Evolution of Reporting to AI Part 2, Predictive & Prescriptive Analytics

Analytics

Don't make decisions in a bubble! As technology advanced, so did the capabilities of . Operational reporting does and should continue to exist. Analytics emerged as a significant improvement over basic reporting and "hunch based" decisions. There's no replacement for experience, but making decisions without factual data to support it is like making a decision in a bubble.

Analytics involves a crucial step that requires more sophisticated data cleaning, data modeling and manipulation of data. As a result, analytics allows 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. 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.

Enter Predictive Analytics

Predictive analytics, the next step in this evolution, marked a paradigm shift in how organizations leveraged their data. This methodology involves the use of historical information and statistical algorithms to make predictions about future outcomes. By analyzing patterns and relationships within the data, predictive analytics empowers businesses to anticipate trends, identify potential risks, and seize opportunities before they even arise.

The foundation of predictive analytics lies in the quality of data. Organizations must gather, clean, and prepare data from various sources to ensure accuracy and reliability. This stage is crucial as the success of predictive models depends on the integrity of input data.

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In , you might integrate sales data with shipment information and order information. Say you are out of stock in 150 stores. But you can see that of those locations, you have shipments in transit to 100 of those stores. You can also see of the 50 remaining stores, you have 20 stores where there are orders. But by integrating the data and creating a reliable source of information, you can now identify that there are 30 stores, where you are out of stock with nothing in shipment and nothing on order. Now you can quickly identify where to focus your attention.

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In another case you can look at historical sales trends. Perhaps you are integrating weather trend data. You can see that you typically send “x” amount of duct tape to these 200 stores in South Carolina. Weather trends might be predicting a hurricane the second week of October. Having the right data sources integrated, you can predict what will likely happen and make preparations for that happening.

takes the information from what you’ve learned in your predictive analytics and automates adjustments. It can feed information back to the source system, where after human review, can be applied to source systems. For example, as in the situation of the hurricane above, you might have the system reallocate shipments to the stores where hurricanes are anticipated to hit the hardest.?

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