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Drowning In Data

Every so often…well very often, I catch myself counting with my fingers. I’ve never been a “numbers” guy (which probably explains that embarrassingly low math score on the SAT I took in 1990). So ironically, I entered a profession, supply chain, which utilizes data and analytics in every capacity and form possible. Out of necessity (and self-preservation), I’ve come to embrace data and the realization that to be successful, in almost any profession, one must comprehend and apply the wealth of data and info available, or be left behind. All that being said, there exists the real issue of data overload (which can result in analysis paralysis which is an entirely separate discussion).

Although the growing tsunami of data in the digital age at our disposal is advantageous, it also is accompanied by pitfalls. Limitless terabytes of data, often reflecting customer experience as it happens, have the potential to remake business models, regardless of industry or geography.   These models can and should be more efficient, productive, flexible and responsive. But in the wrong context, or utilized ineffectively, it can be extremely messy. The current period of hyperdata growth leaves most companies in a position where their ability to uncover business insights is effectively hidden within an increasingly complex and often unfathomable amount of data.

Unmanaged, that complexity becomes a barrier to innovation and precludes our ability to derive meaningful insights and analysis. Moreover, it becomes a barrier to achieving the automation and efficiency we are seeking in the first place.   To realize the full potential of information, companies must develop data strategies, and better data management discipline, and start asking better questions. Organizations must act quickly to take control of data growth, complexity, and chaos. That includes focusing, simplifying and standardizing data analysis through an enterprise data management strategy, and exploring the range of possibilities afforded by machine learning, IoT, and blockchain.

In the context of the supply chain; the question becomes how much is too much? When do you know?

That’s the question many of us supply chain leaders are asking ourselves in this new world of big data. In a time when new information is rapidly becoming easily accessible, it’s now possible to have more details than ever about your suppliers, customers, and even your own operations. Here’s a few questions to ask yourself or your organization…

Are we valuing quantity or quality?

It should never be about collecting or compiling the most data possible, but the quality and efficacy of that data. Don’t’ be left with a useless big pile of data. Make sure what you’re collecting actually adds value and insight into your key business strategy. More data doesn’t automatically mean better decision making. It can actually have the opposite effect.

Do we have a centralized data depository?

Void of a centralized, harmonized, hub to capture and store this massive collection of data, you will most likely mismanage it. Information needs to be accessible and quickly applied to be used in anything from demand planning to customer modeling.   It can’t be spread out among multiple systems that do not “talk” to one another. Falling into the trap of having to manually merge data from multiple sources is overly time-consuming and dramatically increases the chance for error. That’s the last thing you need when attempting to use that data when making a critical business decision.

How fast are we analyzing new data?

Not all of the data you will collect is useful, and its “cleanliness” may vary. The biggest companies in the world are increasingly harmonizing upwards a dozen or more different data feeds (the average is seven). The machines we have invented to produce, manipulate and disseminate data generate information much faster than we can process it. It is apparent that an abundance of information, instead of better enabling a person to do their job, threatens to engulf or diminish their effectiveness.

It doesn’t take a world-class IT department to find data. It’s rather easy to retrieve. It’s how you correlate it and how you put the pieces together; how you understand the context of the data, and how you relate it to what problem you’re solving. Even with a plethora of high-quality data, you’re still going to need to understand what it’s telling you. During that investigative diligence process, you will ultimately identify the decision making element and act upon it.

There’s no doubt big data is revolutionizing supply chain, marketing, commerce, and just about everything else. It’s been a buzz word in the industry for years and the direct and indirect benefits are limitless. However, until Watson figures out how to optimize the vast universe of data and tailor it to each unique situation and purpose; it’s critical that we all recognize and manage the benefits and pitfalls of the big data revolution.

Dave Kipe
Dave Kipe
DAVE is currently the Senior Vice-President of Global Operations at Scholastic, Inc based in New York, NY. A dynamic and transformative senior leader, Dave has over 20 years experience leading global supply chain, distribution, finance, E-Comm, and IT operations across various industries, platforms, and sizes. From private equity backed start-ups to Fortune 500 organizations, Dave has built and led teams to unprecedented and sustained success in operations, technology, and financial execution. Dave employs a hands-on approach building a culture of employee engagement while delivering exceptional bottom line results.

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2 CONVERSATIONS

  1. I see data analysis often misused. Let’s say 9 out of 10 doctors say we should eat carrots. Now, how did that 9 out of 10 come up? It could be because 9,000 out of 10,000 say we should eat carrots. Or it could be from a small sample of 10 taken out of 50,000, and 9 out of that 10 says we should eat root vegetables. Since a carrot is a root vegetable 9 out of 10 doctors say we should carrots.

    The problem with using data is what drives the narrative we’re using data for. Is it cherry picked to support our beliefs, our position; OR is it used in the correct context to challenge our beliefs and motivate change to the right way.

    In a world of identity politics, cherry picked data is driving corporate and governance change; change that is responding to things that aren’t even happening. A few months ago Ontario in Canada passed Bill C-16. http://torontoist.com/2016/12/are-jordan-petersons-claims-about-bill-c-16-correct/

    Today in Canada government, motion M103 is being discussed.
    http://www.cbc.ca/news/politics/m103-islamophobia-khalid-motion-1.3972194

    These two are recent examples of data cherry picking. I can give a few more examples from HR policies and corporate training I’ve seen. But in doing so, I would get into a lot of legal trouble.

    • Chris, you make some great points. “What” drives the narrative is a very important question. What’s the agenda behind the analysis? Is it an exercise in sincere diligence or is it “cherry picked” to support an already established position? Your examples are a microcosm of a bigger issue that are prevalent from politics to economics. Data is powerful, but can be easily manipulated or misinterpreted (either implicitly or explicitly).

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