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Enterprises that use data will thrive; those that don't, won't

A data divide is growing between enterprises that embrace business intelligence and those that don't. It's the data elite that will survive, analyst Wayne Eckerson says in a Q&A.

There's a growing chasm between enterprises that use data, and those that don't.

Wayne Eckerson, founder and principal consultant of Eckerson Group, calls it the data divide, and according to Eckerson, the companies that will thrive in the future are the ones that are already embracing business intelligence no matter the industry. They're taking human bias out of the equation and replacing it with automated decision-making based on data and analytics.

Those that are data laggards, meanwhile, are already in a troublesome spot, and those that have not embraced analytics as part of their business model at all are simply outdated.

Eckerson has more than 25 years of experience in the BI industry and is the author of two books -- Secrets of Analytical Leaders: Insights from Information Insiders and Performance Dashboards: Measuring, Monitoring, and Managing Your Business.  

In the first part of a two-part Q&A, Eckerson discusses the divide between enterprises that use data and those that don't, as well as the importance of DataOps and data strategies and how they play into the data divide. In the second part, he talks about self-service analytics, the driving force behind the recent merger and acquisition deals, and what intrigues him about the future of BI.

How stark is the data divide, the gap between enterprises that use data and those that don't?

Wayne Eckerson: It's pretty stark. You've got data laggards on one side of that divide, and that's most of the companies out there today, and then you have the data elite, the companies [that] were born on data, they live on data, they test everything they do, they automate decisions using data and analytics -- those are the companies [that] are going to take the future. Those are the companies like Google and Amazon, but also companies like Netflix and its spinoffs like Stitch Fix. They're heavily using algorithms in their business. Humans are littered with cognitive biases that distort our perception of what's going on out there and make it hard for us to make objective, rational, smart decisions. This data divide is a really interesting thing I'm starting to see happening that's separating out the companies [that] are going to be competitive in the future. I think companies are really racing, spending money on data technologies, data management, data analytics, AI.

How does a DataOps strategy play into the data divide?

Headshot of Wayne Eckerson, founder and principal consultant of Eckerson GroupWayne Eckerson

Eckerson: That's really going to be the key to the future for a lot of these data laggards who are continually spending huge amounts of resources putting out data fires -- trying to fix data defects, broken jobs, these bottlenecks in development that often come from issues like uncoordinated infrastructure for data, for security. There are so many things that prevent BI teams from moving quickly and building things effectively for the business, and a lot of it is because we're still handcrafting applications rather than industrializing them with very disciplined routines and practices. DataOps is what these companies need -- first and foremost it's looking at all the areas that are holding the flow of data back, prioritizing those and attacking those points.

What can a sound DataOps strategy do to help laggards catch up?

Eckerson: It's improving data quality, not just at the first go-around when you build something but continuous testing to make sure that nothing is broken and users are using clean, validated data. And after that, once you've fixed the quality of data and the business becomes more confident that you can deliver things that make sense to them, then you can use DataOps to accelerate cycle times and build more things faster. This whole DataOps thing is a set of development practices and testing practices and deployment and operational practices all rolled into a mindset of continuous improvement that the team as a whole has to buy into and work on. There's not a lot of companies doing it yet, but it has a lot of promise.

Data strategy differs for each company given its individual needs, but as BI evolves and becomes more widespread, more intuitive, more necessary no matter the size of the organization and no matter the industry, what will be some of the chief tenets of data strategy going forward?

Eckerson: Today, companies are racing to implement data strategies because they realize they're ... data laggard[s]. In order to not be disrupted in this whole data transformation era, they need a strategy. They need a roadmap and a blueprint for how to build a more robust infrastructure for leveraging data, for internal use, for use with customers and suppliers, and also to embed data and analytics into the products that they build and deliver. The data strategy is a desire to catch up and avoid being disrupted, and also as a way to modernize because there's been a big leap in the technologies that have been deployed in this area -- the web, the cloud, big data, big data in the cloud, and now AI and the ability to move from reactive reporting to proactive predictions and to be able to make recommendations to users and customers on the spot. This is a huge transformation that companies have to go through, and so many of them are starting at zero.

So it's all about the architecture?

Eckerson: A fundamental part of the data strategy is the data architecture, and that's what a lot of companies focus on. In fact, for some companies the data strategy is synonymous with the data architecture, but that's a little shortsighted because there are lots of other elements to a data strategy that are equally important. Those include the organization -- the people and how they work together to deliver data capabilities and analytic capabilities -- and the culture, because you can build an elegant architecture, you can buy and deploy the most sophisticated tools. But if you don't have a culture of analytics, if people don't have a mindset of using data to make decisions, to weigh options to optimize processes, then it's all for naught. It's the people, it's the processes, it's the organization, it's the culture, and then, yes, it's the technology and the architecture too.

Editors' note: This interview has been edited for clarity and conciseness.

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How can organizations that are weak on using data close the data divide and catch up to enterprises that use data?
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