Data & Analytics (D&A) is expected to impact and alter the competitive business landscape in the near future.
While D&A has graduated from a relatively unknown term to a business buzzword, the term D&A – however – may have been abused and its connotations and key messages were distorted over time.
Much has been said about the concept of data and that organisations should invest in data mining and visualisation tools to extract useful information. Like many other countries, Malta underwent a staged developmental process, over the last decade. Several initiatives were undertaken to mine data, to build data warehouses and more recently lakes, and to implement business intelligence (BI) solutions.
The term BI added to the confusion. On the one hand, it conveyed the message of sophistication while on the other hand, the term was frequently associated with projects that failed to deliver what the business was promised: namely adding business value.
This was due to a culmination of factors, chiefly project fatigue; too much focus on building the data warehouse; less focus on how to process the data and poor advice on the appropriate technology stack. The resultant effect was that few organisations managed to reap the benefits of what ultimately became known as “BI projects”.
D&A is not the new label on old “BI bottles”, that is, it should not be if it is to be successful. The approach and the business mindset need to change. Lessons from failed BI projects have taught the industry that the focus should be on identifying the right data sources and focusing on those that are readily available.
Perhaps less attention and importance should be given to data consolidation and cleansing. Although two very important aspects, experience shows that they drain financial and technical resources and sometimes lead to project delays and abandonment. The focus should shift more on to analytics. Analytics refers to a scientific approach where advanced statistical mathematical techniques use to examine data and extract meaningful information extracted.
This approach, uses descriptive analytics to assess the findings in a scientific way – the first step in an ‘investigative’ journey. The journey moves into predictive analytics where we project the past experience on to what would happen to an outcome in the future. Prescriptive analytics takes this a step further: it attempts to optimise the outcome.
Predictive analytics has been around for the last 20 years and has been applied successfully in the engineering field, including oil and gas exploration, medicine and more recently in the business fields. Prescriptive analytics is considered by some as an obvious extension to predictive analytics. However, it sends an important message to the business: prescriptive analytics is about optimisation of processes using underlying data whereas predictive analytics stops at predicting a business outcome given past experience.
A tangible example could relate to a customer requiring assistance to optimise his debtors. The first step would be to review the demographics of the customers. Through descriptive analytics, charts and graphs would illustrate the mix of the customers that pay late. For instance, using predictive analytics, one can predict with a degree of certainty that customers who purchase a particular product and fall within a particular age bracket, have a propensity for not paying. Recommendations would be proposed accordingly.
Prescriptive analytics might lead to the recommendation of adopting different payment terms using an automatic system when selling the product. This will optimise the debt collection process as the client is receiving real-time advice. All of this is driven by the data that the organisation already possesses.
D&A is progressively becoming the scale by which performance and growth is measured driving business decisions and disrupting traditional business models.
… D&A is a game changer.
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