The reciprocal connection between knowledge management and social collaboration platforms would be clear to most. But what about a connection between knowledge management and big data?
Using KM can help facilitate additional value from big data.
A Big Data Knowledge Spiral
Wikipedia defines big data as: “Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.”
In other words, the big data movement is about extracting business value from very large data sets. I'll leave it to the other experts to write articles about how to achieve that, as databases and data manipulation are not my areas of interest or expertise. However, once you've extracted useful information from the data, then you enter the realm of processing and sharing the useful gems of intelligence gleaned from the originally humongous data set.
As noted before, KM doesn't always play nicely at the lower levels of the data-information-knowledge hierarchy — what one organization may define as a knowledge management problem, another will see as an information management issue. So there can be considerable overlap.
So what would this look like in the big data context? Let's stick with the progression from data all the way up through information to knowledge:
Last month we looked at Nonaka and Takeuchi’s SECI model, specifically noting how this model suggests we can transform information into knowledge, and then use that knowledge combined with our experience in processing new information into knowledge in the future. I attempted to capture this dynamic in a simplified form in the diagram above.
Information developed from running powerful analytics tools against your massive data sets can be turned into knowledge. In turn, this knowledge can be applied to processing new information from new or expanded data sets in the future. This is a version of what is often called the Knowledge Spiral.
Creating Value with Big Data
Businesses don't store, process and analyze very large data sets for the fun of it. They are looking for some business value, or some research finding that will drive value further down the chain. But what happens if they look at the progression a different way?
Analysis of large data sets can provide information in many forms, including historical trends which can feed the development of new models applied to the analysis. However what you want to achieve is that “actionable insight” that provides decision making support. This might be the ability to state with authority that over the last five years, the company has purchased too much raw material and made too many widgets, which sit on the shelf for too long. A holistic view emerges, which points to efficiencies that can be made in the supply chain processes.
Retaining the knowledge that drove this decision becomes the next priority, perhaps in the form of explicit knowledge assets captured in a system, but also in the “corporate memory,” via organizational learning methods. Now we are firmly back in the KM world, and the practical application of theory can help your organization lever the explicit information and the tacit knowledge that is the end product of some serious analytics applied to some really big data sets!
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