Evolution of Reporting to AI - Part 1

In the ever-evolving landscape of data-driven decision-making, several terms often take center stage: reporting, analytics and artificial intelligence.

While they might all seem synonymous at first glance, a closer examination reveals distinct differences that play pivotal roles in shaping business strategies. Follow me in this series blogs, where we will embark on a journey to explore the realms of reporting and analytics, understanding their unique characteristics, benefits, and how they work hand in hand to empower organizations and how they evolved into artificial intelligence. In this series, I’ll start with describing the difference between reporting and analytics. Then explain the difference between analytics, #PredictiveAnalytics, #PrescriptiveAnalytics and #ArtificialIntelligence.

Let's start with "Operational Reporting." Not too long ago, businesses primarily relied on basic reporting to gain insights into their operations. This is operational reporting. These reports are run real time against your operational system. They provide information such as “What sku's were on that truck.” “How many widgets shipped in the past hour.” Operational reports provide you with a snapshot of what is happening now.

Reporting also involves the compilation and presentation of historical data. Many companies still manually pull data together into spreadsheets where individuals apply their own calculations to provide information on past and present performance. It falls short in offering proactive insights for the future. It’s very prone to human error and often leads to arguments about who's report is most accurate because one person might apply their formulas differently. It is also prone to error based on data validation issues, timing of reports and definitions of business terms. Even with this form of reporting, decision-makers were limited to reacting to events that had already occurred, missing out on opportunities for optimization, innovation and improved profits.

Enter "Analytics." As technology advanced, so did the capabilities of data analysis. Operational reporting does and should continue to exist. Analytics emerged as a significant improvement over basic reporting. It involved more sophisticated data cleaning, data modeling and manipulation of data. It also allowed for more visualization techniques, enabling organizations to dig deeper into their data. Analytics allowed the recipients of this information to delve in and really understand what was happening. They could drag and drop different data elements into reports. They could filter, sort and drill down into the data. ?With analytics, businesses gained a better understanding of the "what" and "why" behind their data. Interactive dashboards and data exploration tools became more prevalent, allowing stakeholders to identify trends, patterns, and anomalies in real-time. With the ability to cleanse and validate data, users could rely more on the accuracy of the information. Business rules and significant data models allowed for fast and easy access to information. This capability became a necessity for companies as they began to understand where dollars were most effective and which products, locations, etc to focus their attention on. Analytics helps companies pin-point where they should focus their attentions.?

Follow me for the next two articles where I'll explain the evolution from analytics to predictive and prescriptive analytics and #ArtificialIntelligence. Click here for a copy of the full set of articles.