AI News, Achieving Analytics Sophistications

Achieving Analytics Sophistications

Today, we collectively cover these under “data science[1].” Data science today is an umbrella term spanning the use of data mining, predictive analytics, advanced analytics, statistical modeling, decision science, knowledge discovery in databases (KDD), machine learning, pattern recognition, and even AI to make better decisions based on vast amount of data of all kinds.

Figure 1 is an illustration of this goal that has survived the test of time —the desire to collect and process all types of data in granular detail and in conjunction with each other to gain valuable novel insights.

Analytics tools and related technologies have also been continuously improving at great pace with the most significant change being the availability and richness of open-source analytics tools/software.  At the same time, the possibilities for classic and new companies to innovate/improve their products and services by leveraging analytics has significantly increased.

 In a nutshell, the general theme of getting from detailed granular data of all kinds to intelligent business decisions has stayed unchanged in the last three decades while the technologies, processes, and implementation details on how to achieve it has been in continuous change.

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