Many companies haven’t seen the payoff from their big data investments. There was a disconnect. Business and tech executives thought they could do a lot more with their data, but the learning curve was steep, tools were immature, and they faced considerable organizational challenges.

Now, some are rethinking their data strategy as the landscape matures and AI itself becomes more real and practical. They’re starting to ask the right questions, like: How can we make our processes more efficient? and What do we need to do to automate data extraction?

At the same time, organizations are now able to take advantage of new tools and technical advancements, including:

• Easier methods for mining less-structured data, including natural language processing for text indexing and classification
• Enterprise application suites that incorporate more AI
• Emerging data lake-as-a-service platforms
• Public clouds that can take advantage of different kinds of data
• Automated machine learning and data management

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The right approach to data

It’s rarely a good idea to start with a decision to clean up data. It’s almost always better to start with a business case and then evaluate options for how to achieve success in that specific case.

A healthcare provider, for example, might aim to improve patient outcomes. Before beginning to develop the system, the provider would quantify the benefits that AI can bring. The provider would next look at what data was needed—electronic medical records, relevant journal articles, and clinical trials data, among others—and the costs of acquiring and cleansing this data.

Only if the benefits—including measures of indirect benefits and how future applications can use this data—exceed the costs should this provider move forward.

That’s how many organizations will ultimately reform data architecture and governance: with AI and other technologies offering value propositions that require it.