There is a lot of data in the world today. The amount of data stored increases dramatically every year. Everyone knows it already and are tired of hearing about the subject.
Storing data is getting cheaper but there is still a cost associated with it and what’s worse, data sitting on a disk doesn’t do anything. It simply costs money and time to store, secure and back it up.
It is what is done with the data that makes it valuable.
Descriptive analytics (aka reporting and dashboards) tells you what happened. To get to that point requires manual work to sort, select, and summarize the data so it can be presented to a person for interpretation. The data still doesn’t tell you much about the future since the viewer has to extrapolate from the past in their head.
Even more labor intensive is exploring the data visually. That is akin to pulling out a map, picking a road, and then hoping that road leads you to someplace interesting.
Descriptive analytics over stored data certainly has its place in business operations. Reporting and dashboards monitor existing processes, alert people when something is off target or goal right now, and communicates what is happening in operations to different people simultaneously. But they still do not find patterns, predict trends or recommend actions to take. That is the mission of AI.
Artificial intelligence (AI) and its various forms of machine learning, predictive analytics and prescriptive analytics uses the historic transaction data to provide insight into patterns, predict future values in the trend, and recommend actions to take. AI uses the same historic data but looks at it differently than a human would to come up with insight that a human MIGHT find. More so, AI can run across large amounts of data and still take action. That is something a mortal human cannot compete with.
That is how AI and machine learning makes the data sitting in storage into something that is of value to the company.
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