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SmartData Collective > Big Data > Data Quality > What does Whale Vomit have in Common with Big Data and Analytics?
AnalyticsBig DataBusiness IntelligenceCollaborative DataData ManagementData QualityDecision ManagementExclusivePredictive Analytics

What does Whale Vomit have in Common with Big Data and Analytics?

Ray Major
Ray Major
6 Min Read
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You didn’t think it was possible, did you. Neither did I. Apparently inspiration comes from wherever it wants to…

Recently I had dinner in New Zealand with Doug Laney from  Gartner, Inc. where we had an engaging conversation about two topics that we are both passionate about, Data Monetization and Infonomics.

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You didn’t think it was possible, did you. Neither did I. Apparently inspiration comes from wherever it wants to…You didn’t think it was possible, did you. Neither did I. Apparently inspiration comes from wherever it wants to…

You didn’t think it was possible, did you. Neither did I. Apparently inspiration comes from wherever it wants to…

Recently I had dinner in New Zealand with Doug Laney from  Gartner, Inc. where we had an engaging conversation about two topics that we are both passionate about, Data Monetization and Infonomics.

At this point, you might ask, “What does that have to do with whale vomit?” Well, let me explain. Before he got there, I was scrolling through offbeat news articles as I often do, when I ran across one about a guy and his dog making a somewhat curious find. You can watch the complete story here, but allow me to summarize:

Ambergis

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Ken is walking on the beach with his dog, Madge. Madge discovers a large, smelly rock. Puzzled looks are exchanged. They walk on.
Back at home, Ken was just curious enough to google the strange thing Madge found.

To his eternal surprise and delight, Ken discovered that Madge may have found Ambergris, a by-product of sperm whale digestion traditionally used in perfumes. Ken and Madge hurried back to the beach and retrieved the object. After making a few calls, Ken determined that the hunk of whale vomit was worth about U$ 170,000.  

After feasting on some of New Zealand’s finest cuisine, I headed back to the hotel and I start to clearly see the correlation between Ken’s whale situation, Data Monetization and Infonomics. As I pondered the actors in this whale drama I started thinking about who did well and who didn’t. Clearly Ken did very well and I’d like to think Madge was rewarded for her efforts too.

Oh, but the whale… The whale, while simply going about the business of being a whale and thus requiring no extra effort on its part, produced something of no value to itself but of significant value to someone else. Because it had no idea that its by-product was of any value to anyone, it received no compensation.

It led me to the conclusion that many businesses are figurative whales. They create voluminous amounts of data as a by-product of just existing as a business. Some of the Data they collect is valuable to them but, in most cases, they are unaware that much of the Data that they collect might have significant value to others. It just sits there unused in the bowels of some centralized data center. Like the whale it’s akin to a waste product, and they receive no compensation for it.

What companies don’t fully realize is that their own “Data” exhaust fumes contain nuggets of information that other businesses can use to enhance their operations and, when packaged correctly, these businesses might be willing to pay handsomely for that information. Having someone pay you for your data exhaust is like the whale being paid for making smelly rocks. It’s a win-win situation.

Data is an asset, plain and simple. Let’s dig a little deeper using an example we can all relate to: ERP systems. We all know Data is a key component of an ERP. Moreover, we believe that better Data makes for a better return on our ERP investment. Therefore, we can and often do say: “If we had more and better Data, our ERP system would provide more benefits. Let’s call a meeting…”

This sentiment isn’t wrong, it’s just a bit narrow. Yes, by all means do better at what you know needs doing. But there’s a bigger play here. Start by asking some questions:

  • Who would want my Data? How does Data Quality and comprehensiveness affect the answer?
  • Why would they want it?
  • Beyond what I currently know, what else is knowable?
  • Sell Data, trade Data or both?
  • Can my Data help my supply chain serve me better?

The bigger play is called Infonomics. Infonomics is an emerging practice introduced by Gartner, Inc. that calls on companies to manage Data with the same discipline as any other corporate asset. In fact, adherents believe that Data should be treated as a new asset class with measurable economic value.

According to Doug Laney, Infonomics is the study of the way corporate Data is used to further the business — specifically, the best ways to use it. Laney conducted an informal study that showed 90 percent of surveyed executives’ list information as one of their most important competitive assets, but fewer than 10 percent of them actually quantify it. Gartner, Inc. estimates 30 percent of businesses will have begun monetizing their information assets via bartering or outright selling by 2016.

I’ll cover Infonomics and my theory of the six orders of Data Monetization in more detail in a future post. It’s a rich topic. Now I’m off to the beach in search of inspiration and U$ 170,000.

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