One must have heard the statement 'The world's most valuable resource is no longer oil, but data".
The above statement stands to its testimony as of today.

Not many years ago,

  • Payments and Banking industry were less digitalized. 
  • People had limited access to Smartphone’s and the internet.
  • Organizations had their data stored in silos.
  • Processes were less automated and less connected.
  • Ease of access to the internet was limited.

As technologies progressed, they were able to record and store data extensively. Be it of customer complaints, to GPS locations, to Google searches or internal data of an organization.
However, data just being stored serves no purpose unless one is able to make sense out of it.

But what good is this data if no insight could come out from it.
It was not long ago that data got BIG and COMPLEX, becoming practically impossible for humans to gain insights from it.
In the IBM study in the year 2017, '90 % of all the data in the world back then was created in the last two years.’

The statement above does justify the following points.

  • Tools/Applications are collecting and storing data vigorously.
  • Data collected can be used for deeper insights.

With the quantum of data that has been generated and more of which is on the verge of creation, it is no more possible for humans to make the best of such data.
According to IDC's study, "The Digital Universe in 2020," there shall be around 40 trillion gigabytes of data, i.e., 40 zettabytes in 2020.
Also, as per the same study, the size of BIG data stood at 1.2 zettabytes in the year 2010, growing at a rate of 200% YoY from 2010 – 2020.
The ton of data that has been created over the years because of digitalization is no more comprehensible for humans to generate insights from it.
Enterprises don't just need historical or present data but also be able to make decisions based on available data.

With ITSM tools, such as ServiceNow, now one can make the best of data using Predictive Analytics.

  • Now, improve customer satisfaction and enterprise efficiency by detecting major incidents in advance
  • Now, make use of natural language processing (NLP) to recommend Incidents, cases, and knowledge, enabling enterprises to increase workforce productivity.
  • Now, route and categorize issues automatically to decrease resolution time and avoid manual errors. 

Analytics is the discovery, interpretation, and translation of data into meaningful insights. Analytics helps organizations make better decisions by providing exact and specifically needed reports.

According to SAS,’ predictive analytics uses statistical analysis and machine learning to predict the probability of a specific event occurring in the future for a set of historical data points.’

Businesses aim to be ready for unseen threats and opportunities, and they are trying to resolve it via predictive analytics. The enormous data on customer details, sales, purchase history etc. can provide enhanced efficiency, better customer satisfaction, reduction in risks, and optimum decision-making capability to C – suite executives.

 

Sources

Big Data Statistics 2020 – www.techjury.net

Predictive Analytics – 5 Examples of Industry Applications – www. emerj.com

How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read – www.forbes.com

What is predictive analytics? Transforming data into future insights – www.cio.com

The world’s most valuable resource is no longer oil, but data – www.economist.com