Artificial intelligence has struggled to live up to the hype in recent years. If you were to believe the buzz, AI would be responsible for automagically solving all our biggest problems with complex computer wizardry and granting everyone a life of leisure and simplicity. It reminds me of the Hitchhiker’s Guide to the Galaxy, in which hyper-intelligent beings design a computer to reveal the answer to the meaning of life, the universe, and everything – only to find out that the answer is 42, and they never knew what the original question was anyway.
At the same time, information management approaches have failed to keep pace with technological change. Most technologies were built and designed for the days of on-premises applications that wrote to on-premises databases, where the goal was to extract data and load it into a data warehouse for business intelligence (BI) and reporting. While that need still exists, the data that we manage and the ways we extract value from that data have all radically shifted and diversified.
We are left with a complex mix of structured, unstructured, and object store data residing in a blend of cloud and on-premises systems with access often limited or non-standardized via APIs. The result is a complicated landscape of data sprawl, tooling diversification, and data silos. This all leads to an increasing inability to “locate the wisdom we have lost in knowledge” and the “knowledge we have lost in information” (credit to T.S. Eliot).
The combination of this failure of AI and information management can be seen in a few data points:
- 86% of enterprises claim that they are not getting the most out of their data.
- Five out of 10 early data-science initiatives fail to get to production.
- 74% of enterprises say their data landscape is so complex that it limits agility.
And perhaps most telling: two-thirds of businesses consider machine learning and AI important business initiatives, but only one-third or fewer are confident in their ability to implement them.
This is why we have developed an entirely new solution from the ground up, with open source and cloud principles in mind, to ask how you tackle these challenges in order to unlock the true promise of enterprise AI and achieve data intelligence. Data intelligence is what happens when you bring together both halves of the equation: managing your data wherever (and whatever) it is, then extracting value from that data using the latest tools and techniques.
Find out how SAP Data Intelligence can help companies get business value from their data assets in a trusted, scalable, and governed fashion. Start your free trial of enterprise AI and intelligent information management today.
For more on this topic, please read “Artificial Intelligence Without Data Intelligence Is Artificial” and the “Enterprise Data Strategy” series on The Digitalist.
And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.
This article originally appeared on the SAP HANA blog and is republished by permission.