Lay the groundwork now for advanced analytics and AI

Proper data integration, modeling, and maintenance make up the unglamorous but necessary foundation for high-impact analytics and AI applications. Without it, data is too hard to access and, even if can be analyzed, will deliver inaccurate results.

When global technology company Lenovo started utilizing data analytics, they helped identify a new market niche for its gaming laptops, and powered remote diagnostics so their customers got the most from their servers and other devices.  

Comcast is using data analytics to reduce the cost, and improve the efficacy of, its 10P byte of security data to better understand attacks, respond more effectively, and improve its ability to predict future threats.    

And at First Commerce Bank, EVP and COO Gregory Garcia hopes to leverage unified, real-time data to monitor risks such as worsening vacancy rates that could make it harder for commercial property owners to pay their mortgages.

But reaching all these goals, as well as using enterprise data for generative AI to streamline the business and develop new services, requires a proper foundation. That hard, ongoing work includes integrating siloed data, modeling, and understanding it, as well as maintaining and securing it over time.

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