The biggest mistake companies make when monetizing data

“We have all this data! How can we monetize it?” is one of the most common questions we hear from clients today. Unfortunately, too often that simple question and the various initiatives that follow lead many companies to make costly and serious mistakes. 

There are many ways data monetization “goes bad” but possibly the biggest and most fundamental mistake senior management teams make is treating data monetization primarily as a technical challenge rather than what it is: an asset utilization issue.

Every week we work with companies that upon realizing they are sitting on a potentially (that’s an important word) valuable data set, proceed to invest large amounts of time and money before they had even established:

a)    what the potential revenue from any monetization might be;

b)    how they would generate this revenue (the business model);  

c)    the cost of doing so; and

d)    (worst of all) what the risks and complications are.

The solution is to view data monetization the same way you would approach any other investment issue. Imagine your COO wants to purchase a new production line. The obvious questions (how much will it make us, over what time period, what will it cost and what are the risks) would be asked and answered before you bought the line. Data monetization should be no different – you need to build the business case for investment before even starting the technical challenges of data extraction, cleansing etc.

The Other Side of the Coin – the IT Department’s Conundrum

Assuming you’ve worked out the cost of the data monetization initiative is from an IT perspective, the challenge now faced is justifying the cost because the revenue side of the equation (the value of the data) is often completely missing.  The conversation with CFO often goes this way “ok you’ve told me you need $500,000 to get the data into shape, but what will we make? What will the ROI be?”  [Cue blank looks.]

Trying to make a data monetization decision solely based on cost is like only having the denominator in an equation. If you want to make a business case to monetize your data, you also need to work out the numerator in the equation – that is, the likely economic value that can be generated from the data.  Understanding this will enable a true ROI (Return on Investment) to be calculated.

Imagine I told you the cost of monetising your data would be $500,000. This might sound expensive – until you find out that the expected value of that data monetization is $500,000,000 – in which case you’ll be happily signing that cheque.  On the flip side, if the expected revenue generated is only going to be $50,000, then the cost of monetization isn’t worth it. In short, it’s essential to understand both the costs and return on investment the data can potentially make.

So what’s my data worth? How do I value it?

One of the reasons that many companies aren’t doing this kind of basic cost/benefit or ROI analysis is due to a common misconception that you can’t accurately value data. This is incorrect. You absolutely can value data – it just requires a different methodology as it needs to take into account a much broader range of factors than are included in a traditional physical asset or business valuation.

For example, we recently worked with a major mall owner to determine the value of its data. The client had identified multiple data feeds from its malls, but it was struggling to understand what the data was worth and whether it was worthwhile to monetize it. The client’s accounting specialists had advised this was effectively impossible. We worked with the client to systematically identify 19-different end-use cases for the data and then used both qualitative and quantitative analysis to determine the value of the data based on the investment required, the likely payback, total value and risk adjusted returns.

The result: the project concluded that the value of the data was actually more valuable than the malls themselves and provided the client with a clear strategy of how to leverage, extract value and manage risk around its data.

Where to start

Many companies are sitting on potential highly valuable data sets (as well as other important intangible assets such as content, brands and software). However, these assets are frequently often off balance sheet, under-utilized and under-valued.

Management and boards have a legal obligation to generate a return on all assets, including data assets. Hence doing nothing is not an option.   

However to unlock value, companies need to recognise that while data has the potential to deliver significant economic returns, data monetization comes with significant costs – and risk. What seems like a highly valuable data lake can quickly become a quicksand pool of unexpected costs, lower returns and larger risks than expected.

It is essential that companies start the data monetization journey by building business cases that include an objective, evidence backed understanding of the potential economic value of the data to be employed via the specific business case; the potential costs involved and the potential risks to build an accurate Return on Investment Model.

This is no different to any other asset investment decision: developing land, buying equipment or monetizing data, the basic rules still apply. Although the methodologies will be different the core logic of Return on Investment analysis remains.

While data monetization holds enormous promise, it is important that management teams and Boards recognise the opportunity, but that they also don’t forget the fundamental basics when it comes to assessing the business case.