with all the numbers and data. You’re able to analyze them with confidence and reach the results you were asked to find. But, this is not the end of the road for you. You still need to write a data analysis report explaining your findings to the laymen - your clients or coworkers.  That means you need to think about your target audience, that is the people who’ll be reading your report.  They don’t have nearly as much knowledge about data analysis as you do. So, your report needs to be straightforward and informative. The article below will help you learn how to do it. Let’s take a look at some practical tips you can apply to your data analysis report writing and the benefits of doing so.

This series is about understanding data fundamentals applicable to information systems. In this article and the next, record types specific to an organization’s line(s) of business are discussed. These records support maintaining data for an organization’s Products, Customers, Sales, and sale-related Locations. They will be viewed within the context of five generic line of business functions that represent the business processes involving any product as it goes through its lifecycle.

We begin our exploration of information system data fundamentals by looking at types of records applicable to any organization. Records such as     GL ACCOUNT, STAFF MEMBER, and ASSET are well-understood within any organization large enough to warrant information systems supporting Accounting, Human Resources, or Asset Management functions. These functions and record types are well supported today by commercial off-the-shelf (COTS) packages. So well supported, it’s difficult to imagine any organization justifying a decision to develop an in-house solution rather than buy a commercially available one.

The quality of any data analysis created to inform business decisions will ultimately be constrained by the quality of the underlying data. If the data is faulty, then the analysis will be faulty too. This is why data wrangling–the transformation of raw data into a format that is appropriate for use–has become such a ubiquitous task in most organizations. Unfortunately, the significance of data wrangling is still often overlooked. And this is where data-savvy business analysts can help save the day.

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”.