The following article is a condensed version of the DataSpeak webinar on Data Monetization that the WinPure team held with Bill Schmarzo, the Dean of Big Data. In this webinar, Bill and Ben discuss what really is data monetization and what key steps companies can take to ensure their data return value – both in terms of financial, and organizational value.
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Who is Bill Schmarzo?
Bill is currently part of Dell Technology’s core data management leadership team, where he is responsible for spearheading customer co-creation engagement to identify and prioritize the customers’ key data management, data science, and data monetization requirements.
Bill is the former Chief Innovation Officer at Hitachi Vantara where he was responsible for driving Hitachi Vantara’s Data Science and “co-creation” efforts. He was selected for Hitachi Limited’s 2020 Solution Innovation Award for his ground-breaking work in data science and Automated Machine Learning.
Bill also has served as CTO at Dell EMC where he formulated the company’s Big Data Practice strategy, identified target markets, developed solution frameworks, and led Analytics client engagements with his Vision Workshop, a methodology that links an organization’s strategic business initiatives with supporting data and analytics. As the VP of Analytics at Yahoo, Bill delivered the analytics tools and applications that optimized customers’ online marketing spend.
Bill is the author of four books including:
- “Big Data: Understanding How Data Powers Big Business”
- “Big Data MBA: Driving Business Strategies with Data Science”
- “The Art of Thinking Like a Data Scientist”
- “The Economics of Data, Analytics, and Digital Transformation”
You can learn more about Bill Schmarzo here.
Definition Of Data Monetization
Data monetization is the insights and our ability to leverage and apply those insights in a way that helps us make informed decisions in a way that benefits the business, the customer, and the organization.
Contrary to popular belief, data monetization is not about selling your data. The word has become largely skewed where most organizations take the default meaning and attempt to sell their data.
So what does it really mean? Bill’s commentary on the term can be broken down into three points:
1). Insights: Data, by itself, is just raw information. It is the insights you derive from the data that lets you predict consumer behaviors, assess their performance criteria, identify what products customers are most interested in and use all these insights to provide a better experience and operational efficiency.
2). Value: If businesses can use these insights to predict behaviors, they can provide new sources of value to customers as well for the business. It’s important to remember that data in itself is not actionable. This is a harsh message for organizations that are trying to mask all this data and think they have value, but they don’t. Value then is insights that can be used to dictate an action plan – which leads to our third point, act.
3). Act: If for example, a hospital can identify which of their patients are likely to catch a hospital-acquired infection, then, it can act to prevent the causes that lead to infection. With data leading to insight, the hospital can act to catch a staph infection before an outbreak occurs.
Data, therefore, is a means to an end, but it is not the end.
What About Companies that make money Selling their Data?
This was an interesting question from the audience.
Companies that hoard data are making big money selling customer lists to other businesses. Does that come under the umbrella of data monetization?
Hardly.
According to Bill, you can certainly sell your customer lists and other data, but there is a lot of work you would need to do. You have to ensure you know privacy rules, understand GDPR compliance, and all the other regulations that come with data collection and processing. You can certainly move into the business of selling data, which a company like Nielsen has been doing. They are in the data business and their business is all about pulling data from different places, especially from companies that are willing to sell their data for pennies on the dollar. And then packing it together to sell it off. Again, there’s nothing wrong with that and it is a good opportunity, but it is not really what data monetization in organizations is about.
If you were to sell your data in a bid to make money, you’d be bending over to pick pennies when there could be hundred-dollar bills floating in the air all around you. For example, if you reduced customer attrition by 5%, what is the worth of that to your organization? Or if you increased customer acquisition by 5%, you’re increasing profitability. You can use the same data set to improve cross-sell and upsell existing products while working on new product introductions.
So while you can sell your data if your business model allows you to, it’s better not to get fixated on that one aspect of data monetization. You could use that data internally to optimize processes and customer experiences! Now although this sounds good, one of the biggest challenges companies face in monetizing their insights is usually because of a much-talked-about technology problem – that of data silos.
Data Monetization Cannot Happen in a Silo environment
You would think data silo is a modern phenomenon, but if you talked to people with decades of experience in data management, you’d know that data silos existed some 20 years ago for the lack of better capabilities. Businesses back then didn’t really have cloud or virtual data storage or processing platforms. Today though, with all the technology and virtualization capabilities, there’s no reason why data should be in a silo.
And what would possibly cause data silos in today’s age?
It may be attributed to two important issues: Value and Culture.
Value:
Organizations don’t know the value of their data and one cannot determine the value of your data in isolation from the business. We say that again, you cannot determine the value of your data in isolation from the business. There’s a handful of data sets organizations have that probably provide 90% of the value.
For instance, organizations build a data lake and they throw 35-40 data sets into the data lake, and stand back and hope it produces fruitful results. That’s costly and also insufficient.
Until we don’t define the problem well enough up front or what we are trying to achieve, or what are the major KPIs and metrics against which you measure success is, nothing we do would work! There is a lot of work that has to happen before you even put the science into the data!
It takes a level of collaboration between your data and your analytics team and your business stakeholders to make sure you’re all trying to achieve the same objectives – which could be values that are beyond just financial gains. Customer satisfaction, employee satisfaction, and ethical issues are all part of the variable value calculation metric.
Culture:
Here’s a brilliant example by Bill that explains this issue.
‘I was working with a financial services organization and they’re trying to not only determine their customer lifetime value of all their customers but also what’s the maximum predicted lifetime value. What else could I be selling to them or offer to them to improve their overall lifetime value? To know this, I would need a holistic view that I could only obtain if I could pull data from their credit cards, to checking and savings, and mortgages and home loans, and retirement programs, and college savings programs and other data sources.
The problem is most of these businesses don’t want to share data. The small business units for example, didn’t want credit card organizations to bug them. The wealth management organization didn’t want these organizations bugging their high wealth customers. And so you don’t have that holistic view that allows you to figure out who your most valuable customers are.’
Therefore, it becomes very cultural. There are political caps to data sharing. There are trust barriers. There are even privacy laws that prevent the sharing of these data sources!
What is the Catalyst to Break Cultural or Trust Issues Across Organizational Silos?
Bill advocates design thinking as the discipline that will help solve these problems in the data science industry.
According to Bill, design thinking and data science, from a process and success perspective are different sides of the same coin. They both try to start by empathizing and understanding the problem we’re trying to solve. They both try to democratize ideation in trying to find those variables and metrics that might be better predictors of performance. They all seek to drive specific kinds of outcomes. They all, they both embrace experimentation and failure as a way to learn, right. And design thinking is very powerful actually has a patent process where that brings together data science and design thinking as one common methodology because they are so similar in what they try to do.
What is Design Thinking and How Do You Tie that Into a Data Monetization Strategy?
Design thinking forces us to walk along the journey of our key customers or constituents who have an intention and an aspiration about an outcome. We can walk along with them and understand what actions are they trying to take, what decisions are they trying to make, what are the KPIs and metrics to measure, and what are the pains and gains? And we can literally build analytics to help them smooth along and optimize that process. We can build data products along each of those steps to help optimize that process.
Design thinking, therefore, means bringing together a culture of empowerment where ideation is encouraged and everybody in the organization can contribute to or apply data analytics to drive improved, more relevant, more actionable outcomes – which in turn helps your business make money.
You can read more about Bill’s design thinking principles here.
To Conclude
Data monetization isn’t about selling your data. It’s about leveraging your insights and using design principles to move your organization into acting on those insights to deliver on value.
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How Does WinPure Help With a Data Monetization Strategy?
Master data management is a big part of data monetization. To get insights, you need to create master data records that are reliable, accurate and can act as a single source of truth. WinPure’s no-code, point-to-click MDM platform lets you clean, match, and consolidate multiple data sources in just a matter of minutes. You could save millions of dollars on expensive platforms, hire data talents more strategically, and spend time working up a strategy rather than in doing grunt work. Let the software take the load off you so you can focus on building an effective data monetization strategy!
