When Netflix decided to finance the series “House of Cards” they were not just taking a blind bet. After analyzing user trends, preferences and habits they decided that a series like “House of Cards” will hit the nail. And they did. A profit of 31.8 million was accomplished (Adweek, 2013) and this is just one example of how data analysis can help us take smarter decisions. How is this done? Can we really make sense of the huge amount of data that is being generated daily? How does this relate to knowledge management (KM)? Read on to find out.
Big data is the name given to large sets of data that can be analyzed computationally in order to reveal patterns such as those spotted by Netflix. “The challenge is related to how this volume of data is harnessed, and the opportunity is related to how the effectiveness of society’s institutions is enhanced by properly analyzing this information” (IBM, 2014). In order to do so, various companies such as IBM and Oracle are already developing custom tools that can be applied in various work environments. Other tools derived from social media are helping track trends and user insight within social networks such as Facebook. This helps publicity and marketing initiatives.
In KM, Big data can also help us. Let me explain this with a couple of examples derived from certain scenarios at work.
Social collaborations tools are popular nowadays in KM due to the fact that they facilitate collaboration and knowledge transfer. At office we developed our own social platform based on Liferay. Communities come together in order to ask questions, share information and analyze project outcomes.
By analyzing the amount of data that was being produced as a consequence of community interaction, we were able to spot common problems in various projects ranging from management issues to specific technical scenarios. At another company I worked at we were able to do a similar analysis based on data generated in the discussion forums. Our aim was to spot repetitive problems that project teams might be facing. So if you have a social collaboration tool you might just want to have a closer look at where interaction is leading to. Facilitating knowledge transfer is just as important as analyzing data in order to continuously improve the work environment.
It’s important to remember that KM can capitalize social data- feedback streams, forums, micro-blogging, but this data must be distilled and combined with other information, such as business insight, lessons learned analysis, etc in order to provide a more powerful and insightful understanding of the situation at hand. Just picture a scenario where you can start predicting the strategic knowledge which will be required before even managers become aware of it.
In general terms, this means that Knowledge Managers must start to dig deeper into Big Data and consider including data analysis in the KM tool box. Whichever way we look at it, the challenge has already risen.
© Jose Carlos Tenorio Favero