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Does prescriptive analytics deserve more attention?

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Leveraging data to gain worthwhile insights is fast becoming a natural practice for businesses, as they contend to remain competitively viable in a market where the only constant seems to be change. Data is an asset and those not investing in it are likely to be missing out. Adding to this, some industry reading provided a solid reminder that there is another side of the data coin. Did you know that Forrester Research estimates that 60-73% of collected data is never successfully used for any strategic purpose? This certainly puts the ‘data hype’ into perspective for some – especially those businesses who don’t generate massive amounts of data and may feel they are missing key opportunities. There is a fine balance in having too much data to analyse and the data selected for analysis, to present the best possible scenario outcome and assist with more informed decision making. But for those who don’t have ample data, they certainly should not ignore the very effective tools that are available to get the most out of the data they do have. One such tool is that of prescriptive analytics and an insightful article I recently read addresses some very value points around best practices for this. In fact, it sparked my interest and reminded me just how valuable this form of analytics is – especially in a world where analytics skills are limited, coupled with the focus around artificial intelligence (AI) and machine learning (ML) elements for future competitive business success. Below I’ve included some of my ‘takeaways’ from this article on how applying prescriptive analytics to available data can offer a business many opportunities, including:

  • The ability to better manage data – even if the amount of data available is small. With a view of implementing prescriptive analytics, businesses are driven to take their data more seriously and ensure the quality of the data before its use to derive insights from. For instance, what use is an outcome from prescriptive analytics if the business is not confident in the data used, or if they will still make their own inferences following the outcomes presented from prescriptive analysis?
  • To make use of available data, even if in small quantities – with prescriptive analytics in mind, the business will hold value to their data, and avoid disregarding their smaller amounts of available data, thinking that effective analysis can only be done on large data sets. The assumption around ‘the more data the better the outcome’ is an outdated way of thinking and sadly, in my experience, has resulted in many businesses believing that they can’t look at such analytics tools.
  • To provide a use case for AI – pulling on insights from the article mentioned above, having the right AI processes in place means that businesses can leverage these for the purpose of prescriptive analytics. This removes the human element that would usually be needed to do the analysing of the data. In relying on AI to produce algorithms, the human analysis skills – which are often difficult and costly to come by – are no longer needed.

While often not considered, or maybe the correct assumption is that it is not a high priority, prescriptive analytics is a form of analysis that can offer a business so much. From the potential to see future outcomes based on data insights (which of course leads to better business decisions) to having a good use case for investing in technologies like AI – ensuring the business is future proof and using data assets to remain competitive.

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