Will PLM crunch untapped data in manufacturing organizations?

Will PLM crunch untapped data in manufacturing organizations?

Do you remember the golden era of desktop searches? I remember first time I had a chance to run Google Desktop on my computer. The most inspiring moment was to see documents and emails that you completely forgot about. Today, desktop search solutions are not as popular as before. Our personal digital life moved to the cloud. Application search, such as Outlook search and others improved significantly (thanks to open search solutions reused by many vendors). The focus of “data crunch” moved from a single desktop solution to cloud and mobile devices. Despite a huge promise of enterprise search solutions, majority of them are experiencing difficulties to provide efficient, reliable and cost-effective solution that can help to organization to capture and search trough massive amount of digital data. Focused search solutions are more efficient and we can see them coming from enterprise software vendors.

However, it doesn’t solve the problem of huge amount of existing data in organizations. I’ve been reading Crowdshifter article Behold The Untapped Big Data Gap. It shows some data coming from IDC study. Here is an interesting quote:

…article reported that 23% of data within the digital universe of 2012 could be useful for big data collection and analysis purposes if tagged. However, there is a huge gap in the amount that has been tagged versus the amount that remains without semantic enrichment. Only 3% has been tagged and only .5% has been analyzed.

Source: IDC/EMC.

Manufacturing organizations are desperately looking how to improve their decision management process. To leverage the existing data in an organization can be an interesting approach. I can bring many examples from PLM space where data about change management history, maintenance, suppliers, etc. can help to make a better decisions. For the moment, the majority of the information stored in application silos and cannot be used in an easy way. This data can easy become digital garbage similar to last year papers on your desktop and similar to old documents and email on your desktop before desktop search era.

What is my conclusion? To analyze data is a tough job. It requires computing resources, time, investment and smart algorithms. Google laundry list of results won’t be helpful. The new methods of data crunching and data discovery need to be developed. With only .5% of data analyzed and 3% of data tagged, we have a huge potential to tap in. Just my thoughts…

Best, Oleg


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