From Data To Decision Making
Organisations have access to ever increasing amounts of data and information. Just a few decades ago these were restricted to what was held in the enterprise operational or financial systems. Now days, vast amounts of data collected from sensors and RFID, social media, and other sources, cause organisations to have more data than they can handle (Delen 2015).
Business Intelligence and Analytics provide us with the means to analyse data and help organisations to make better decisions (Chen et al. 2012). Unfortunately, in many cases these instruments create even more information for the organisation to digest and thus creating issues rather than solving them.
The challenge we are facing is how to make the best use of these tools. How can practitioners translate their analysis into useful knowledge and how can organisations utilise it in their decision making process?
From Data to Analytics
Business Intelligence, Business Analytics, and Big Data Analysis use data and information with the purpose of creating new knowledge and help organisation to make betterinformed decisions. They include an assortment of information systems software and hardware, statistical methods, business processes, and methodologies.
Davenport (2013) discussed the progress of analytics from Business Intelligence (Analytics 1.0), through Big Data (Analytics 2.0), to Data Enriched Offerings (Analytics 3.0). Each stage represents ever growing levels of data quantities and complexities.
Business Analytics can also be classified as Descriptive, Predictive, or Prescriptive (Delen 2015). The entry level descriptive methods help knowing what happened or is happening in the organisation. From there predictive analytics can be used to try and predict what will happen in the future. Prescriptive analytics is the most mature form of analytics, and is used to determine what to do based on the results of the descriptive and predictive analytics.
From Analytics to Knowledge
To create knowledge out of the vast amounts of analysed information, BI&A practitioners need to possess more than analysis capabilities. According to Chen et al. (2012), good business knowledge and communication skills are essential to the success of BI&A projects.
Pauleen et al. (2017) suggest that contextual knowledge can assist in choosing analytic tools and analysing data for specific or exploratory purposes. Contextual knowledge comes from different sources, both implicit and explicit, within the organisation. Practitioners should have exposure to this knowledge and thus be able to better align their analysis to the organisation needs.
New knowledge, developed through analysis, needs to be assessed and confirmed by reviewing the analysis process. This should be done by the analysts who gain knowledge about the business area, the underlying data, and systems used (Sacha et al. 2014).
From Knowledge to Decision Making
In spite of the resources dedicated to the analysis of data and information, organisations are not always able to achieve satisfactory results from their investment. In many cases the linkage between the data, its analysis, and the decisions taken, is insufficient (Pauleen 2017).According to Laursen and Thorlund (2016), business analytics should be well connected with the organisation’s strategy and their role is to help the business to move towards achieving its objectives.
In order to ensure BI&A are utilised for their full potential, they need to be immersed in the business and the decision making process. Such a close association may make certain that the knowledge created by BI&A is actually beneficial to the organisation.
One way to achieve that is to have BI&A practitioners as part of the functional teams. This way the practitioners can have full understanding of the business processes and be in a position to influence.
In cases where an organisation have a centralised BI&A function, appropriate processes should be in place to make sure the involvement of the function in decision making.
Gold or Hot Air
BI&A have the potential to help organisations to utilise the vast amounts of information available. It can, if done properly, extract golden knowledge out of what seems to be heaps of straw, help the decision making process, and add to the competitive advantage of the organisation. Done wrongly, and even the most sophisticated and statistically sound analysis will contribute nothing. After all, we do not want to end up in a situation like the one described in the man in the hot air balloon story.
Chen, H., Chiang, R.H.L, and Storey, V.C. (2012), “Business Intelligence and Analytics: From Big Data to Big Impact”.
Davenport, T. (2013), “Analytics 3.0”.
Delen, D. (2015). “Real – World Data Mining”.
Lauren, H.N. and Thorlund, J. (2016), “Business analytics for managers: taking business intelligence beyond reporting”.
Pauleen, D., Yu, W., and Wang, C. (2017), “Guest Editorial – Does big data mean big knowledge? Knowledge management perspectives on big data and analytics”.
Pauleen, D. (2017), “Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen”.
Sacha, D., Stoffel, A., Stoffel, F., Kwon, B.C., Ellis, G., and Keim, D.A. (2014), “Knowledge Generation Model for Visual Analytics”.