How can companies build a strategy for monetizing data?

Laggards and leaders in data monetization

Are most organizations thinking about monetizing data in these broad ways?

How do we extract value from data to drive better outcomes in whatever business we're in? I think that's a better, more encompassing definition.

Maccaux: It depends on the company and largely on the executive staff of the organization whether they see data as something useful to them or not. Look at the executive team. If they don't understand anything beyond dashboards, they probably can't even guess why data is so important. That will influence the culture and processes, and it informs whether budget is spent on technology to make data an asset. If they don't understand data, they won't authorize the millions the investments require to leverage advanced analytics.

Most organizations have reporting. They have data warehousing and reporting -- some level of business intelligence. That's looking at what's happened in the past. That's not advanced analytics. Advanced analytics is using data that happened in the past to predict what is likely to happen in the future, and to do that you need more data, more sources, advanced technology to process that data and people who know how to process that data and to take action on that. That takes lots of money.

Is there a ceiling on the value from data that companies can extract?

Maccaux: I don't think so. I don't think there's a ceiling to it. The list of use cases is endless.

Organizations have been talking about monetizing data for at least several years now. Where do you rank the maturity of most organizations on this objective?

Maccaux: We have to carve it up into different chunks. You have the internet companies of the world: Data is their business. These companies were built on data. They set the standard. If we exclude them from our analysis and we look at the rest of the enterprises, nonprofits and government agencies, I would say it's a mixed bag. The leaders [in using data] are almost always [telecommunications] and financial companies. The most forward-looking ones in these industries are saying their industry isn't money; they're digital data companies. But even within those industries there are companies encumbered by legacy and by executives, staffs and processes that make them slow to transform.

Three-pronged challenge

What are the biggest challenges most organizations face in either monetizing their data or extracting the most value from data?

Maccaux: The first challenge is people. The people challenge starts at the top with the board; they have to say we're going to be a data-driving organization. The second challenge is whether organizations have the process in place. Just because they say they can monetize data, they might not have the processes in place to react to the data and the processes to take advantage of the data-driven decisions. And the third piece is technology, because without the technology in place to do the analysis and act on it, then you have a major challenge.

Is it up to the CIO, or the chief data officer (CDO), to drive the response to those challenges?

Maccaux: It's important to separate the CIO and the CDO. They need to be separate because the CDO is not a technology officer. That person is responsible for figuring out what the data monetization strategy is and working with the CIO on a technology plan that takes that into account and applying governance around the data. That's the primary mission of chief data officer today.

So, it's the combination of those two who can work to influence the rest of the organization. They can work together in tandem to drive a proper strategy.

From a process perspective, everything flows from there. The CIO can determine technology decisions, and the CDO with the COO can define the organizational processes that need to be put in place. It's those two or three roles -- and the chief security officer; everything derives from that. They define the strategy, execution plan and the technology.

How do organizations embark on, and then measure, a successful data monetization journey?

Maccaux: It has to be multifaceted. The technology stuff is easy: Get rid of legacy where you can, put modern technology in place. It takes years to do, and there are milestones around doing that, but that is kind of the easy part. There are very well-defined milestones around that -- such as consolidating data as much as possible and making it available through self-service. That's the technology roadmap. If you can get to a point where your users can self-serve with data, then that's the outcome you're looking for.

But to do that, you have to have the processes and organizational structures in place to define what data can be made available and under what conditions and, as we derive more insights, how do we fold that back in. This is where the role of data steward comes in, and this is where an understanding that no one group owns the data but it's the organization that owns the data.

Having that organizational structure in place with the processes to define the data stewardship role is critical and it's really hard work. And it has to come from the top -- from the CEO or the COO.

So, putting those processes in place, from the business side, where the technology and business people are working together, is the critical milestone. Then you can get fine-grained with the processes.