Top 5 things to know about data intelligence

One buzz word on the rise is “intelligence.” It’s born of a real problem. Companies have data. They’ve digitally transformed. And now companies can find out what their sales tally was—but that’s in a different system than customer preference data and marketing info. 

You see, it’s not enough to just have the information online: You need to make sense of it.

Here are five things to know about intelligent data. 

1. Lock-in isn’t working out. Vendors hoped that being the provider of your data would keep you in their building. But normally, no one vendor has all of a company’s useful data. And customers are demanding openness so they can use their Salesforce data with their Microsoft data, for example. 
2. Startups have solutions. You say you want to combine data from different siloed services? We have the solution at startup with Data in the name! Our several billion dollar valuation shows how good our service is at taking your data and creating useful forecasts from it. 
3. The big companies are using it but they may be laying the foundation of their own demise. Vendors like Adobe and Atlassian rely on startups to bring intelligence into their system. This may mean that what Salesforce is to Oracle, Databricks might be to Salesforce. 
4. It’s about questions, not status. The previous wave of digital transformation was about getting your data into a dashboard so you can see it. The intelligence wave is about letting you ask questions, like, “What will my customers want next June?”
5. There’s a swing toward openness. A great tool that interprets data from a vendor can be the best artificial intelligence ever, but if your data isn’t all in that vendor’s system, you’re going to have questions. More agile outside companies who don’t have an interest in keeping your data in one bucket are going to have the advantage. 

We’re just at the beginning of this trend, so get ready to hear more about systems of intelligence and combining your data in a data lake. But it does look like it’s a swing toward reducing lock-in and getting more useful predictive insights out of all that data you worked so hard to gather. 

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