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How to visualize future PLM data?

by Oleg on August 12, 2014 · 0 comments

collective experience of empathetic data systems

I have a special passion for data and data visualization. We do it every day in our life. Simple data, complex data, fast data, contextual data… These days, we are surrounded by data as never before. Think about typical engineer 50-60 years ago. Blueprints, some physical models… Not much information. Nowadays the situation is completely different. Multiple design and engineering data, historical data about product use, history of design revisions, social information, data about how product is performing coming in real time from sensors, etc. Our ability to discover and use data becomes very important.

The ways we present data for decision making can influence a lot and change our ability to design in context of right data. To present data for engineers and designers these days can become as important as presenting right information to airplane pilots before. Five years ago, I posted about Visual Search Engines on 3D perspective blog. I found the article is still alive. Navigate your browser here to have a read. What I liked in the idea of visual search is to present information in the way people can easy understand.

Few days ago, my attention was caught by TechCrunch article  about Collective Experience of Empathetic Data Systems (CEEDS) project developed in Europe.

[The project ]… involves a consortium of 16 different research partners across nine European countries: Finland, France, Germany, Greece, Hungary, Italy, Spain, the Netherlands and the UK. The “immersive multi-modal environment” where the data sets are displayed, as pictured above — called an eXperience Induction Machine (XIM) — is located at Pompeu Fabra University, Barcelona.

Read the article, watch video and draw your conclusion. It made me think about the potential of data visualization for design. Here is my favorite passage from the article explaining the approach:

“We are integrating virtual reality and mixed reality platforms to allow us to screen information in an immersive way. We also have systems to help us extract information from these platforms. We use tracking systems to understand how a person moves within a given space. We also have various physiological sensors (heart rate, breathing etc.) that capture signals produced by the user – both conscious and subconscious. Our main challenge is how to integrate all this information coherently.”

Here is the thing. The challenge is how to integrated all the information coherently. Different data can be presented differently – 3D geometry, 2D schema, 2D drawings, graphics, tables, graphs, lists. In many situations  we can get this information presented separately using different design and visualization tools. However, the efficiency is questionable. Many data can be lost during visualization. However, what I learned from CEEDS project materials, data can be also lost during the process of understanding. Blindspotting. Our brain will miss the data even we (think) that we present it in a best way.

What is my conclusion? Visualization of data for better understanding will play an increased role in the future. We just in the beginning of the process of data collection. We understand the power of data and therefore collect an increased amount of data every day. However, to process of data and visualizing for better design can be an interesting topic to work for coming years. Just my thoughts…

Best, Oleg




The speed of data creation is amazing these days. According to the last IBM research, 90% of the data in the world today has been created in the last two years alone. I’m not sure if IBM counting all enterprise data, but it doesn’t change much- we have lots of data. In manufacturing company data is created inside of the company as well as outside. Design information, catalogs, manufacturing data, business process data, information from supply chain – this is only beginning. Nowadays we speak about information made by customers as well as machined (so called Internet of Things).

One of the critical problems for product lifecycle management was always how to feed PLM system with the right data. To have right data is important – this is a fundamental thing when you implement any enterprise system. In the past I’ve been posted about PLM legacy data and importance of data cleanup.

I’ve been reading The PLM State: Getting PLM Fit article over the weekend. The following passage caught my special attention since it speaks exactly about the problem of getting right data in PLM system.

[...] if your data is bad there is not much you can do to fix your software.   The author suggested focusing on fixing the data first and then worrying about the configurations of the PLM. [...] today’s world viewing the PLM as a substitute for a filing cabinet is a path to lost productivity.  Linear process is no longer a competitive way to do business and in order to concurrently develop products, all information needs to be digital and it needs to be managed in PLM. [...] Companies are no longer just collecting data and vaulting it. They are designing systems to get the right data.  What this means on a practical level is that they are designing their PLM systems to enforce standards for data collection that ensure the right meta data is attached and that meaningful reports can be generated from this information.

PLM implementations are facing two critical problems: 1/ how to process large amount of structured and unstructured information prior to PLM implementation; 2/ how constantly curate data in PLM system to bring right data to people at the right time. So, it made me think about importance of data curation. Initially, data curation term was used mostly by librarian and researchers in the context of classification and organization of scientific data for future reuse. The growing amount and complexity of data in the enterprise, can raise the value of digital data curation for implementation and maintenance of enterprise information systems. PLM is a very good example here. Data must be curated before get into PLM system. In addition to that, data produced by PLM system must be curated for future re-use and decision making.

What is my conclusion? The complexity of PLM solutions is growing. Existing data is messy and requires special curation and aggregation in order to be used for decision and process management. The potential problem of PLM solution is to be focused on a very narrow scope of new information in design an engineering. Lots of historical record as well as additional information are either lost or disconnected from PLM solutions. In my view, solving these problems can change the quality of PLM implementations and bring additional value to customers. Just my thoughts…

Best, Oleg




Security. It is hard to underestimate the importance of the topic. Information is one of the biggest assets companies have. Data and information is a lifeblood of every engineering and manufacturing organization. This is a key element of company IP. Combined of 3D models, Bill of Materials, manufacturing instructions, suppliers quotes, regulatory data and zillions of other pieces of information.

My attention caught Forrester TechRadar™: Data Security, Q2 2014 publication. Navigate to the following link to download the publication. The number of data security points is huge and overwhelming. There are different aspects of security. One of the interesting facts I learned about security from the report is growing focus on data security. Data security budgets are 17% as for 2013 and Forester predicts the increase of 5% in 2014.


The reports made me think about some specific characteristics of PLM solutions – data and information classification. The specific characteristic of every PLM system is high level of data complexity, data richness and dependencies. The information about product, materials, BOMs, suppliers, etc. is significantly intertwined. We can speak a lot of about PLM system security and data access layers. Simple put, it takes a lot of specifics of product, company, business process and vendor relationships. As company business is getting global, security mode and data access is getting very complicated. Here is an interesting passage from report related to data classification:

Data classification tools parse structured and unstructured data, looking for sensitive data that matches predened patterns or custom policies established by customers. Classiers generally look for data that can be matched deterministically, such as credit card numbers or social security numbers. Some data classiers also use fuzzy logic, syntactic analysis, and other techniques to classify less-structured information. Many data classification tools also support user-driven classification that users can add, change, or conrm classification based on their knowledge and the context of a given activity. Automated classication works well when you’re trying to classify specic content such as credit card numbers but becomes more challenging for other types of content.

In my view, PLM content is one of the best examples of data that can be hardly classified and secured. It takes long time to specify what pieces of information should be protected and how. Complex role-based security model, sensitive IP, regulation, business relations and many other factors are coming into play to provide classification model to secure PLM data.

What is my conclusion? I can see a growing concern to secure data access in complex IT solutions. PLM is one of them. To protect complex content is not simple – in many situations out of the box solutions won’t work. PLM architects and developers should consider how to provide easier ways to classify and secure product information and at the same time be compliant with multiple business and technical requirements. Important topic for coming years. Just my thoughts…

Best, Oleg



PLM implementations: nuts and bolts of data silos

July 22, 2014

Data is an essential part of every PLM implementation. It all starts from data – design, engineering, manufacturing, supply chain, support, etc. Enterprise systems are fragmented and representing individual silos of enterprise organization. To manage product data located in multiple enterprise data silos is a challenge for every PLM implementation. To “demolish enterprise data silos” […]

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What PLM Architects and Developers Need to Know about NoSQL?

July 7, 2014

People keep asking me questions about NoSQL. The buzzword “NoSQL” isn’t new. However, I found it still confusing, especially for developers mostly focusing on enterprise and business applications. For the last decade, database technology went from single decision to much higher level of diversity. Back in 1990s, the decision of PDM/PLM developers was more or […]

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PLM One Big Silo

June 9, 2014

Silos is an interesting topic in enterprise software. And it is a very important topic for product lifecycle management. Why so? Because, PLM is heavily relies on the ability to work and communicated across the organization and extended value chain. Accessing information in multiple departments, functional domains and application is part of this story. Silos […]

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Will PLM Vendors Jump into Microsoft Cloud Window in Europe?

April 10, 2014

Cloud is raising lots of controversy in Europe. While manufacturing companies in U.S. are generally more open towards new tech, European rivals are much more conservative. Many of my industry colleagues in Germany, France, Switzerland and other EU countries probably can confirm that. Europe is coming to cloud systems, but much slower. I’ve been posting […]

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How cloud PLM can reuse on-premise enterprise data?

April 7, 2014

Cloud becomes more and more an obsolete additional word to call every technology we develop I hardly can image anything these days that we develop without “cloud in mind”. This is absolutely true about PLM. Nowadays, it is all about how to make cloud technologies to work for you and not against you. For cloud […]

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Why PLM vendors need to hire data scientists?

December 4, 2013

The importance of data is growing tremendously. Web, social networks and mobile started this trend just few years ago. However, these days companies are starting to see that without deep understanding of data about their activities, the future of company business is uncertain. For manufacturing companies, it speaks a lot of about fundamental business processes […]

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