Industry Week, MIT and MarkLogic: How to Close the Data Gap
Posted by Matt Turner on 27 November 2019 12:54 PM
What’s holding manufacturers back from reaching their goals of applying new technologies and approaches of Industry 4.0? In our recent webcast hosted by Industry Week, “Industrialize Your Data to Deliver Industry 4.0,” I had a chance to discuss this with John Carrier who has been looking at the role of data in process engineering in his work at MIT’s Sloan School of Management.
John laid out the case for data as the critical component in the important feedback loop between the model and the system. When you design a system, the model describes as much of the end-system as possible. But once it is put into action—for instance, the actual building of a refinery or the creation of a production line—the actual performance of that system needs to be measured against the model.
This feedback loop is data. An example John gave of the value of this feedback loop included the reduction of energy usage by canal houses in Amsterdam that had their meters visible on the ground floor.
However, in today’s environments, the data you need to complete the feedback loop is not always available. Often, the system data is actually produced, but it isn’t in a state to be understood or acted upon. This is because not only is there a growth in the volume of data produced, the data is also more complex and from more sources.
John talked about the importance of organizations focusing on being able to find “the right data.” This is harder than ever with the growing volumes of data and the variety of data being produced by today’s systems. John said that tackling this hard problem of getting to the right data will enable OODA (observe the situation, orient the information and then decide and act).
Assessing Data Challenges
To get a sense of where our audience for the webcast was in the conversation about these challenges with data, we conducted two polls during the discussion. In the first poll, we asked, “How important is data to your organization?” Fully 43% of the respondents answered, “Data is a topline, strategic priority” and another 35% said, “Data is important for divisional and operational goals.” Since the webinar was titled “Industrialize Your Data,” this may not have been a surprise that attendees were focused on data. But the confirmation of the investment in data was good news for John’s work and for the overall data conversation in industry.
However, when we asked, “How well does your company deliver value from data?” only 11% answered that they “were excellent at delivering value from data.” And, 38% said they were “OK at delivering value” while 36% said they were “not good at delivering value from data.”
These results were not a surprise for John and me. John had already pointed out the difficulties with getting the right data to close the feedback loop, and in my part of the talk, I discussed how traditional data management falls short of delivering what is needed to tackle this complex data.
Getting to the Right Data
What can organizations do to close this gap? In my part of the webcast, I shared how manufacturing leaders like AutoLiv, Airbus and Chevron are “industrializing their data,” which means investing in data, treating data as a first-class asset and making that data universally accessible. To overcome challenges with getting to the “right data,” these organizations are all using the MarkLogic Data Hub pattern.
By loading data as-is from source systems and harmonizing the data in the database, these organizations are bringing together a great variety of data being generated in their facilities. And, critical to making it universally available, the data hub securely preserves the data from the source systems, allowing them to know where the data came from and how it can be used. You can learn more about how a MarkLogic Data Hub can help you industrialize your data here.
I learned a great deal from my conversation with John and just how critical it is to close the feedback loop. To find out how you can bridge the gap between your goals for data and the difficulties of delivering that value, check out the full webcast here.
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