Data Analysis & Modeling

It’s no longer rare to see machine learning (ML) models used to support a variety of business decisions, from whether a financial transaction should be sent to the fraud investigation team, to what discount a distributor should get.

Still, even in organizations that have embraced ML-based systems, it’s common for business problems that could benefit from machine learning to be solved using a less effective (and often more costly) approach. 

As a business analyst, it’s useful to be aware of some signs your business problem might benefit from ML...

A point in time field supports a business need for an information system to know when an event took place (or will take place). Date, Time, and Date/Time field values represent a quantity of time involving a specific unit of measure and precision. Like other quantity values, they can participate in calculations (E.g. subtracting one date from another to determine the number of days in-between).

Being “data-driven” doesn’t help create project success; being evidence-based does.  Evidence-based problem solving reduces the risk of blind spots and confirmation bias and increases the chances of achieving the desired outcomes. In high-stakes projects, risks can be dramatically reduced when a business analyst is willing to apply first principles thinking, hypothesis testing, and information value analysis to integrate the best evidence into the decision-making process.

A classification field allows the recording of a meaningful fact about a record instance, with that fact drawn from a pre-established set of values. Online access to values applicable to a given instance might be through a drop-down or pop-up list, or as labelled check boxes or radio buttons. The organization may be interested in just the values, or there may be additional information about each value that the system needs to manage.

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. 

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 explored information system record concepts, the objective of this article is to examine one particular type of field — the record business identifier. Its purpose is to uniquely identify an instance of a record.  Users of an information system are expected to have knowledge of, or access to, this value. The value is used to start down, or stay on, the ‘happy path’ of any business process that deals with the specific record instance it identifies.

Does it make sense to merge Agile philosophies with data science? The short answer is yes, as long as the organization recognizes and accommodates the ambiguous, non-linear nature of the data science process rather than expecting data scientists to fit into the same mold they’ve adopted for “Agile software development”. The problem, in my experience, is that this rarely happens. Probably because the data science field is still new, many organizations are still trying to shoehorn data science into Agile software engineering practices that compromise the natural data science lifecycle.

Data has emerged as the sole driver of the digital transformation of the present world. It has turned into the most significant resource, and without it, one couldn't really expect to succeed in today's crowded market. Organizations should proficiently utilize their data since it very well may be a factor that can differentiate you in corporate development.  This requires the compelling combination of artificial intelligence with data analytics for enhancing business processes.  The automated direction along with data-driven decision-making is rapidly turning into the standard in this digital world. Since the assortment of data and its analysis is more reachable than any time in recent memory, organizations of all shapes and sizes are leveraging this innovation, hence, noticing noteworthy outcomes. Yet, luring those significant experiences out of your data can be challenging when you reach "big data" extents.

This article discusses record types supporting the concepts product, customer, sale, and location. The names given to these records varies depending on the line(s) of business an organization is in and, in particular, the organization’s sales processes.

A picture is worth a thousand words. Charts offer visualization and help to understand and comprehend things that would be more painful and time consuming to understand by reading free text. Diagrams help us design systems and processes, organize our screens, while facilitating a common understanding of the big picture. They help us make visible the invisible.

Αs a BA you can exploit a big variety of diagrams to help you communicate better and more accurate information concerning the requirements and the solution. Diagrams leverages the effective use of visuals and modeling techniques in helping organizations and individuals work from the 30,000 foot view down to the level of detail that is needed by those who are actually going to perform the process activities. Moreover a diagram can serve as a single point of truth navigating what should be done and saving time from questions deriving from ambiguous point may found in a text.

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.