Predictive analytics: Transforming data into future insights | CIO

Any unordered data from the collection in any form i.e. visual images, text, files, or any other data is considered raw data. We all are familiar with the growth rate of digital data of the universe by a factor of 10. However, is the best site to visit and nourish the knowledge.

Before proceeding, we must be familiar with “how is raw data used”. Basically, raw data can only be used when it has a well-structured and well-organized shape. We can only get this shape by transforming it into some useful factor.

Converting raw data into organized form to make it useful is known as data transformation that most enterprises consider as water to their business soil. One of the advanced forms of data transformation i.e. Big Data Analytics is a complicated method of analyzing big data to organize information that helps companies to operate more efficiently. But there are enterprises that find it a challenging task to perform because it is time-consuming and requires self-service. 


A survey was made on North American organizations that were followed by high tech companies. The takeaways say that;

  1. Companies spend lots of time on data integration and planning.
  2. In bringing data analytics initiatives to market, companies struggle with significant organizational and technological constraints.
  3. New possibilities are created by integrating cloud data systems.
  4. For business users to solve current obstacles, businesses need proper scheduling and self-service. 

This survey highlights the obvious challenges for businesses to conduct data conversion more rapidly to provide the organization with analytics-ready insights. Not only this study describes the factors that hold them back but also, the capabilities and functionality required by a framework through the original dataset to optimize business performance. It is hard to keep up with big data analytics


Companies have concentrated on applications for big data. 7 best practices to transform data are;

  1. Design the target: First of all, know the business process in which you are about to transform the data.
  2. Understand the state of raw data: Unless you don’t know the state of raw data whether it is numerical, graphical, or textual, you’ll never come to know what type of transformation is needed. 
  3. Understand what kind of data transformation your enterprise needs: Getting to know what type of data transformation is required, is the 2nd step to develop the base working.
  4. Conform the data: The previous three phases laid the groundwork for transforming the data, also known as conforming the data, into the targeted file. Here, the awareness of the reference data from the data transformation team meets the need for observed variables from the users.
  5. Build dimensions for data and then go for facts: Dimensions place the data in perspective; facts clarify what existed in the context of the dimensions.
  6. Record each and everything: The capability to record fact documents and to prove the reliability of parameters measured from fact data is given by collecting proper data test results.
  7. Engage with the user community: The measurable indication of the importance of data transformation is the degree to which the reducing uncertainty asset is embraced.

A major rich vein for companies are today’s data. Knowing the fundamentals of data transformation, such as data replication, sampling, cleaning, conforming, analyzing, and presenting, can place you in a position to discover useful insights that can have a significant influence on the company.