Data Analysis & Modeling

Businesses increasingly depend on smooth data integration, efficient product development, and perceptive analytics to drive innovation, smart choices, and customer value in today's fast-moving, data-centric world. Guaranteeing the three key components work together as "a single, unified entity" requires dealing with many challenges. These challenges relate to organization, communication, technology, and culture. Connecting all data, every product, and all analytics requires a thorough approach. Highly meaningful teamwork, precisely adjusted efforts, and advanced tools are key to success. This post explains how to close this gap and it offers a solution to the problem.

The function of business analysts has changed dramatically in today's technologically-advancing, digitally transformed business environment. Using analytics for data-driven decision-making is one of the major areas where their experience is becoming more and more important, particularly in the field of process management. It clarifies how business analysts can use data to optimize workflows and lead organizations toward long-term success.

Process Mining is a subject that has garnered a lot of attention over the past two to three years. Much of the noise has centred around the mergers and acquisitions in the vendor space, and contrary to what some write, it is still in its infancy when it comes to end user adoption.

Some academics and industry analysts suggest that Process Mining is a technique that replaces the need for traditional business and process analysis, but this is never likely to be the case. Instead Process Mining and its closely related cousin Task Mining, are complementary to traditional approaches, and should be thought of in the context of “and” rather than “or”.

For the past 30 or more years we have increasingly applied automation to processes, both with and without proper documentation. The result is that many of the processes we use, business decisions taken, rules applied, and customer journeys are now embedded or hidden within systems. This cloak of “invisibility” makes it practically impossible for us to apply traditional analysis techniques to discover and analyse these rules, processes decisions and journeys. 

An information system maintains data in fields within records. Equally important is the system’s ability to navigate between records. Parts 5 thru 9 of this series discussed fundamental business data field types. This article discusses a record navigation field. These fields do not themselves contain business data, but support the system’s ability to navigate from a given record instance to business data in related record instances.

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.