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The Diminishing Return of Data

If companies and individuals want to avoid drowning in data while searching for a differentiator, they have to develop a smart data strategy that focuses on the distinct things that are core to their objectives.

By ·
By ·

Editor’s Note: Dave Kipe is the Senior-Vice President, Global Operations at Scholastic Inc.

Although I’ve never been a big fan of the descriptive analogy, “drinking from the firehose” (the visual image constructed in my mind borders on the disturbing); there’s no better way to describe data overload.

It’s no revelation that data is indispensable in the modern world. Irrespective of application or context, we now utilize data and perform analytics in every with every possible opportunity.

Motivated by success as well as survival, we’ve learned to embrace data.  Today, a company’s ability to compete is increasingly driven by how well it can leverage data, apply analytics and implement new technologies.  That being said, there exists the real issue of data overload and paralysis.

Although the growing surge of data in the digital age at our disposal is advantageous, could there also be a diminishing return?  Limitless terabytes of data, often reflecting customer experience as it happens, has 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 confusing.  The current period of hyper data 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.

(But) unmanaged, that complexity becomes a barrier to innovation and precludes our ability to derive meaningful insights and analysis.  Moreover, it becomes an obstruction to achieving the automation and efficiency we are seeking in the first place. 

To realize the full potential of information, organizations must develop data strategies, practice 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.

The indispensability of data is undebatable.  The question becomes how much is too much? When do you know? 
That’s the question many of us are asking ourselves in this new world of big data and even bigger ideas .  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 business.  Here’s a few questions to ask yourself or your organization.

Are we valuing quantity or quality?
Data hording is just as ugly as your grandmother hording Tupperware.  It should never be about collecting or compiling the most data possible; but rather the quality and efficacy of that data.  Don’t be left with a useless mountain 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 of 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. 

In the digital age, data isn’t hard to find.  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 that big data is revolutionizing the global market.  From supply chain, sales and customer experience to e-comm and marketing, the applications are limitless.  The key is optimizing this vast universe of data and tailoring it to each unique situation and purpose. If companies and individuals want to avoid drowning in data while searching for a differentiator, they have to develop a smart data strategy that focuses on the distinct things that are core to their objectives.


About the Author

Patrick Burnson, Executive Editor
Patrick Burnson is executive editor for Logistics Management and Supply Chain Management Review magazines and web sites. Patrick is a widely-published writer and editor who has spent most of his career covering international trade, global logistics, and supply chain management. He lives and works in San Francisco, providing readers with a Pacific Rim perspective on industry trends and forecasts. You can reach him directly at [email protected]

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From the September-October 2017
Additive manufacturing and 3D printing promise to simplify manufacturing, reduce inventories, and streamline operations. But, to determine when and how to apply additive manufacturing, organizations need a decision model that assesses it’s market strategy, supply chain performance, and complexity.
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