How to Turn a Data Policy into a Data Strategy

Video produced by Steve Nathans-Kelly

At Data Summit Connect 2020, DataStax VP Bryan Kirschner explained the consequences for organizations that generate and store data without developing a plan to leverage it. 

Full videos of Data Summit Connect 2020 presentations are available at

"Many of you have probably been on a digital transformation journey, right? This CIO's company is doing digital business at scale, but they don't have a data strategy. He was very blunt: 'I generate a terabyte of data a day, but I don't have anything to do with it. So every month I just throw it away. I delete it. There's no one studying this data, trying to turn it into significant value.' They don't have a data strategy. They have a data policy. They control the cost and risk of storing data, right? So they're generating data, but they haven't invested. And how we turn this data into value. So square one or square zero. What does it take to start to turn that data into significant value? So we talk to CEOs, our customers experts, and we came up with a list based on what we heard," Kirschner said.

First, enterprises need a data strategy. A data strategy is going to make the ask, "how can we make real significant changes to the value proposition we offer customers?" It's not just about incremental improvements.

Then organizations need a chief data officer. It doesn't have to be someone with a title CDO, he explained. It could be a CIO, but it needs to be a senior executive who makes sure the organization is not just advancing the strategy and asking hard questions, but also orchestrating other parts of the business. 

AI and machine learning can deliver these enhanced value propositions to customers.

"You can be on a journey and we'll show you some data about how some companies are using AI in production at scale. It's OK if you're not there yet. But you need to at least be investing in increasing those skills. And you need to be starting to deploy AIOps to optimize your infrastructure and reduce toil so people can focus on creating value rather than maintenance and operations," he said. 

And finally, the organizaton needs to have data portability and data scalability to operate at scale. 

"You have to have many teams and people across your company or across your partner ecosystem have access to the data technology strategy," he said. "This again is a flywheel. As you're able to deploy cloud-native applications fast to scale them on demand. You're more able to have your teams, your chief data, officer, your strategists, thinking about how we could deploy AI and ML to be focused on creating value rather than maintenance and optimization because you're using AI AI ops, and you're able to have more people in teams working on it because your data is portable and scalable. So this is a virtuous cycle of being able to deliver great experiences at speed, use data and insights to make them better, rinse and repeat.