Data is a very fascinating word. A few weeks ago, I was invited by Nina Dar to her Change Troubleshooter podcast to talk about data and the role of data in a modern business environment. We touched on a few very interesting topics about what data is, how it is created, what is good and bad data, and how to navigate a complex digital “data” world.
Back in the podcast, Nina asked me a really interesting question – what is good data, and how to differentiate between different pieces of data. The question might sound simple, but in fact, it is not. In my opinion, the question is not if all data is equal, but the question is what can I do with the data assets and how can I turn the data into something valuable.
One of my OpenBOM customers shared an article with me – There is no such thing as data by Benedict Evans.
I found the article very insightful because it directly connects to the point I discussed with Nina Dar a few weeks ago. Here is the thing – what we have are innumerable different collections of information, each of them specific to a particular application. Each of us, individually or collectively, owns a particular bit of data that can be collected in a variety of ways, but what is ultimately important is not data bytes and pieces, but what we can do with the data.
Here is my favorite passage from the article:
“Data is not the new oil. It is sand.” Data is valuable only in the aggregate of millions. Your electricity usage is not about other people, but it’s not valuable by itself, only in the aggregate of all domestic electricity usage in south London or Brooklyn, or wherever. And, again, data isn’t fungible — a power utility needs this data, but it’s no use to LinkedIn. … the value isn’t in the data at all but in the flow of activity around it — the meaning is not in the picture or video you post but in how the network reacts to it tomorrow. You could see TikTok or PageRank as vast “mechanical Turks” — we do not yet have AI that can understand what every page, picture or video are in themselves, and so we need humans, all of us, in the loop somewhere, at the right point of leverage, liking, linking, clicking and watching. These are systems, not data, and the value is in the flow.
The article made me think and expand my answer to the question Nina Dar asked me in the previous podcast episode – is all data equal. Here is my expanded take on the value of the data.
Data Sharing and Workflow
The foundation of a data-driven world is about data flow. In PLM and digital enterprise, the focus is on data handover or data workflow that helps to run business processes. As such, data flow allows us to create products, optimize manufacturing processes, organize sales, and, later, maintaintenance. The importance of data sharing and how the data can be used in multiple places is super high. The idea of data available across silos, companies, and globally is super powerful and will be explored in the future.
One of the most valuable things that can happen is when multiple data pieces can be connected to each other. This is how the value is created. Connecting information about products and suppliers, quality and performance of the components to the product they used, materials and regulation, you name it. This is a place where the most important things can happen. The biggest value of data connections is in the future of digital thread, allowing to connect of multiple pieces of data used by different people, organizations, and customers.
In the past, data lived in siloes that represented a business organization. Called “silos” on purpose, because they were a reflection of business and organizational boundaries. These silos exist in a single organization, but the same problem exists in the cross-organizational connections. The opportunity of business applications to aggregate the data and make it on a global scale will have a huge potential and impact on business systems and manufacturing businesses in the future.
What is my conclusion?
Data is an abstract thing and only can become valuable when it is involved in processes, sharing, and aggregation. It creates a way to turn such data into a resource that can be used for a specific process and provide a specific outcome. For example – risk assessment of the component is valuable data that can be obtained from a system (or multiple systems) by applying a method to extract it, aggregate, contextualize and apply for a specific use case. For example, a bill of materials (BOM) represents a piece of information that can be used contextually by manufacturing company departments, suppliers, and contractors to support more efficient business processes. Just my thoughts…
Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital cloud-native PLM platform that manages product data and connects manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.