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IIoT and data warehouses

IIoT Platforms Are Today's Data Warehouses

It’s amazing to do a Google search to find the 10 best IIoT (read Industrie 4.0, digitalization) platforms. Rapidly I came across ordered lists such as this one:

  1. Microsoft Azure IoT. ...
  2. Amazon Web Services or AWS. ...
  3. Google Cloud Platform. ...
  4. ThingWorx. ...
  5. Cisco IoT Cloud Connect. ...
  6. HP's Universal of Things. ...
  7. SAP Cloud Platform. ...
  8. Oracle Internet of Things.
  9. Bosch IoT Suite
  10. IBM Watson Internet of Things

There are others, but, interestingly, there’s little in the list – or for that matter, in the explanatory text – that tells you anything about what problems you can solve with these platforms. Most offer up aspirational ideas with little to tell you how to make them a reality. I remember one vendor (paraphrased) saying, “if we collect all the locomotive data we can save 1% on fuel cost…” (a big, big number follows). But, how do you do that? 

The overwhelming idea appears to be that if you have the platform you can solve any problem that needs digital data. Is that true? The platform vendors would have you believe so. But, realistically, isn’t the IIoT platform today’s incarnation of the data warehouse? 

It has been well-reported that more than half of all data warehouse projects failed. Data solutions expert Tim Mitchell writes: “Data warehouse projects are among the most visible and expensive initiatives an organization can undertake. Sadly, they are also among the most likely to fail. At one time, Gartner reported that more than 50 percent of data warehouses would fail to make it to user acceptance. Because of the size of investment (both time and money) required, the success of such a project can make or break careers.” He goes on to tell you why. You can read the full article, but here’s the hidden gem: “A surprising number of technical projects, including data warehouse initiatives, are undertaken without clear vision as to why they are needed.” Imagine that. 

I believe IIoT platforms are far more expensive than those data warehouses. So, are today’s IIoT platform decisions following the same IT-driven technology, multimillion-dollar errors of the past? It looks that way when I see many large established companies embarking on platform journeys without a clear understanding of a real, grounded business issue to be solved. 

Based on my career observations, including both successes and failures, my sincere advice is to understand the business problem to be solved – this is solved by an application, not a platform – and then put together the infrastructure to assure the application can solve the problem. Do it in such a way that your solution will scale and expand in functionality as needed for other applications to solve other problems. Mitchell writes magic, or maybe we are aligned on this. He says: “The most successful implementations I’ve seen have all involved incremental data warehouse development.”

So, find a big business problem and meticulously develop the requirements: more than, as Mitchell says, a punch list. Then you can find the right data persistence, data collection, aggregation, cleansing, whatever information you need to tackle your problem. With that problem focus, you can identify the appropriate product to analyze your data and present actionable information (or automate action) in the best way to bring scalable, supportable solutions quickly and easily. Do it with a product, not a service… you want robust solutions. 

Do not contribute to the 50 percent plus of IIoT projects likely to fail. 

Learn more about the keys to success in our executive brief, Digital Acceleration Opens a New Frontier of Value Creation.


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