“We just can’t find data to analyze!”
“If only we had more data to work with!”
“We’ve solved all of our data challenges, let’s buy some more data!”
These are but a few examples of sentences people have never said. But these kinds of statements are implied in organizations that believe others can’t wait to pay good money for their data. If we struggle to make use of our data assets ourselves, how reasonable is it to think that others will be able to turn it into real value?
Selling data (or data derivatives) is not a viable get-rich-quick strategy for most companies. This is no longer a creative business model, and the market has become oversaturated with companies that think their data-gathering-and-redistribution strategy will make them the next Google or Facebook. The easy money has already been made in this space—and even our venerable, large organizations that have substantial proprietary databases will find that most market opportunities will not justify the investment to make it client-ready.
The truth is that we all already have plenty of data, and we’re not doing a great job managing, governing, or monetizing it ourselves. Data Monetization is turning data into actual dollars, most commonly by focusing on the increasing revenue piece of the Data Value equation (see http://tdan.com/the-data-forecast-intro-the-value-of-data/20304 for more about Data Value). The way Data Monetization becomes worthwhile for organizations is through a multi-pronged strategy that collectively justifies the effort and expense to create it.
Not unlike Data Quality, Data Monetization opportunities exist on a curve that shows the level of effort and resulting value alongside how removed we are from the atomic data. Atomic data is a common data warehousing term that means the data cannot be further reduced. For example an order is made up of one too many items and related quantities. An order is not typically an atomic data point, but the order line items are.
When we think about Data Monetization, the path of least resistance is selling it to others that find it valuable. This can be something like providing email addresses for other companies that want to market to your customers. This is atomic-level data that we’ve done nothing but collect. It may be useful to those whom we sell it to, but it comes with more risk.
The more atomic a monetized data set is, the more likely our main customers will resist its sharing. We must be careful not to do something that could anger our customer base, or run afoul of legal and regulatory restrictions. The financial impact from these would likely far outweigh anything we would make through Data Monetization efforts. That said, people today do understand that a data economy exists, and that some of the things we enjoy for “free” are paid for by targeted advertising or other data monetization techniques.
Instead of simply selling data, an alternative is doing additional work ourselves to develop insights about the data that we then sell to others. Assuming these are useful to other parties, would it be more or less valuable to them if we could skip them the trouble of doing the analysis—and deliver these insights to them directly? This would save them effort by providing what they care about most, all while protecting the details of our customers’ information. Not only would these vendor-clients be better served, but they will likely be willing to pay more for this added-value service!
The challenge of this approach, of course, is that we need to be competent at developing actionable insights from the data we have. See the cycle at play here? We need to be competent at using data ourselves if we hope to gain the added benefits of externally-facing opportunities.
The power of Data Monetization lies not purely in the data, but in the analytics that drive business outcomes. It is one thing to have data—all organizations do. But how many are making the most of the data they already have? Is the hypothesis that more data will help them really the most reasonable conclusion? What everyone needs are answers, not just data. Any Data Monetization effort should be acutely aware of this.
Data Monetization effectiveness can be a proxy for overall Data Value Chain effectiveness. No more pure measure exists for how much value is created than the amount of dollars generated directly from the value generating activities. This by itself may tend to understate the truth, however, since directed efforts will also impact other areas. This is more true the further up the abstraction curve we go—advanced analysis is likely to have more wide-ranging implications than raw data alone.
The bottom line is that Data Monetization is important, but without putting it into the proper context we may find that these efforts end up too focused on one narrow aspect of the story, and that may compromise our effectiveness across the board. By recognizing that are no shortcuts to data success, we will find the opportunities to create the most Data Value, wherever those opportunities may be.
And until next time, go make an impact!
