The Essential Steps in Managing Data for AI and Analytics

The Essential Steps in Managing Data for AI and Analytics

The enemy of business? Incomplete data sets that can’t be collated to get a full picture of opportunities or threats. How companies collect, integrate, analyze and visualize data is hugely important, but most have built static data architectures that take considerable time to build and change.

Having an outdated data landscape will slow down any organization that is trying to do more with data, or moving towards emerging technologies such as AI. To accelerate time-to-market, create more targeted marketing, reduce churn, create new product offerings and gain a deeper understanding of customers, firms must first modernize their data assets and pool them in modern data platforms. Only if we bring data together can we ask one question and get one answer.

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Many organizations already have a data lake in place, but many of them lack best practices such as an information governance, compliance or an API strategy to enable sharing of data. Whatever their stage of data maturity, we advise clients to adopt a human-centric approach, where usability is at the heart of the solution. 

We also encourage them to apply AI at each step to augment and enhance every aspect of business, and to move towards a modern and adaptive data platform that that enables business to strive for excellence.

Iterative approaches, where companies engage in siloed projects without considering dependencies, can cause problems. Better to think holistically and build out. An example: we worked with a bank to bring together data on customers’ mobile transactions and website activity history. Once the bank saw that working, it wanted to use this data lake for anti-money laundering. The use cases were then expanded and what had been data siloes involving expensive, error-prone processes, are now accurate and standardized sources of information.

To learn more, check out this in-depth article as well as Cognizant's AI offering overview

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