Every manufacturing company is sitting on the goldmine these days. I’m talking about the data that is owned by a company. Every company is producing a huge amount of data every day. Data is a critical asset in every company and it is well understood by everyone. Here are just a few examples.
According to Frost and Sulivan’s research, The world is creating 2.5 exabytes of data daily. Every organization needs to access that tidal wave of information to make better, faster decisions—or risks being left behind.
According to Markets and Markets research on big data. The global big data market size to grow from USD 138.9 billion in 2020 to USD 229.4 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period. The major growth factors of the big data market include the increasing awareness of the Internet of Things (IoT) devices among organizations, increasing availability of data across the organization to gain deeper insights to remain competitive, and increasing government investments in various regions for enhancing digital technologies.
The Bloomberg article says Nearly 90% of Organizations Say Success Depends on Data-Driven Decisions Made by Frontline Employees, According to New Report. At the same time. However, new research by Harvard Business Review Analytic Services, sponsored by ThoughtSpot shows only 7% of organizations are fully equipping their teams with the analytic tools and resources needed to drive decision-making and autonomy.
What are the sources of the data?
A manufacturing company has multiple sources of information. Unfortunately, many of them are not organized well. The foundation of the data about the product data, which has multiple siloed spaces such as design, engineering, production planning, procurement. Organizing product data can give a big boost to the data collection analysis as it provides a skeleton for data collection. The data is located in files, databases, and applications. The new sources of information are devices (especially connected devices), social information (eg. social networks), online data (eg. cost, suppliers, risks factors), customer data, maintenance information, and many others.
How to switch to data-driven processes?
While there are many sources of information, companies are usually experiencing substantial difficulties in organizing the information. To have a proper data management strategy, organize data silos, integrate information streams, how data is connected – these are just a few examples of complexity in data management every manufacturing company is experiencing. Add on top of this the need to collect and analyze the data, and you will see unmanageable tasks for many manufacturing companies and their ITs.
How to fix the problem? PLM systems with flexible data models and the ability to organize and absorb the information seamlessly have an opportunity to do so. It all starts from the product data – items, a single source for data records, multi-disciplinary product structures, lifecycle, and the ability to connect and expand the data as it grows (in both lifecycle and domain specifics).
Focus on the creation of a product data skeleton and then start connecting pieces of information together. I can see two challenges for many existing PLM systems to do so – (1) data model flexibility; (2) processing. The first just limits PLM systems most to the database to manage CAD file records. The second is the 20+ years old data architecture that has limited ability to process, index, analyze and make the data available in the right form and at the right time.
What is my conclusion?
A traditional approach was to have data analytics as a separate discipline collecting and analyzing data, then delivering results in a separate application using accessible and available to a small group of users in the company – so-called data analysts. While nothing is wrong with this approach. The opportunity is to deliver the data analytics to the end-users via the applications they are using every day. To make it happen, a new type of application and infrastructure is needed. Modern SaaS applications have access to the data and capabilities to organize seamless data collection and analytics processes to turn the data into actionable information in front of the users. The opportunity for SaaS PLM tools to collect, index, organize, and make analyses and to embed them into the usual everyday tasks – product cost, change management, design, procurement. Data is a new oil and processing the data can bring a big opportunity to PLM software providers. Just my thoughts…
Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital network-based platform that manages product data and connects manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.