Digital transformation and PLM data quality

Digital transformation and PLM data quality

Manufacturing companies are preparing to move into digital future. Digital transformation is one of the most hot trends. And as I’ve been thinking about this transformation one thing came to my mind – what digital transformation means for data in manufacturing companies? The reality of manufacturing companies is complex set of data and systems. All together, data is hold by multiple systems historically created by departments and functions. But this is not all. Modern manufacturing is moving towards even greater disintegration. To optimize cost, performance, global access to resources and supplies, manufacturing companies are optimized with a very specific set of functions. The tiers of contractors and suppliers are built to keep it going. And it creates another level of data complexity.

Earlier today, I read an article about data quality – The price of poor data quality. My attention was caught by an interesting phrase: Bad data is not better than no data. My favorite part of the article is about fragmented data. Here is a passage:

The crux of the problem is that as businesses grow, their business-critical data becomes fragmented. There is no big picture because it’s scattered across applications, including on premise applications. As all this change occurs, business-critical data becomes inconsistent, and no one knows which application has the most up-to-date information. It saps productivity and forces people to do a lot of manual work. The New York Times called this being a janitor. too much handcrafted work — what data scientists call data wrangling, data munging and data janitor work — is still required. Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.

It made me think about evolution of data and processes in manufacturing companies. Time ago, it was good enough to keep data in department to serve internal processes such as design and engineering. Manufacturing planning was a separate function, which was run by a separate set of systems. Sales and services was separate too. Maintenance is support usually handled by completely different set of systems. The data connection and handover between systems was important, but not so critical. As speed of manufacturing increased, global market  and complexity of supply chain created new realities of data. The fragmentation of data is a real problem for business decisions made by companies.

And now, as industry is moving to a digital transformation, the question of data fragmentation and data quality is becoming critical. On one side, manufacturing company cannot reborn in one day to improve the quality of the data. At the same time, one of the critical questions, PLM implementations are facing is a bad quality of the data. And the question I wanted to ask is how to measure data quality.

In my view, one of the biggest things that affects quality of data is duplication of data across multiple data sets. When it happens the opportunity is ripe for errors and duplicates. The first step toward successful integration is seeing where the data is and then combining that data in a way that’s consistent. Here it can be extremely worthwhile to invest in proven data quality and accuracy tools to help coordinate and sync information across databases. As part of digital transformation process to create a consistent layer of data representing data connection and updates can be extremely helpful.

One of the recent trends is to present PLM as a layer on top of existing data management systems. While this is a good strategy to prevent big bang of replacement, the question of data quality is the one not to miss. They way data is connected and synchronized will define to quality of overall data management system.

What is my conclusion? Digital transformation will require significant reassessment of data quality metrics. Old ways of taking data ‘under control’ might not be enough. The data is becoming more complex and interconnected. To provide an assessment of data quality should be on the top of minds for PLM data analysts in coming years. 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.


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