Why Big Data opportunity for product information is huge

September 11, 2015


Time. It is all that matters for business. The demand of customers to solve problems here and now are growing. To present 5 years roadmap is important, but to solve today’s problem is even more critical. If something will kill our business today, then 5 years roadmap can be irrelevant. Access to information is in many ways one of the keys to save time.

Companies manufacturing products are drowning into the ocean of data. It starts from design information, product, engineering, manufacturing data. But it doesn’t stop there. Products are generating lot of information during their lifecycle – new trends for IoT will bring even more information to manufacturing companies. With the shift of companies to sell services (and not products), companies will be facing even bigger challenges how to handle information.

I’m sure you’ve heard about “big data”. And in my view, big data is a big challenge for PLM vendors. There are technological reasons for that, but it is also significant cultural and vision change.

I’m following how PLM vendors are making progress with big data technologies and solution for the last few years. Siemens PLM is one of them. Earlier this year at PLM Connection 2015 in Dallas I learned about Siemens PLM acquisition of Omneo and future development of big data solutions as part of Siemens PLM Cloud Services group. Navigate to my old blog – Cloud Services and Big Data.

Yesterday at Siemens PLM Analyst event in Boston, I had a chance to listen to the presentation of Michael Shepherd of Dell about how big data can improve customer experience. Below few snippets of the presentation that caught my special interest and attention.

It all starts from the fact Dell is collecting an overwhelming amount of log information about what happens with customer devices in a real life.


Which creates a real problem of finding, slicing and dicing date. Which can be solved using different tools. A team of data geeks can work on the data and find a problem, but here is the thing – time is a problem. In most of the situations you would like to find a pattern of problem in a much faster way than team of data geeks can do.



This is where big data solutions like Siemens PLM Omneo can solve a problem. By collecting product information, applying variety of methods can bring data and find pattern in a much faster way.


One of the things that is very important is user experience. A combination of search and data exploration can be an interesting way to solve the problem. The following picture can give you few examples of how to do with Omneo.


What is my conclusion? PLM and IoT solutions a coming to the intersection of their data platforms. Manufacturing companies have a problem to process, classify, explore, search large volumes of data and relationships. PLM data platforms are hitting some limits here. This is where big data technologies and new data tools can come in place. Siemens PLM Omneo is a good example of such tools bundled into solution to save time to manufacturing companies facing data problems these days. Just my thoughts…

Best, Oleg

3 reasons why big data is a big challenge for PLM

June 2, 2015


Data was always a core part of what manufacturing does. Manufacturing companies have lots of data. According to Joanna Schloss of Dell Software, manufacturers are literally sitting on big data dynamite of potential revenues and opportunities driven by data initiatives. Joanna Schloss is subject matter expert on business intelligence and analytics; data warehousing, & big data analytics. Her recent article – On the cusp of a Big Data boom caught my attention this morning. According to her, there are several reasons that manufacturers can become a primary beneficiary of big data boom. The following passage can give you an idea why manufacturing can leverage big data:

Relative to other vertical markets, manufacturers enjoy three primary advantages that leave them uniquely positioned to benefit from big data. First and foremost, every industry and individual is touched by manufacturing in some way. You’re either doing business directly with a manufacturer, or purchasing something that at some point or another emanated from one.

In addition, because manufacturers were among the first to make widespread data collection a standard practice, they can quickly and easily scale their data collection efforts. Put more simply, a manufacturing company can track virtually everything much faster than most other companies can.

Lastly, manufacturers typically don’t face the data collection barriers that many other companies encounter. Whether they know it or not, many consumers readily provide valuable data to manufacturers on a daily basis.

The opportunity driven by big data can include improving product quality, help to discover new design for existing products and find new product opportunities. I agree with author – big data sounds like a gold mine for manufacturing companies.

It made me think how to bring these opportunity into reality. Do you think PLM vendors and platforms are in the position to make a play around big data opportunities? Manufacturing companies are sitting on piles of data. Existing business intelligence software was able to get this data, but wasn’t able to crank it until new big data technologies became available. I touched big data opportunity several times on my blog earlier – Will PLM vendors dig into big data? How PLM can ride big data trend in 2015; PLM… wait, Big data 2.0 is coming.

Big data solutions are quite unique in the way companies are implementing them. In my past publications, I was looking for examples of Big Data usage in product design, engineering and manufacturing. One of them was company True & Co that is using customer data to improve product design – PLM and big data driven design. Another example, I captured last month, is related to Siemens PLM big data projects based on Omneo platform. Read more about it here – Siemens PLM: cloud services and big data.

I’ve been thinking about the potential of CAD and PLM companies to leverage big data trend. My conclusion is that most of big data use cases are representing a big challenge for existing CAD/PLM vendors. Here is the summary of my thoughts. I can identify 3 main reasons for that.

1- Existing CAD / PLM systems are built on top of 15-20 years old RDBMS technologies. These platforms are providing limited capabilities to capture the amount and diversity of new data insight. Modern web and big data platforms are leveraging polyglot persistence principle that allows to use different database models to solve complex problems.

2- PLM platforms are built around the concept of closed world assumption (opposite to open world assumption) where all data models are predefined by a platform. Under open world assumption the data and statements about knowledge that are not included in or inferred from the knowledge explicitly recorded in the system may be considered unknown, rather than wrong or false. Existing PLM platforms have a big challenge to handle "unknown data" and be flexible enough to discover new data patterns.

3- The openness of PLM platforms are improving these days. A good example of that is Codex of PLM Openness focusing on how to establish data transparency between vendors, customers and services providers. Unfortunately, for most of PLM vendors, openness is reflected as an ability to export data from PLM system via predefined APIs. At the same time, it is hard to make design and PLM systems to be driven by the data coming from an outside world.

What is my conclusion? I think, big data is a big challenge for PLM vendors. Most of big data solutions are using platforms that are different and disconnected from existing PLM platforms built on older RDBMS technologies. Existing PLM platforms are suffering from limited ability to manage meaningful connections with big data platforms and are not capable to provide a platform to leverage big data insight and analysis. PLM vendors should discover how to apply modern data management principles to improve their ability to leverage piles of data and transform their solution from traditional data recording into data driven discovery and decision. Just my thoughts…

Best, Oleg

Image courtesy of jesadaphorn at FreeDigitalPhotos.net

Siemens PLM: Cloud Services and Big Data

May 27, 2015


You can say that buzz around big data is annoying. At the same time, organization are struggling with a fundamental challenge – there are far more data than they can handle. Some interesting facts about data growth around us. Back in 2000, only 25% of all data stored in the world was digital. By 2007, 94% of all data was stored digitally. Some experts has estimated that 90% of all data in the world was produced for the last 2 years.

Manufacturing and engineering organization have to deal with a growing amount of data. Old fashion methods of handling data are not good anymore. You may want to look on some of my previous posts – Will PLM vendors will dig into big data? , Big data and importance of information lifecycle. Even more, the question of how to use data to improve product quality or design becomes important – PLM and big data driven product design. For many organizations data can become a very disruptive force.

Last week at PLM Connection 2015 conference in Dallas, I learned few interesting facts about how Siemens PLM is developing big data cloud solutions to handle large volumes of complex information for manufacturers. Steve Bashada’s presentation was about the work Siemens PLM did following the acquisition of Omneo, which was part of Siemens PLM acquisition of Camstar.

Getting back to Siemens PLM Omneo. The idea is to discover data patterns that can lead to optimal product performance. This is may sound too generic. However, if you translate it into more specific actions. Think about finding reasons why the last batch of hardware devices such as computer flash drive or wearable gadget was defective and track a supplier of faulty components. Inside Big Data whitepaper gives you an interesting perspective on Omneo solution. You can download whitepaper in exchange of your email address here. Here is the passage from the article I specially liked:

For a compelling example that illustrates how big data is affecting the manufacturing sector, we can consider Omneo, a provider of supply chain management software for manufacturing companies. The business need was to enable global manufacturers to efficiently manage product quality/performance and customer experience. Consequently, Omneo needed to collect, manage, search and analyze vast amounts of diverse data types, and it sought the right software and hardware infrastructure to support this effort.

  • Enables global-brand owners to manage product performance and customer experience
  • Delivers a 360-degree view of supply chain data
  • Searches billions of data records in less than three seconds
  • Scales to support 300 million records every month
  • Allows customers to quickly search, analyze and mine all their data in a single place so that they can identify and resolve emerging supply chain issues

The following slide can give you generic yada-yada about the solution.


Siemens PLM is working on a solution with few selected customers. Dell is one of them. The following slides gives you an idea how a specific customer problem can be solved.


The solution uses “search based” user experience to search, filter and navigate between bits of data.


What is the technical foundation of the solution? Omneo is using some elements of existing big data stack you might be familiar with – HDFS, Hadoop, Cloudera combined with open source search technologies like SOLR. Omneo brings meta data and unified data model to handle product information and uses HBASE to manage information. The following slide can give you some more information about technical stack and how product is handling data.


What is my conclusion? Big data is a hard problem to solve. But it brings very interesting business cases. Siemens PLM Omneo is an example of specific data solution targeting big data problems in manufacturing organization. So far, the most specific example I was able to find reported by PLM vendors. My hunch, other PLM vendors might be looking on solutions, but haven’t seen specific publications about that. I think, big data can be applied in a very interesting ways to handle different product development, customer and manufacturing issues. We just not there yet. Manufacturing organizations and existing vendors are too slow to discover them. Just my thoughts…

Best, Oleg

picture credit Inside Big data article

May 19, 2015


Siemens PLM connection 2015 is taking place in Dallas this week. Thanks for Siemens PLM inviting me, I had a chance to attend the conference this week. More posts and thoughts will come, but today, I want to give you some of my notes from the opening day of the conference with keynote from Chuck Grindstaff and several other presentations made by Siemens PLM folks and customers.

First, about the community of PLM connection. Combined from customer and managed by separate board, it represents multiple industries. However, as you can see it from the picture below, the dominant 70% is covered by three main industries – aerospace & defense, automotive and industrial machinery. No surprise here…. if you think about established PLM customer community – these are industries are mostly engaged in PLM use and implementations. The interesting news is to see reps from other industries too.


The main message I captured from Chuck Grindstaff’s keynote is about smart products and how it will impact the manufacturing. In my view, the main point is that we are not separating products into large and small anymore. What we called in the past small and simple products are not simple. Any product today is a combination of multi-disciplinary technologies: advanced materials, electronic and software.


Separate note about cloud. Siemens PLM was long time silent about cloud technologies. Not anymore. Cloud messages were sent during keynote and other sessions. Initial Siemens PLM cloud strategy was IaaS and Amazon. I covered it in my earlier posts. The thing I captured yesterday is the work Microsoft and Siemens PLM is doing to certify Teamcenter and other products to be used on Azure cloud. I guess more to watch here in the coming months.


Another interesting topic I picked up was about cloud services and big data. These days product data is getting more in focus. I lives everywhere – in design, manufacturing records, sensors and many other places. To bring data together, connect it semantically and make available via search-like interface is an opportunity many companies are pursuing these days.

Siemens PLM new cloud services organization is up to the goal. I’ve been listening to Steve Bashada’s  presentation speaking about the work they do following the acquisition of Omneo, which come to Siemens PLM as part of Camstar acquisition. The following pictures can give you an idea of what Siemens is planning and I’m sure will follow this up in my future posts. They are currently working with Dell and few other companies on the solution covering engineering and manufacturing product data intelligence cases.



I was super excited to listen to Jay Rogers, CEO and co-founder of Local Motors. Not aware about Local Motors? You should close your knowledge gap asap. Why? Because Local Motors is on the mission towards next industrial revolution. Imagine you have an idea for product, push a button and…. yes, you engage in the community of people designing, engineering and manufacturing it. It comes as a smart network of people involved into design, manufacturing and distribution of the product. New materials, new manufacturing processes- agile, collaborative and what is most important – quick and efficient. Local Motors can deliver products with 5x less time and 100x less cost.




Siemens PLM discussion about Manufacturing Operation Management gave me an addition perspective on how to make more efficient production. It is about connecting engineering and manufacturing together. In a nutshell, unified manufacturing backbone connects production, quality, logistic and maintenance. It is all impossible without tight connection with PLM backbone and integrating product views – multiple bill of materials, bill of process, electronic and software related information.




The final presentation I was watching was by Craig Brown, leading PLM at General Motors. The main topic is how to deliver connected, contextual experience among all products involved into design, productions and maintenance of GM cars.



My special attention was caught by the work GM is doing integrating multiple tools including TeamCenter using LinkedData technologies. The most resonating message – use web technology for data management and integration into enterprise.


What is my conclusion? We are getting in the era of smart products, which will create even more data management challenges for manufacturing companies and PLM vendors. It will come from diverse sides – community based design and collaboration, agile engineering to manufacturing processes, smart manufacturing and production. Existing tools will not be replaced overnight, therefore an ability to co-exist will be demanded by PLM vendors and their customers. Just my thoughts…

Best, Oleg

PLM, wait… Big Data 2.0 is coming

May 5, 2015


Unless you’ve been living on the moon for the last few years, you’ve heard the buzzword ‘big data’. It came to us mostly as an ability to process large amount of data – structured, semi-structured and unstructured.

Earlier this year, I mused about the opportunity to leverage big data during different design stages (Information as a services, expertise as a service) in the context of data analytics. Navigate here to read more. Two stories excited me the most with regards to big data and analytics – the story of True & co – company using customer data to generate better design and machine design stories from AU 2014 – Future of design: how to connect physical and digital entities.

Big data tech indeed delivered impressive results in the ability of crunching data for different purposes. However, most of that was focused on the ability of actually processing data and developing algorithms to access and analysis the data. So, think about gathering information about all available design usage of part can be invaluable. At the same time, just by giving this information in the way of usage stats can drive countless questions from customers – how to get use of this information?

Here is a new trend coming – Big data 2.0. Aside of marketing buzz, it takes big data story into very interesting direction – intelligence. Entrepreneur article Watch Out! Here Comes Big Data 2.0 sheds some lights on the idea.

…But this period, bereft of insights, will soon be coming to an end. There is a new wave of companies and technologies that are changing how data is handled. Instead of simply visualizing and presenting data, the big data 2.0 groups will be interpreting your data and giving insights and actionable advice on it. [And this is] No longer for the big boys. It used to be that insights into what your data was telling you were exclusively reserved for enterprise level companies. It was they who could afford data scientists, analysts, and modelers to wrangle their data and present solutions rather than more questions. But with the advent and advances in deep machine learning and AI (artificial intelligence) software and machines are now able to provide many of these insights to the general business community.

What is my conclusion? It looks like machine learning and AI trends are coming back from late 1980s in a form of new type of technology crunching data and delivery insight on data. It can change the way of making decisions. One of the largest opportunity in the engineering field is design intelligence. Most of design decisions are relying on the traditional engineers’ memories of "how to design things". Big Data 2.0 can change that into "data driven design" approach. An interesting space to innovate. Just my thoughts.

Best, Oleg

Image courtesy of Salvatore Vuono at FreeDigitalPhotos.net

Top 5 PLM trends to watch in 2015

January 15, 2015


Holidays are over and it was a good time to think about what you can expect in engineering and manufacturing software related to PLM in coming year. You probably had a chance to listen to my 2015 PLM predictions podcast few months ago. If you missed that, here is the link. Today I want to give a bit more expanded list of trends in product lifecycle management to observe in 2015.

1- Greater complexity of cloud PLM implementations

Cloud adoption is growing in enterprise for the last few years and it is getting more mature. PLM vendors are making steps in the cloud direction too. Companies are moving from marketing and research to “nuts and bolts” of implementations. Switch to the cloud is not as simple as some marketing pundits predicted. It is more than just moving servers from your data center to somebody else place. The complexity of implementation, maintenance and operation will emerge and will drive future difference between “born in the cloud” solutions and existing PLM platforms migrating to the cloud.

2- The demand to manage complex product information will be growing

Products are getting more complex. You can see it around you. A simple IoT gadget such as door lock can combine mechanical, electrical, electronic and software parts. It introduces a new level of complexity for manufacturing and PLM vendors – how to manage all this information in a consistent way? To bring together design and bill of materials for every discipline becomes a critical factor in manufacturing company of every size.

3- New type of manufacturing companies will be attracting focus of PLM vendors

Manufacturing landscape is changing. Internet and globalizaiton enabling to create a new type of manufacturing companies – smaller, distributed, agile, crowdfunded. It requires new type of thinking about collaboration, distribute working, digital manufacturing and more. These companies are representing new opportunity and will drive more attention from PLM vendors.

4- Growing interest in mobile enterprise PLM solutions

Mobile went mainstream in many domains. Until now, engineers in manufacturing companies mostly used mobile for email. In 2015 I can see a potential to have a greater interest in mobile solution from manufacturing companies. Distributed work and need for collaboration will drive the demand to make existing enterprise systems more mobile.

5- The demand for big data and analytics in product lifecycle.

Data is driving greater attention these days. I even heard data “data as a new oil”. Manufacturing companies will start to recognize the opportunity and think how to use piles of data from their enterprise engineering and manufacturing system to drive some analysis and use it for decision making.

What is my conclusion? I think 2015 will be a very interesting year in PLM. Broader adoption of cloud, mobile and big data analytics will drive future transformation in engineering and manufacturing software. The disconnect between old fashion enterprise software and new tech vendors will increase. Just my thoughts…

Best, Oleg

What stops manufacturing from entering into DaaS bright future?

January 7, 2015


There are lot of changes in manufacturing eco-system these days. You probably heard about many of them. Changes are coming as a result of many factors – physical production environment, IP ownership, cloud IT infrastructure, connected products, changes in demand model and mass customization.

The last one is interesting. The time when manufacturing was presented as a long conveyor making identical product is gone. Diversification and local markets have significant impact. Today manufacturing companies are looking how to discover and use variety of data sources to get right demand information, product requirements and connect directly with customers. Data has power and the ability to dig into data becomes very valuable.

As we go through the wave of end of the year blog summaries, my attention caught Design World publication – 7 Most Popular 3D CAD World Blog Posts of 2014 . I found one of them very interesting. Navigate your browser to read – Top Ten Tech Predictions for 2015. One of them speaks about DaaS – Data-as-a-Service will drive new big data supply chain. Here is the passage I captured:

Worldwide spending on big data-related software, hardware, and services will reach $125 billion. Rich media analytics (video, audio, and image) will emerge as an important driver of big data projects, tripling in size. 25% of top IT vendors will offer Data-as-a-Service (DaaS) as cloud platform and analytics vendors offer value-added information from commercial and open data sets. IoT will be the next critical focus for data/analytics services with 30% CAGR over the next five years, and in 2015 we will see a growing numbers of apps and competitors (e.g., Microsoft, Amazon, Baidu ) providing cognitive/machine learning solutions.

The prediction is very exciting. Future data services can help manufacturing companies leverage data to optimize production, measure demand and help manufacturing a diverse set of product for wide range of customers. However, here is a problem. I guess you are familiar with GIGO – Garbage in, Garbage out. When you deal with data, there is nothing more important then to have an access to an accurate and relevant data sets. Big data analytic software can revolutionize everything. But it requires data. At the same time, data is located in corporate databases, spreadsheets, drawings, email systems and many other data sources. To get these data up to the cloud, crunch it using modern big data clouds and make it actionable for decision processes is a big deal.

What is my conclusion? Data availability is a #1 priority to make DaaS work for manufacturing in coming years. The ability to collect right data from variety corporate sources, clean, classify, process and turn into action – this is a big challenge and opportunity for new type of manufacturing software in coming years. Just my thoughts…

Best, Oleg

photo credit: IvanWalsh.com via photopin cc


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