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Data Is The New Sugar

A Snarketing post by Ron Shevlin, Director of Research at Cornerstone Advisors

I recently attended a large technology vendor’s summit on analytics and Big Data, and heard one of its senior executives say:

“Data is the new oil, the next new natural resource.”

My first reaction to the statement was “Oh yeah? Then why is your firm buying up the technology providers in the space, and not investing in the data itself, if it’s that valuable?”

In hindsight, it was a stupid question, because the answer is obvious: The money is in refinement, not the commodity itself.

(If you don’t believe me, take a look at the stock market. Exxon Mobil, Chevron, and BP all trade at lower multiples of revenue than the Halliburtons, Baker Hughes, and Sclumbergers of the world.)

In the midst of the current obsession with Big Data, comparing data to oil, and calling it a “natural resource,” is bound to get  you some attention.

It’s definitely a pithy quote. But it’s the wrong analogy.

My take: Data is the new sugar.

Sugar sure tastes good, doesn’t it? Put a little on your cereal,  in your coffee, put it into a cake, yep, sugar pretty much makes anything taste better.

Same thing with data. Have a tough management decision to make? A little bit of data — when you have none to start with — can sure help you make a better decision.

But too much sugar isn’t a good thing. It makes you fat. It’s hard to digest. There’s a limit to how much sugar you can put in your coffee or in a cake before it doesn’t taste so good. 

It’s the same with data. Too much data leads to decision paralysis. Too much data is hard to process (refine) and incorporate into the management decisions you have to make. And while there aren’t that many different kinds of sugar out there (or are there?), data isn’t so homogeneous, so mixing data makes it hard for you determine which data elements you’re using is contributing the most to making quality decisions.

All of this is an attempt to inject reality into the discussion about Big Data. As I half-joked in an earlier blog post, “Every example of a firm using data is not an example of Big Data.” 

And data is anything but a natural resource — i.e., something that exists in nature, and is there for the picking, extracting, and/or gathering.

The newly emerging data scientists out there would do well to think of data as the new sugar, not the new oil.

Ron ShevlinRon Shevlin is Director of Research at Cornerstone Advisors. Get a copy of his best-selling book, Smarter Bank: Why Money Management is More Important Than Money Movement. And don't forget to follow him on Twitter at @rshevlin.

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  1. It might be sugar for now, but it should be salt! Sugar hides the flavor of the food it is added to (like you said), but salt brings forth the true qualities of that to which it is added. Just be careful not to mistake your salt for sugar… I accidentally added salt to my father’s coffee as a kid. The two have been on separate sides of the kitchen ever since!

  2. Ron,, I agree with Jennifer, though for a laugh perhaps MSG would be a funny alternative to salt or sugar to keep on the flavor-enhancing thread!

  3. Dan Bendt says:

    This analogy leaves me with the feeling that data is just an extra additive to make decisions better. Shouldn’t data be of core importance? After all, data is fact, and even though it can be interpreted subjectively, is pretty much the closest thing we have to truth.

  4. If there’s another analogy you like better, I’m all ears. Was testing the waters with the sugar analogy, and mostly trying to discredit the oil analogy. The notion of “core importance” is tricky, though. Both oil and sugar pass that test. You can’t run the car without oil. You can’t make plenty of recipes without sugar. But having just oil doesn’t make the car run, either. So now we’re stuck trying to determine which elements are more important –i.e., of core importance — than others?

  5. Good stuff, Ron. I love analogies but they are tough to get right (not to mention spell). I agree that data is an ingredient in the management/marketing/sales mix. The trick is to definitely know which data to refine and which data to use in any given situation. How I wish I was born in the day when businesses were run without refined data just instincts…but then cars and computers and power tools (just a few of my favorite things) didn’t exist then either.

  6. JC: You wrote: “The trick is to definitely know which data to refine and which data to use in any given situation.” BINGO. This is the issue I have with the whole Big Data Deception. People talk about it as if you can just throw a ton of data at a decision, and Poof! the right answer will pop out. Ironically, the value of good analysis is often in figuring what data is useful and what isn’t. So good analytics is constantly at war with Big Data.

  7. Damn straight! Not to talk myself up, but I think that is why I took to data-driven marketing. My background as a journalist causes me to stop and figure out what questions I want answers to and where I will find the best answers. However, that is not the end of the process. Once I get the answers I check and see if I can corroborate the answer with another source. If I can’t I then look at the answer I have as a hypothesis and conduct tests to see if the results support the answer the data gave me. Any one selling a simpler solution is selling…well…snake oil.

  8. Part of the decision making process is knowing when enough is enough. This applies to managing data as well. I think that we can agree that data-based decisions are virtually impossible without the benefit of data. Thus, we should be able to agree that data is necessary to make risk-adjusted decisions.

    Needless to say, data by itself is necessary, but not sufficient in the process. Extraction of implications resulting from analysis is critical — but how is this different from what every business is doing today (or should be doing)?

    The big difference today vs 20 years ago is that we now have much more data, and we now have tools to gather and analyze that data. Net / Net – this is a huge benefit for businesses that decide to accept the challenge.

    There is certainly danger with “Big Data” in the form of a) expertise, and b) paralysis by analysis. But this is where experience and knowledge should insert itself to balance the “recipe”.

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