The Great Data Barrier

Could the NSA Hold the Key to Better Manufacturing Insights?

A few years back a classified (S//SI) NSA document was leaked that describes one of the most useful tools in the NSA arsenal titled the “The Rewards of Metadata”. FASCIA II was a huge database that enabled users to leverage Information Chaining to identify previously unknown threat targets by mining COMINT call event metadata. If you are not sure what metadata is, metadata is data about data. The NSA is so hungry for data that they collect data about data. This means that the calls themselves were not stored (at least in this database), but only the details about the call e.g., caller, callee, type of call, location of call, duration, etc. This data was used to locate al-Qaida Leader Khalid Sheik Mohammed and led to his capture in Karachi in 2003 with the same being true for many other world threat actors.

What if the NSA were brought in to help you run your manufacturing facility? Do you think they would ask for more data or less data? Do you believe you are collecting enough data to give meaningful insights? My guess is that we are collecting barely enough in most cases, and we could be collecting much, much more. Hypothesize with me further, do you think that they would say that you have the right tools in place today to make use of this data? I further surmise the answer is a resounding “no.” Why is this? Do you believe the NSA could create a threat monitoring solution to give you better insights into your manufacturing systems? Almost all manufacturing organizations could benefit from a focused data capturing and data mining program to uncover threats to your manufacturing systems or unknown opportunities hidden within plain sight.

I spent my formative years working in the intelligence community and I have seen firsthand the power of data. Being the tip of spear myself, I also know the great hurdles that our government navigates to capture and report intelligence data. If only we had some of this diligence within manufacturing.

Data Diligence

So, why don’t we have this data diligence within our manufacturing plants? Disk space is cheap. The incremental cost to add 10,000 more tags to a historian isn’t going to disrupt the finances of a plant. In fact, I can get you 10,000 tags stored in the cloud leveraging AVEVA InSight for less than $7,000 per year. You don’t have to worry about disk space, servers, increased IT spend… worry free data for 70 cents a tag per year. I might say we are penny wise and pound foolish but, in most cases, we are just not confident that we know how to use the data once we have it and consequently don’t ask for the money. I believe this is the root cause of the great data barrier in manufacturing.

So, even though we can do much better in data collection, and the data that is available is largely underleveraged and not well democratized, most organizations already have enough data to start benefiting from better analytical tools and even machine learning/AI. It is also important to note that even if you don’t have enough data, with IoT, low-cost hardware, and cloud infrastructure, there are low-cost pathways to start collecting the right data for your organization.

Another challenge for getting value from your data is that the data is siloed and compounding the issue there is a lag time between those siloed data sources. An example of this is the difference between a Historian which collects data in real-time and an ERP system that collects data in a transactional nature. Another example of time lag is the difference between when a Finished Good is made and when offline quality lab result data is made available.

Digital Twins to the Rescue?

There is much talk about digital twins and manufacturers would love to build a digital twin of their manufacturing process. A digital twin would enable them to run manufacturing scenarios and identify hidden opportunities. One example of hidden opportunities could be understanding the effect of humidity on a particular supplier’s raw material and your finished product produced on a specific line at night by a specific operator during the fall. To build such a complex digital twin of your manufacturing, you will need to solve the data siloes and time lag issues. Just as the NSA uses Information Chaining to draw conclusions, you need to interconnect your siloed data with the full understanding of the data’s connection to time within your process.

Check out Braincube

I admit this is a formidable challenge, but I wouldn’t be writing this blog, if I didn’t have answers and believe we can solve these challenges. We have recently partnered with Braincube to bring this NSA like power to your organization and finally give you a chance to extract that hidden value from your data.  Or watch our Webcast on the Topic:  BrainCube Turn Data into a Competitive Advantage

The InSource team would love to discuss with you and your team how we can bring the power of the digital twin to your company.