The Cold Start Problem with Artificial Intelligence

Turning Data into Analytics

Even though AI is currently what everybody talks about, before we get there we still have to take an intermediate step, which is Analytics. What I mean by Analytics, is the systematic computational analysis of data or statistics. In most companies, the process to get to the visualization of the data might be known to few, but the impact it has on each department is tremendous.

Analytics is the systematic computational analysis of data or statistics.

Companies need to determine which Key Performance Indicator’s (KPI’s) actually drive the business. Working in the Payments Industry, my KPI’s include Processed Revenue, Transaction Costs, Profits, Authorization Rates, Chargeback Rates, Fraud and many others that provide me with the information to manage the performance of the business. For a Taxi App, KPI’s might include, Revenue, Profit, Average Pick-up Time, Average Ride Time, Active Users and Active Drivers.

From those KPI’s, a company can then decide what type of reporting or dashboards are necessary for the business users to make informed decisions and work on automating the systematic computational analysis of the data or statistics.

But as the volume of data increases from KB’s to TB’s, and business users are looking more and more at aggregated reports and visualizations of the data, the chances of detecting smaller issues reduces significantly. It is only then, that implementing AI can become a worthwhile investment of time and resources.

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