Changing Landscape of Analytics - Past vs Present

Like all living beings that evolve with time, technologies are also evolving - albeit at a much faster pace that humans can fathom. The field of Analytics in the realm of technology is no exception. 

For easy reference, I have captured below the changes that we are witnessing in the Analytics landscape. (Source: mentioned at the bottom of the post)

 Past:

• Analytics was about reviewing data, comparing it to previous events so that historical reports could be produced.

Present:

• There is now a desire to ask more sophisticated and interactive questions of the data. Organisations want more. They have "dashboard fatigue" from the old approaches. Analytic tools are now expected to invite the next question of the data, to spot non-obvious relationships, and to get into an interactive dialogue with the user to solve a specific problem.

 Past:

• Previously, analytics had to be simplified so that high-level business people could use it.

Present:

• Analytics has several audiences. This could include leadership, general business users, domain-specific analysts, and highly technical data scientists. The expectation is that your platform should be able to cater to the needs of several job functions.

 Past:

• Data representations were done through pie charts or histograms predominantly.

Present:

• We’re now looking at how entities of real meaning are related to each other and what the consequences of those relationships are.

 Past:

• Dashboard screens would be crowded with visuals which provided no insight.

Present:

• We can highlight visually relevant facts and relationships. Usability is catching up with the potential. For instance, a knowledge graph can help one tell the story that the data has to tell. You no longer need to think like a programmer to gain insight. You just have to just think about the problem.

 Past:

• Within an organisation, analytics was often separate from machine learning and data science groups.

Present:

• We are now seeing more convergence of these groups.

 Past:

• For a long time, AI had been a solution looking for a problem.

Present:

• Whereas now, analytics has developed to a sufficient extent to be able to take advantage of AI.

Reference: https://www.enterprisetimes.co.uk/2020/07/02/the-evolution-of-analytics-platforms/