Predicting Better with Predictive Analytics
One must have heard the statement, “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, the payments and banking industry were less digitalized. People had limited access to smartphones 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 customer complaints on GPS locations, Google searches, or internal data of an organization. However, data just being stored serves no purpose unless one can make sense out of it.
Facts and Finds
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 world data 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 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 the IDCs 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 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, one can make the best of data using Predictive Analytics.
Now, improve customer satisfaction and enterprise efficiency by detecting major incidents in advance.
Now, use 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 them via predictive analytics. The enormous data on customer details, sales, purchase history, etc. can enhance efficiency, better customer satisfaction, reduce risks, and optimum decision-making capability to C – suite executives.
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