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 The trends like BYOD shows that people cannot tolerate outdated IT anymore.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…
Let’s talk nuts and bolts today. APIs.. If you think about any PDM / PLM implementation, the question about API is one of the most important. Why so? Because you know – it is near to impossible to get all done out of the box and via configuration. Even if marketing advertised and sales promised, you will have to have something to be done behind the scene using this magic word API.
PLM Openness
The topic of openness comes very often these days. I’ve been posting about openness about a year ago – PLM and New Openness. Notable news around PLM Openness is coming these days around so-called “Codex of PLM Openness” introduced by ProSTEP iViP. Navigate to the following link and you discover that majority of PLM vendors, including big-three-PLM (Dassault, PTC and Siemens PLM) are committed. Yesterday, during the opening session of annual Siemens PLM user conference – PLM World 2012 in Las Vegas, the topic of PLM Openness came into many conversations and even was captured by Siemens PLM blog.
Enterprise Systems and APIs
Enterprise systems have long history of API development. If you spent enough time in your life with databases and enterprise business you probably remember horrible stories of proprietary databases, move to SQL, hope of XML, believe in SOA / Web Services latest dreams about REST APIs. Last week, I came to a very interesting blog trilogy from CloudAve blog about enterprise architecture, APIs and more called – Simple Service Enterprise part 1, part 2, part 3. It is a bit long, but I recommend you to have a read. The following picture was resonating to my thoughts related to PLM implementations and APIs:
Here is my favorite passage that I’d apply to product lifecycle management and many other enterprise implementations:
…the fundamentals of information interchange: exposing business functionality, currently encapsulated in the back-end, to the outside world via services. These services are a one-to-one translation to back-end functions, which are one-to-one translations to business process steps themselves: the smallest level of business transaction.
Implementations, API and Open Data
Here is the idea how I see the future of open APIs. PLM system(s) is holding hostage of data and responsible for a set of process and transactions. Since PLM system cannot live in a vacuum, the interaction of PLM system with other systems in the enterprise (including various B2C and B2C services) is driven by processes. In order to have a productive API, you need to expose these processes using an appropriate level of granularity, including semantics of data (in this context, thinking about resources seems to me as an appropriate way). Having such a level semantically-resource-oriented-APIs can provide an easy and open way to interact with PLM system to build the most effective services.
What is my conclusion? To build a good API is a very complicated task. To make Open API is even harder. I can see a potential in exposing both semantics of data and related system functions in a way allowing me to use it and accomplish processes automatically. I think, web and REST give us a bit promise. The responsibility of vendors is to develop an appropriate level of granularity to make it usable. Just my thoughts…
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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 
Let’s talk nuts and bolts today. APIs.. If you think about any PDM / PLM implementation, the question about API is one of the most important. Why so? Because you know – it is near to impossible to get all done out of the box and via configuration. Even if marketing advertised and sales promised, you will have to have something to be done behind the scene using this magic word API.
