Big Data is Dead

In every industry across the globe, data is gathered, collected, sorted and stored. In the words of one of our recent clients “we have a data rich environment, but we are decision poor.” All of modern humanity is busy collecting “BIG DATA”. Manufacturing has been one if the biggest collectors of data. The Industrial Internet of Things (IIOT) and the Internet of Things (IOT) bring high levels of connectivity ensuring the big data problem evolves even faster.


The problem is we still live in a data segmented world. Users want and are struggling with decision making.  No one likes to do the hard work of analysis.  With “so much data, so little time” users still have deadlines for decisions right or wrong.  Making most users like Veruca Salt in my last blog. They want the systems to make the decision for them or point them in the right direction. Businesses, departments and people are hanging in the balance or worse. As a result, they use guessing and rules of thumb because they cannot see the decisions in the data.

 Don't mistake data for information

“ alone can’t solve the need.”

The solution to this problem is bridging the gaps and closing the feedback loops. Helping our businesses act more as an organism whose separate parts work together.  The true drivers to this solution are answering directional and operational questions. Questions where finance, health, safety, and operational data alone can’t solve the need. These solutions are available through connected value. Connected value is the ability to determine and balance product demands with operations and capacity. Understanding which products to retire and which products to introduce, which customers are stars and which are dogs and should go to competitors, combined with a clear and auditable understanding of the costs and marginal impacts of the scheduling decisions.   These are just some of the big decisions that drive business success, and it’s not big data.

Comments (3)

  1. Ken Templin:
    Aug 23, 2016 at 06:18 PM

    John: I agree completely with the comment that we are data rich and decision poor. And this is not for lack of trying. My 35 plus years in manufacturing taught me a lot and one of those key lessons was it is usually less costly to use the data you have to help in a decision than to approach it using another set of facts. Unfortunately, access to the data and the analytics needed to help in the decision was more times than not seemingly more difficult than coming at the decision from another view. The way to overcome this is to treat the already stored and available data as your only source and work across departments and specialties to help formulate the desired outcome. It can be "painful" (read frustrating, irritating, etc.) and time consuming to do but the end result is most times very rewarding. It not only helps you achieve what you set out to do but usually opens your eyes to multiple opportunities in other areas.

    Big data is what we have in abundance. Unfortunately it is not always in the right place and accessible to those who need it. Integrating (includes sharing) the data and using multiple analytical tools to apply it are within reach for all of us. I have preached integration long before it became a popular buzz word. It is how we avoid costly duplication and redundancies that have been all to common in most organizations.

  2. Julie Fraser:
    Aug 30, 2016 at 07:30 PM

    Well, there's no question manufacturers are data rich and decision poor. It's also clear that the data alone can't solve the need. Clearly, cross-disciplinary data access and collaboration is critical - these are process and culture issues.

    HOWEVER, I believe that big data is just coming into its own.

    Most manufacturers have no concept what big data is and don't have the tools and skills to take advantage of the big data tools and analytics now available. Unless you are a data scientist, you may not care. But you'd better hire a few, ASAP! These are the guys who can help you answer the big questions fast enough to satisfy the Veruca customers.

    Big data refers to taking disparate data and putting it in context. Big data analytics takes that and identifies patterns. Human beings CANNOT EVER process that volume of data in a meaningful way, no matter how much time you give them.

    I have seen the leaders figure out how to use big data in ways where they can predict and PREVENT problems and losses both in the production arena and for customers in the field.
    Big data processing and analytics can turn our traditional problem-solving approach on its head into: problem prevention! Think of what a different world we could have...

  3. John Jackiw:
    Sep 14, 2016 at 02:33 PM

    Ken thanks for your response and perspective in addressing this issue head on.
    For a long time, it was hard to see any other option but to bring in independent disconnected solutions that were hard to justify. This, as you clearly pointed out, moved us further to data specialties and Information turf wars.
    What I hear you saying is: This process starts with a manufacturing or a consolidated Center of Excellence(CoE). Doing this would avoid creation of multiple instances of the same data. If that is the case, establishing a CoE should be priority one. The Center can work with the data and information owners in your organization to consolidate and collaborate
    In the last five years, we have seen a surge in the availability of tools to extract transform and load information into common platforms. Often times, this can happen in the cloud so no new infrastructure is needed. As you indicated, if we use cross department and specialty information the yields are eye opening. This new information creates opportunities and insights.
    We all simply need to take a moment and look around us to see what we can share. In contributing to the cross department cause we create value.

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