Big Data and knowledge management: different concepts and methods – and still some parallels…

shield-pixabay_CC0_229112_640 2017-01-08
shield-pixabay_CC0_229112_640 2017-01-08

Knowledge

In our human brain data and information combined with experiences and values build up knowledge for decision making. This model can be illustrated with the knowledge pyramid.

Principles of Knowledge Management

The traditional main goal of knowledge managers in organisations is to gather, store and share tacit and implicit knowledge in order to increase the value of intellectual capital. Thus knowledge should be transfered into information and data to become external. These repositories should be used to support a culture of learning and knowledge sharing. Although technology is used learning is a process of socialisation, as described in the SECI-model. The knowledge manager should create value out of implicit and tacit knowledge of the members of an organisation.

New technology – new opportunities

The internet gives us the opportunity to store and share information and knowledge. This technology opens up a wide area of opportunities and activities for knowledge managers in organisations. Many applications have been published to enable communication, interactions and business online.

The technology behind it provides us with different types of media, ranging from simple text to blockbuster movies, although the memory of the servers can only store binary data. This is a digital example of getting useful knowledge out of a huge amount of simple data.

When we communicate with websites we leave a trace behind us because we have to interact with the content. This trace can be collected as data. Putting all our traces together would give a statistic probability of who we are. In this way new knowledge can be captured.

Principles of Big Data

The concept of data mining and big data is to create new knowledge from the data that are stored when we interact with online-applications. This knowledge is used for decision making in many business and government areas.

Due to the rapid market and environmental changes, the generation of new knowledge (such as product trends, customer preferences….etc.) becomes more important than just stocking archived knowledge. New knowledge often comes from public data and outside the organization. (Prof Eric Tsui, KM and IRC, Dep of Industrial and System Engineering, The Hong Kong Polytechnic University)

The huge amount of structured and unstructured data that is collected can be used to create value out of it. This concept outlines our brain performance in supporting the building of knowledge from data.

Personal Approach

During my study of applied knowledge management at the university of applied sciences in Burgenland I attended the edX-Massive open online course (MOOC) about Knowledge Management and Big Data in Business. This MOOC has been provided by the Hong Kong Polytechnic University. Thus I gained an understanding of the differences and parallels of knowledge management and big data. At first knowledge management and big data were two different areas with only minor parallels. Although there are differences, the interesting part was exploring the striking parallels.

In my job I work with intranet-cloud-applications. My employer could also use the concept of data mining and big data to implement this information into the application lifecycle. This could be a method to improve the usability of these applications. Data that could be of interest are:

  • Login-logoff times
  • sequence of activities and transactions
  • often used links and buttons

This article by the Hong Kong Polytechnic University gives a deeper understanding of this topic.

Related blogposts

Big Data for Knowledge Managers
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