Last week was clearly Facebook week. However, if you had a chance to take your head out of Facebook IPO and Mark Zuckerberg and Priscilla Chan wedding, you probably noticed an interesting news that came out of Google. It called Google Knowledge Graph.
Google’s Knowledge Graph isn’t just rooted in public sources such as Freebase, Wikipedia and the CIA World Factbook. It’s also augmented at a much larger scale—because we’re focused on comprehensive breadth and depth. It currently contains more than 500 million objects, as well as more than 3.5 billion facts about and relationships between these different objects. And it’s tuned based on what people search for, and what we find out on the web.
It is not so clear how GKG was built and organized. Google clearly mixed information collected from Freebase, CIA Factbook and Wikipedia. You can read Deconstructing Google Knowledge Graph blog post for more information about “How?” it is done. It is still on the level of guesses. I’m sure in the next few months we will see more examples and explanations.
Why PLM vendors should care?
PLM vendors and product are struggling with the high level of complexity. It comes also from the side of semantic richness of data as well as from the side of user interaction complexity. Google Knowledge graph shows an interesting way to simplify knowledge representation and knowledge interaction with end users. Another aspect is related to the large-scale information modeling. The information about products and product lifecycle is getting more and more complicated every day. PLM products running on SQL databases will have to find a better technological foundation for the future scale.
What is my conclusion? Web technologies are moving forward with the speed of light. I cannot say the same about enterprise software. For the majority of people in manufacturing companies, life is a bunch of Excel spreadsheets and databases with applications running for years. The cost of existing IT environments is skyrocketing. The trends like BYOD shows that people cannot tolerate outdated IT anymore. So, how to build a product knowledge graph in your company? This is a question PLM managers need to ask these days. Just my thoughts…
Big data is hyping trend these days. Many people is using the term of big data for different purposes and situations. Here is a problem of big data in a nutshell, how I see it. The data is growing. It is growing in organizations and outside of organizational boundaries. It is growing because of application complexity and implementation complexity. My take is that each time we face “data problems” that cannot be solved in a traditional phase, the case of “big data” discussion comes up. To confirm that, take a look on the definition of Big Data you can find in Wikipedia:
In information technology, big data consists of data sets that grows so large and complex that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage,[3] search, sharing, analytics,[4] and visualizing.
So, I wanted to come with some examples of situations where “big data use case” is real and can bring a significant value to manufacturing organizations. My attention caught by the report made by SAS – Data Equity: Unlocking the Value of Big Data. You can grab a copy of the report by registering via this link. Download your copy. I’m sure you find it interesting. Here is a very good explanation about why big data becomes important.
Big data is becoming an increasingly important asset to draw upon: large volumes of highly detailed data from the various strands of a business provide the opportunity to deliver significant financial and economic benefits to firms and consumers. The advent of big data analytics in recent years has made it easier to capitalise on the wealth of historic and real-time data generated through supply chains, production processes and customer behaviours.
Big data can bring value. This is what you can learn in the SAS article. You can see it on the chart SAS presented to show BigData forecast to 2017 (see below).
Thinking about PLM and the impact on specific industry sectors, the example of a supply chain is very appealing. The data in a supply chain is getting really messy. Here is a very insightful take on supply chain and big data from the same SAS report.
Optimal inventory levels may be computed, through analytics accounting for product lifecycles, lead times, location attributes and forecasted demand levels. The sharing of big data with upstream and downstream units in the supply chain, or vertical data agglomeration, can guide enterprises seeking to avoid inefficiencies arising from incomplete information, helping to achieve demand-driven supply and just-in-time (JIT) delivery processes.
Why big data is complicated and why software vendors may consider it? Here is the interesting quote from the report that actually explains that:
A major obstacle to undertaking big data analytics is the level of technical skill required to operate such systems effectively. Although software solutions for tackling big data continue to become more user-friendly, they have not yet reached the stage where no specialist knowledge is necessary. The requisite skills for big data analysis are above those required for traditional data mining, and the cost of hiring big data specialists can be prohibitive for many firms.
Big Data and PLM vendors
I haven’t seen PLM vendors providing examples and mentioning big data. I think the fundamental problem is technology. The majority of PLM software vendors are running PLM products based on platforms developed 10-15 years ago. All these solutions are relying on RBDMS. As we learned, RDBMS doesn’t scale at the level of big data. An interesting exclusion is Dassault System, which decided to acquire Exalead back 2010 and improve their semantic indexing and search. However, I haven’t seen any implementation of Exalead applied to manufacturing and big data domain.
What is my conclusion? The value of big data is undoubted. To adopt “big data”, PLM vendor needs to go to “unknown” place characterized by a different technological stack. It is not clear how they will do so. The time is running. The ability to dig into big data problem will become an imperative very soon. Just my thoughts…
I was reading IT World article early today – GPL, copyleft use declining faster than ever. It gives an interesting analyzes in the so-called “open source” field. Despite the broad definition of “open source software”, the debates about different flavors of open sources are on going. I was following Open Source in earlier on my blog. Navigate to the following link to read – PLM and Open Source Licenses. In my previous article, I’ve made analyzes of different open sources licenses available in the market and what it means for PLM.
According to IT World article, I mentioned above while the use of the GPL, LGPL, and AGPL set of copyleft (the method of making software free) licenses dominate free and open source projects, that use is still on the decline. According to the research made in the last year
The GPL family now accounts for about 57% of all open source software, compared to 61% in June,” Aslett wrote. More troubling for copyleft advocates, though, could be the projection Aslett and the 451 Group make based on the data. “…[I]f the current rate of decline continues, we project that the GPL family of licenses will account for only 50% of all open source software by September 2012.”
Later in the article author is coming to a very interesting comparison on the main reasons why “copyleft” is in decline as well as growing distance between “free” and “open source” licenses. The growing consolidation around communities opposite to to vendor’s project stated as one of the factors that drive an increase usage of permissive licenses. Almost two years ago, I discussed PLM vs. Free in my blog post. In my view, IT World article partially confirms the trend supported by many vendors to support more flexible and less restricted open sources licenses.
CAD, PLM, Free and Open Source
In CAD and PLM domain, Aras is a strong supporter of so-called Enterprise Open Source model. Aras is not using GPL licenses. Aras is developing community of users to share and development solutions on Aras Innovator platform. You can read Aras license agreement here. There is no other vendors in PLM world that follow open source strategy. At the same time, there are few products in the market distributed for free by CAD/PLM vendors (eg. Dassault System DraftSight and others)
What is my conclusion? I’m following the development of open-source software, communities, free software and future potential steps of vendors with high level of interest. In my view, vendors are more interested in how to innovate in business models rather than follow strict rules of GPL licenses. Just my thoughts…
Update 20-May-2012:
Aras just published a very good review of Aras Innovator licensing – Enterprise Open Source Aras Way. I recommend you to have a read. The short summary is as following:
1. Aras is combining Open Source solutions and non-open source infrastructure
2. Aras enterprise PLM applications are using OSI-compliant licenses – BSD and Ms-PL.
3. Aras core (or how Aras calls it Application Framework) is not available as open source. Aras is distributing it as a binary code, but it can be downloaded for free.
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Big data is hyping trend these days. Many people is using the term of big data for different purposes and situations. Here is a problem of big data in a nutshell, how I see it. The data is growing. It is growing in organizations and outside of organizational boundaries. It is growing because of application complexity and implementation complexity. My take is that each time we face “data problems” that cannot be solved in a traditional phase, the case of “big data” discussion comes up. To confirm that, take a look on the definition of Big Data you can find in 
I was reading IT World article early today – 
