Humans produce around 2.5 quintillion bytes of data daily. However, over 90% of data collected is never read or analyzed. Data monetization is the process of putting your data to work, resulting in economic benefit.
In many businesses, the amount of data that goes unanalyzed is much higher, approaching 100%. We’re spending millions to collect and store this resource, but we’re only putting a tenth of it to practical use. That’s like finding a massive oil deposit underground, and just pumping the crude up to the surface and storing it in huge tanks.
So the problem is not that there isn’t enough data. We have plenty of data, and we’re exceedingly good at collecting and making more.
The problem is one of refinement and distribution. Monetizing oil requires refineries, trucks and gasoline stations to get it to market. Without those, the oil is worthless.
The one big difference between data and oil is that you can only refine oil into a product once, then it’s gone. Data stays around. You can keep monetizing the same data over and over by refining it, analyzing it, combining it, and produce valuable new assets over and over.
The right insights at the right time can be priceless. They can save lives, avert disasters, and help us achieve incredible outcomes.
Great data projects start with great questions. Not “interesting” or “nice to have” questions, but truly great questions that, when answered, will visibly move the needle on the business.
Unfortunately, most business leaders aren’t used to walking around the office asking impossible questions that seemingly no one can answer. But that’s exactly what I encourage them to do.
The most valuable person at the start of any Big Data project is the person who understands what’s possible with Data Monetization. It takes vision, and their confidence gives others the courage to ask the hard questions.
It’s not enough to just collect and work with data. The questions don’t come from the data, the answers do. It’s your job to come up with the best questions.
Organizations across all industries have large volumes of data that could be used to answer consumer and business questions or drive data monetization strategies.
This requires a skill many organizations have yet to develop. To get the maximum economic value from data monetization, organizations should shift their emphasis from Chief Data Officers, or CDOs, to Data Monetization Officers.
Low-cost BI analytical platforms are revolutionizing the way the world makes decisions. A bold claim? Not really. To help us examine the impact of widely used BI platforms with Big Data will have, let us describe how the information sharing and data monetization process works.
Chief Data Officers typically come from an IT background and report to the CIO. A DMO comes from a business background and understands how the business functions the way a COO or CFO would. They’re tasked with using data to provide direct, measurable benefits to the business. Their job is to monetize the company’s information assets. They have an inclination toward revenue growth and are skilled in finding new data monetization revenue opportunities and customers.
The DMO has a strong affinity for measurement. This shouldn’t be much of a stretch for someone with “data” in their job title, but they need to be willing to apply it to themselves as often as necessary. They need to be picky in choosing the truly “great ideas” for data monetization. They need to resist the ones that won’t improve business performance, no matter how neat they sound.
By 2020, most companies will have a specialized resource, or DMO, in charge of managing and monetizing the company’s most valuable asset: its data.
If you’re reading this thinking “We don’t have enough data to justify this kind of role,” Think again. Most companies already have more than enough data to make an initiative like this worthwhile.
Would your company benefit from someone in charge of managing the ROI of data?
How effective is your organization at leveraging data and analytics to power your business?
Could data monetization change the way you look at your data, and possibly create new opportunity for profits?
Are you a candidate for this type of role?
- Do you understand your organizations key business initiatives and what data reflects how they are doing? Do you understand and track leading indicators of success?
- Can you estimate the economic value of your data both inside and outside of your company?
- Do you have the skills and tools to exploit this economic value?