PLM companies are in full swing adopting IoT technologies and marketing language. And one of the most of important element of IoT technologies is data. Yes, the data produced by connected products, absorbed, collected, classified and capable to produce a desired insight everyone is looking for in manufacturing industry. It can be related to bad supplier or component, suboptimal maintenance or specific product usage pattern.
PTC article – The Data Dilemma raises a question how to get right insight or just find a right data buried inside gigantic data storage.
Buried deep inside data warehouses and Big Data clouds are some heavy questions and assumptions about the future of the Internet of Things. Aging data inside, and metadata about IoT devices has its own, unseen built-in bias. Recognizing those flaws is a hot topic, getting its 15 minutes of fame as researchers and computer scientists caution us on the dangers of becoming too reliant on data. The book “Weapons of Math Destruction” by data scientist Carol O’Neil warns that algorithms decide what we see – or don’t see –on the Internet, and users often don’t control their IoT equipment and logs with location, intention, and preferences. She [ suggests that having more data doesn’t ensure better IoT analytics. There still needs to be a human to ask challenging questions, identify different data sources to shore up your information, and refine it into knowledge.
The article doesn’t give you an answer. But it made me think about indexing and ontologies. For the last decade, companies became obsessed with indexing. Five years ago, nobody asked to index data. Today, everyone is trying to index data. But the biggest problem is not how to index data, but actually an opposite. How to extract data back. The challenge which manufacturing companies encountered over the last 30-40 years is that data flowing into a company often changes rapidly over the time. And, what even worst, at any one point in time, information is not available. Manufacturing companies sucks in what is needed and hope that data will become findable and useful for other system. And this is why ontologies are important.
If you haven’t heard about ontologies, read the following article – Why Ontologies? An ontology is fancy way to say that software or user can create a classification system and use these terms to index data. Sounds like a very good idea. However, the reality is much more complex and there is a gap between ontologies and an ability to get results. And this is a killer for most of search technologies. Ontology or whatever else technology is presented as a next big thing to get data from IoT devices, enterprise data silos or just bunch of old PLM databases. People are dissatisfied with search and to interpreter the data in a right way is becoming an urgent problem. Ontology: Practical application article gives you an idea how important are ontologies: Here is my favorite passage:
“Correctly interpreting user signals enables the system to present the right content for the user’s context, and requires not only that our customer data is clean, properly structured, and integrated across multiple systems and processes but also that the system understand the relationship between the user, his or her specific task, the product, and the content needed—all assembled dynamically in real time. Building these structures and relationships and harmonizing the architecture across the various back-end platforms and front-end systems results in an enterprise ontology that enables a personalized, omnichannel experience. Some might call this an enterprise information architecture; however, there is more to it than the data structures. Recall that the definition of an ontology includes real-world logic and relationships. The ontology can contain knowledge about processes, customer needs, and content relationships.”
Ontologies are a critical component of the enterprise information architecture. Organizations must be capable of rapidly gathering and interpreting data that provides them with insights, which in turn will give their organization an operational advantage. This is accomplished by developing ontologies that conceptualize the domain clearly, and allows transfer of knowledge between systems.
What is my conclusion? The complexity of data is growing and this is what clear for everyone dealing with manufacturing, data and IoT. Manufacturing companies are representing a fortress of data. But to get data out is hard. To get an insight on the data is even harder. PLM IoT, big data, machine learning and other technologies are promising to solve a problem. However, how realistic and reliable are ontologies and other models of information extraction? We don’t know that answer. The reality of data is different from a theoretical point of view. What helps to get data and understand a problem is know hows and practical methods of data extraction. Just my thoughts…
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Disclaimer: I’m co-founder and CEO of openBoM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.