W5: OL, BI, IM, A(nalytics) and Big D

Organisational Learning, Business Intelligence, Information Management, Analytics and Big Data

On first glance, a jumble of buzz-words, but in reality these terms basically summarise the back-bone of Organisational Knowledge Management.

Organisational learning: According to KMT.net, OL can be defined through two approaches. It is important to note that these approaches can work in harmony with one-another. The first definition looks at the organisation as a whole from a “cognitive” perspective. What this means is that the business is treated like a “large brain”, with each area of the brain having a cognitive function that acts as an “individual member” within that organisation. This ‘brain’ is interconnected, constantly sharing and processing information. The second view looks at learning as ‘community’ based, in where the business acts as a community. Lave & Wenger (1991) originally coined this term, where they imagined ‘community based practice’ as:

“…collective social practice that links individuals together across official organisational boundaries and departments, and makes up the community.”

Business intelligence: is essentially a system that assists in informed business decision making. This process can be achieved by businesses, through the use of technology to analyse data. Search data management summarises this, stating that:

“Business intelligence (BI) is a technology-driven process for analysing data and presenting actionable information to help corporate executives, business managers and other end users…”

Information Management: can appear in a number of forms. From a physical customer service team, who take orders and connect product managers to customers. To online cloud-based information management systems, on which data is inputted in such a way that it produces valuable information. I.e. Customer Relationship Management (CRM) software like SalesForce. Both of these examples have their pro’s and con’s, but they both strive to deliver the same outcome of enabling businesses to work smarter, think faster and ultimately deliver.


Analytics: is the process in which data sets are analysed using hardware and/or software, with the goal of trying to find patterns and characteristics within the data that can be turned into information.

Big Data: is like analytics on steroids. The shear size and volume of the data available complicates analytics due to being so large and accurate. Most of this data increases every day too. Gone are the days of eyeballing numerical reports, nowadays organisations can access a wide range of different platforms, including (but not limited to) images (Imgur, Flickr, Instagram), video (Youtube, Vimeo) and personal information (Facebook, Email). Big Data is now essential for companies, helping them to gain access to more accurate information, allowing them to stay ahead of the curve and always be one step ahead of the customer.

Organisational purpose?

All of these systems are designed so that businesses are able to utilise them, in order to make more informed decisions. They are essential in that businesses who are not constantly seeking out what it is their customers require and desire, will likely be left behind by their competitors.

Contrast and compare – same-same but different??

You could argue that organisational learning and business intelligence fall into their own category, while information management, analytics and big data, fall into another. OL and BI might refer to the tacit knowledge within a workplace. The raw intuition gained through years of experience, and the ability to take ‘data’ and ‘information’ and apply some tangible wisdom to it. IM, analytics and BD, could be better referred to as ‘tools’, and although they are powerful tools, they are only as effective as the people operating them.

“KM academics and practitioners must add to their own knowledge base so that they clearly understand the technologies and potential capabilities, as well as the risks, that big data/analytics bring to the organisation. If we do not have the knowledge to ask critical questions of the vendors who sell these systems, the techies who run them, the operational engineers who program the algorithms, and the data scientists who analyse the data then we will cede control to these people”





David Pauleen William Yu Chung Wang , (2017),” GUEST EDITORIAL Does big data mean big knowledge? Knowledge management perspectives on big data and analytics “, Journal of Knowledge Management, Vol. 21 Iss 1 pp.


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