Stop Assuming Your Data Will Bring You Riches
September 23, 2013 Editor 0
“We have a treasure trove of data, it’s highly valuable”.
“If we can unlock the value of all our data, we will have a wholly new revenue stream”.
“Hedge Funds will love our data — they will practically buy any set of data that might give them a potential edge”.
These are just a sample of thoughts from clients in recent months who were excited about the prospect of creating new businesses and new sources of revenue in what seems to be a lucrative new area. Big Data. Analytics. Content Innovation.
In some cases, they’re right. But then again, in a surprising number of cases, they aren’t. The opportunity, which seemed immense on the outset, turned out to be disappointingly smaller after a thorough evaluation. Fortunately, the organizations that took the effort to truly understand the value of their data were able to execute appropriately. Very importantly, they were able to avoid costly technology and implementation programs that would have surely fell short of usage and revenue expectations.
Here are four steps your organization can take in order to understand the value of your data, and to plan for potential monetization:
Clarify whether it’s really your data
Sounds obvious, doesn’t it? Unfortunately, this may be the most common mistake that organizations make. They assume that if they collect the data, and house it in their systems, it must be their data. Or if they collect data and then add a proprietary methodology to it, that automatically qualifies it as proprietary data. Or if they create analytics from raw, underlying data, and subsequently barter the analytics, they must be able to charge hard dollars at a later stage. All of these assumptions may be true…but are more likely false. Unless you have written data contracts in place that clearly allocate ownership of data and derivative works, you may not be able to do anything with the data.
- To evaluate data ownership, enlist the early help of domain experts — content specialists and legal counsel who understand how data is created, stored, manipulated, packaged, distributed, and commercialized.
- Categorize the datasets you have identified into three buckets: “data we own”, “data customers own”, and “data third parties own” to ensure added clarity.
- Quantify (as much as possible) the value-add of any derived data versus the original data, in order to be in a better position to create mutually agreeable data usage and revenue share agreements with suppliers and co-creators of the data.
Understand who would value it, why, and how much
This is easier said than done. In speaking with over a hundred end-users of data over the past year (in Financial Services, Healthcare, Technology and even Nonprofits), I have come to realize that your target customer may not be your traditional customer. For example, a product evaluating nonprofit organizations may be highly useful to a wealth manager seeking to help his client to select a charity as part of a value-added tax efficiency service. Or a healthcare data offering evaluating physicians’ perceptions on a new drug may be useful not only to brand managers at pharmaceutical companies, but also to portfolio managers at asset management firms seeking to find promising investment opportunities.
- Identify target customers by casting a wide net across potential users, and performing customer interviews to establish their “jobs to be done” (as Harvard Business School professor Clay Christensen says). Determine where their current data gaps are, and evaluate if your datasets can fill those gaps, either as raw data or in a more processed, analytical format.
- Test user perceptions with a range of potential data offerings. End-users vary in their level of sophistication of data usage, and some may immediately see the value of the raw data, whereas others may want to be given visual examples of the value the data can bring. As one client once told me, “It’s as if I were at a farmers’ market with all the most amazing fruits and vegetables, and I can think of a hundred recipes that I would love to prepare.” It’s clear that he saw the potential of the raw data. Others may not wish to (or be able to) be the chef, and prefer to be the diner at the restaurant, where they have a menu of dishes that they would rather choose from. Both are eventual consumers of the raw materials, and both should be served!
- Ask prospective customers to assign a gut-check ranking — ”High”, “Medium” and “Low” — to the individual datasets and metrics and note these preferences respectively in green, orange and yellow. End-users’ initial reactions are generally quite pragmatic and representative of their overall assessment of value, utility, and willingness-to-pay. As the customer interviews progress, the color-coded matrix will come to life, and can help prioritize where the opportunities truly lie.
Frame up realistic aspirations for monetization
At this point, many companies get to work and prepare detailed financial projections that show how many new sales they can achieve every year, what their proposed pricing is, what the year-on-year percentage increase will be. Sometimes that makes sense. And sometimes it backfires – what if the monetization potential is not really that big? How do you ensure that your execution is commensurate with the revenue opportunity?
- Set yourself a target that is big enough for you to pursue and that you feel is worth your organization’s time and effort. The innovation consulting firm Innosight often refers to “$50 million in 5 years” as a reasonable target for a brand new business. For you, it may be different.
- Ask yourself “What would it take to get to that target?” Data can be commercialized in a number of ways: via annual subscriptions, via once-off consulting and integration fees, via custom content development, via research and advisory services, and via new analytics development. Ask yourself how many of these are truly viable. Consider comparable offerings from competition and their pricing structure. In many cases, you may come to an epiphany that your target is just not reasonable.
- Understand whether the data provides more value than just the new standalone revenue. You may come to the conclusion that your original $100 million target was unrealistic, but $10 million is achievable. Do you decide to stop? Well, it depends. Sometimes the revenue may be small in absolute terms, but the data capability may be complementary to your current, core business. The value may be in the combination of the two, which can drive significantly higher core business revenues.
Test, learn, and tweak
Now that you have a realistic revenue aspiration and have decided to continue pursuing this opportunity, you turn to execution mode. If you are sure about the opportunity in front of you and your ability to execute, this may work. However, if you are new to the data and analytics game, and are not sure whether you can be successful due to a multitude of ambiguities, you may need a different approach.
- Highlight the areas in which you think you may fail, and create test programs to evaluate your ability to execute. Do you have the ability to provide real-time data 24×7? Will customers really pay what they alluded to in the interviews? Will your data distribution partner really be motivated to work with you, or will they have other priorities? These are all crucial parameters that will determine whether your data can really be commercialized. They need to be addressed, and solved before launching a business in earnest!
- Create tangible success criteria that will allow you to either determine whether you can solve the problem, or whether you can learn something that will help you make a go/no-go decision. For example, a test could be “validate subscription business model via direct sales by securing three signed customer contracts within three months”, or “create dashboard of [specific number and type of] metrics which 100 end-users test, validate and give suggestions for improvement, within three months.”
- Implement the test programs with defined roles and responsibilities for the Test Program Owner, as well as the execution team. Ideally, the team should be small, 100% dedicated to the pilots, and cherry picked for their domain knowledge in content, as well as their ability to work in an agile, entrepreneurial environment.
The results of the test programs can help get you to a more informed view on whether you go ahead with implementation, whether you stop, or whether you need to make some modifications to your business model and/or execution.
In following this overall process above, you can clarify what data you own, and how valuable it is (and to whom). You can frame up realistic aspirations for monetizing your data, and you can prove your right to succeed by testing (and overcoming) areas of potential failure. You can therefore move from an unsubstantiated assumption about the value of your data, to a more informed understanding of its worth in terms of its use to current and prospective customers, its standalone commercialization potential, and its potential to enhance your current business.
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