It’s OK, we understand. Here we are throwing another buzzword at you. But bear with us because it is not really the words that matter, but the thinking and the technology behind the words. And most importantly, what augmented analytics can do for you.
Let’s step back first.
Analytics has been with us for some time – more than a couple of decades – though until recently it was generally called business intelligence (BI). But it is worth reminding ourselves of what it means, and what made it possible.
Analytics is a very broad term and we can get a better understanding of what it means by viewing it in four layers, each of which answers a successively more difficult question:
- What happened? (Descriptive Analytics, formerly called Business Intelligence)
- Why did it happen? (Diagnostic Analytics)
- What might happen? (Predictive Analytics, also referred to as Augmented Analytics)
- What should I do about it? (Prescriptive Analytics, also referred to as Augmented Analytics)
Augmented Analytics was made possible thanks to the use of machine learning techniques such as neural networks and multiple regression analysis and mathematical approaches such as decision tree analysis Bayesian inference modeling. These found their earliest commercial applications in areas such as customer profiling, market segmentation, and credit scoring as well as risk analysis (e.g. to determine the likelihood that a major client will default on a bank loan).
Further technological developments have changed that to give us what Gartner defines as augmented analytics, which “… uses machine learning and artificial intelligence techniques to transform how analytics content is developed, consumed and shared. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.”
This is a journey, and we are still only at the early stages. Nevertheless, by embedding intelligence into all the key applications from source to pay, algorithms can set certain actions in motion based on the collected data, which trigger recommendations for the user in the system.
What is Needed to Get Started?
An obstacle to more rapid progress is the lack of relevant skills in many procurement organizations. While augmented analytics reduces the need to know how the technology works, what is missing is access to the technology and an appreciation of why it should be used, i.e. the benefits it can bring, such as identifying supply market trends ahead of the competition.
By Amenallah Reghimi
Keywords: Future of Work, Predictive Analytics, Procurement