The power of search giants like Google is enormous these days. Think about the amount of information Google, Twitter and Facebook are processing and you will be knocked down by the numbers. Consumerization is a significant trend these days and everybody are thinking how we can apply well proven web and open source technologies in the enterprise field. Think about product designs, engineering documents, Bill of Materials – things that we commonly considering as product data. Eventually, the dream could be to see how Google’s engineers are recommending best parts to use or cracking Bill of Materials with 100s levels of data. Not so fast…
When it comes to a product data you can discover that this type of information processing is different from what we got to know on the web. It starts from the diminished importance of ranking mechanism based on other people discoveries. For example, if you happen to be searching for “Part CHI-93939-STD” it may not come up on the first pages of a search. But it may be found more directly via a connection to an existing assembly that references it. Data semantics in this case is more important than data ranking.
I recently came across the following study – Top Google Result Gets 36.4% of Clicks. Have a look at the charts, you’ll get my point quickly: if you are out of first five (5) page results, you essentially don’t exist. So if you’re “Lady Gaga”, you are certain to appear and ranked in the top pages. These days "social ranking" is adding some additional flavors to the overall search results. Nevertheless if you are “Part CHI-93939-STD”, then chances are, you don’t exist!
Another interesting blindspot of Google search – lifecycle data. Few days ago, I caught an interesting study – Filling a Search Engines Blindspots. Here is the passage describing lifecycle blindspot:
Today, Christian von der Weth and Manfred Hauswirth at the National University of Ireland in Galway identify one blind spot in Google’s coverage and describe their vision for how to fill it. This information blackspot consists of location-specific information that is only useful for people for short periods of time. An example would be a question such as whether an advertised bargain is still available at a particular shop. Another is to ask whether parking spaces are available at a public event such as an air show, music concert or such like. There is no way that a search engine like Google can index that kind of information that is specific to a particular location for just a short period of time.
What is my conclusion? Product data is extremely complex. It contains lots of relationships, dependencies and semantics. However, it is not everything. The most important element of product data is lifecycle information. Since product data is changing as a result of product development, use, maintenance, etc. systems need to be able to capture this product lifecycle data in a real time to provide a correct data representation for people in manufacturing companies and extended eco-system. It is not a trivial tasks and very interesting problem to crack. PLM software architects and other techies – be aware about complexity of product data lifecycle management. Just my thoughts…