Data monetization is the ultimate goal for most organizations as they invest in big data and analytics capabilities,...
according to Matt Maccaux, leader of the big data practice within Dell EMC Consulting Services. Yet, organizational ambition often outstrips capabilities, as many enterprises still struggle with getting the right data program in place to extract real value from data. In this Ask the Expert, Maccaux shares some of his insights on how organizations should think about monetizing data.
Editor's note: The following has been edited for clarity and brevity.
How do you define the term data monetization?
Matt Maccaux: If we're talking to companies that are financially incented -- that is, to make money or to save money -- the definition of data monetization is fairly straightforward: They're using data to improve the top line or improve the bottom line. For example, they're using that data to determine which customers to target or which markets to enter.
But that [narrow definition] excludes really good use cases.
We helped an organization that was trying to help schools achieve higher graduation rates in an underperforming area that had trouble getting students to graduation. So, we used data to inform curricula and other decisions, even down to which students should sit next each other. The result was better graduation rates. But monetization isn't the right word for that example.
So, instead I'd ask: 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.
Does this expanded definition for monetizing data always translate into dollars?
Maccaux: It could always translate back to dollars, but it doesn't have to always tie back to money. Maybe there is a monetary benefit, but that's not the stated goal for a lot of use cases [aimed at getting value from data]. For example, an airport is attempting to improve the traffic in their infamous loop around terminals. If they can get more traffic through the airport, they can get more passengers through, so maybe that means happier passengers who are willing to spend more shopping or eating at the airport. That might mean more revenue for the airport, but it's really about improving the customer experience for the airport and the passengers. The money was not the primary or even the secondary goal; it's about moving passengers into and out of the terminals in a happier way.
Laggards and leaders in data monetization
Are most organizations thinking about monetizing data in these broad ways?
Matt Maccauxleader of the big data practice, Dell EMC
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.
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.
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