Data Analysis & Modeling

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.

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.

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.

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.

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).

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.

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.