When thinking about the value of the data a company collects vs. the traditional value of the product it may produce, collecting and analyzing broad categories of customer + product data is becoming equally — if not more — valuable than the product itself.
And, if the data is becoming so valuable, then analyzing and mining it ought to provide incremental revenue streams beyond the traditional product-based business model. But consider going one step further: If treated right, access to enough quality data would be valuable to others outside of your enterprise too – assuming the correct federation and business models were constructed.
This accretion of value around large data sets – particularly alongside an external ecosystem – is analogous to what we’re familiar with in the product world: The Platform. Indeed, we may find that entirely new business models based on data platforms may arise from legacy product companies.
What Constitutes a Platform?
In the traditional world, the platform is a piece of core technology and/or IP that third-parties write to or build upon, frequently using APIs. The platform helps form a market with value and inertia that attracts third-parties to provide complementary solutions. In turn, the ecosystem of third-party products is made more valuable because they are associated with a platform.
In the new analogy, core data can become a platform. If there is sufficient size and uniqueness, if it’s useful enough to others, and if there are appropriate (legal, technical) methods to exchange/federate it with other data sources, it becomes the accretion point for even more new data, services and products.
The net-net is that amassing and exposing vast amounts of unique data to third-party ecosystem partners can effectively create Data-as-a-Platform.
Some business model examples
To illustrate what I’ve seen so far, here are a few snippets:
- An academic textbook renter/distributor is aggregating knowledge about which textbooks are being used by which students at which schools — much the way a commodity exchange monitors prices, demand, and volume. This company is capturing more data about this than any single publisher or distributor could. And, it is essentially disintermediating such traditional players. The value here is to understand the needs of students, academic instructors, plus demand for specific authors and content. Clearly money is to be made from the core business, but the company’s extended value may not be as much its online platform as it is the data it amasses about content, demand, and pricing. This data would be useful to anyone seeking to create derivative content products for students.
- The New York Stock Exchange has amassed a data mart of every stock market tick dating back years, and has located it within a special-purpose compute cloud. This allows would-be computer trading algorithms (from third-parties) to trial-run effectiveness before going live on-line. NYSE makes money from access to the data, from use of its cloud, and from the additional trading transactions it encourages. Such fast and complete access to historic data is immeasurably valuable for trading analytics, and creates a data gravity effect of attracting even more third parties to the combined data and compute platform.
- Grocery and/or consumer retailers keep massive records on purchasing habits of customers, overtly for affinity programs and for making targeted offers. However, this data could be federated with other complementary retailers to establish (geographic, temporal, or correlative) purchasing patterns to increase overall sales per customer. For example, Neilson recently signed an agreement with Walmart where Neilson gets to perform point-of-sale analysis with Walmart data, and (presumably) cross-correlate it with other retailers.
- Real estate and geographic data such as from Zillow or Factual can provide core accretion of data value for complementary data-based services. Indeed, such data is available to be crossed and mashed-up for use in healthcare, local government, retail marketing/sales, leisure services, and much more. Consider the value for developing assessing detailed demographics, localized services, etc. However, these business models, both with Zillow and Factual, are of pure data services, rather than a derivative from a “legacy” core business.
Where to start?
Does Data as a Platform beginning to sound interesting? It should. Plus, with the cost of data storage plummeting coupled with the onset of Big Data analytics, most enterprises should begin considering developing new IP – in the form of data.
To get your juices flowing about creating a data platform business of your own, consider the following data attributes you may already be developing:
- Uniqueness – is your data unique to you, and therefore hard to replicate by others?
- Size/completeness – how physically large is your data? Does it reach back temporally? Does it include deep details and/or trends? To whom would this be valuable, either pre- or post-analysis?
- Desirability – in addition to uniqueness, would your data be useful/desirable by others in adjacent spaces? How marketable is it?
- Complementarity – consider how valuable your data is to complement or complete other forms of data, or to build-upon other data sets in a mash-up fashion?
- Statistical/correlative relevance – can your data be statistically analyzed for patterns? Are there correlations or patters that might be drawn between it, and other external data sets?
- Long tail relevance – does your data contain elements that lie +/-3 sigma outside the norm? could this “fringe” data be valuable to incrementally increasing sales, or for addressing customer needs outside the core set?
- Data gravity – this new term, coined by Dave McCrory, speaks to the physical immobility of data, and the tendency/requirement to co-locate other applications and data sets with it. Is your data potentially so large that it might actually attract actual applications onto the platform? Perhaps within a special purpose compute-cloud?
In closing, the coming age of big data isn’t just about storing and analyzing lots of bits. It’s about extending the core business models to leverage IP stored-up in your data, and creating new partner ecosystems – and data supply chains – to create even more value for your enterprise. That’s just where the fun starts.
Ken Oestreich is a marketing veteran helping develop new categories in the Enterprise IT, Data Center, and cloud computing spaces. He has held product- and corporate marketing roles with Sun, Cassatt, Egenera, and EMC. Find him on twitter as @fountnhead.