Data Preparation is the latest IT lingo ever since data became a powerful standard for business performance and a measurable metric for ROI and positive outcomes. Strategic insights that are extracted for analysis are translated into actionable decisions. But even before integration, there is an equally important process – cleaning, processing and preparing the raw data for analysis.


What is Data Preparation?

The method of collecting, cleaning, organizing, processing, consolidating and transforming data for analysis is called data preparation. Since it directly impacts business outcomes, data preparation is a critical task usually assigned to skilled data scientists. The process is usually done through traditional ETL (extract, load and transform) or analytical tools.


Average Time Spent on Data Preparation

A recent survey by CrowdFlower found that “Preparation accounts for about 80% of the work of data scientists.” Here are some insights from the survey conducted with 80 data scientists:Data preparation, data analysis, data, data science

It has been seen that data scientists spend most of their time preparing and cleaning the data rather than mining it; most of them are not too happy about it!

Data preparation, data analysis, data, data science

Data Preparation Challenges

Even though it is time-consuming and labor intensive, careful preparation is necessary. It helps to generate rich and accurate insights that drive value in the organization. Some of the most common data preparation challenges are:

  • Inconsistency
  • Multiple formats
  • Access
  • Lack of proper integration infrastructure
The Importance of Data Preparation

How effectively the data is prepared and managed directly affects the accuracy of the analysis. It can add immense value to a business by helping it react positively to market trends and influencing good business decisions. But with the volume of data increasing every day, we need to rethink the traditional preparation and storage methods.