Consider this, the data you collect about your products could become another profitable revenue stream. Ken Oestreich of Gigaom.com:
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.
Some industry examples:
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.
So how do you know if your data if valuable? Mr. Oestreich says you should consider the following:
- 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?
You can read the entire post here