3 important steps of big data analytics you cannot miss As a beginner, you must understand the 3 critical steps in a big data analytics project before you go further.

1. Data preparation

One of the common challenges encountered by most businesses nowadays is lack of data for analysis. Even though they may accumulate membership or transaction data for years, those data are either in inconsistent format, outdated, invalid, or distributed in different systems or under different departments’ control. It usually consumes a lot of time or cross-departmental effort to centralize all data, clean the lists and transform the data into a useful and machine-understandable format to computer before analysis happens, the step we called Data Engineering or Pre-Processing.

2. Data analytics

When all of the data are centralized, of good quality and in a consistent machine-readable format, it comes to the step of data analytics. Normally, we can define data analytics into descriptive analysis and predictive analysis. Descriptive Analysis uses business intelligence and data mining to find out the answer for “what has happened in the past?” The analysis findings are usually presented in a report or dashboard view, and it drills down data in order to uncover detail facts such as the cost of marketing, root cause of failures, key performance indicators, etc. In contrast, Predictive analysis aims to answer the question of “what could happen in the future?”

3. Data visualization

After analysis, it is also crucial to display the results in an easy-to-understand way especially if you need to present it to the management. Data visualization, a new term coming out in recent years, means the presentation of data in a graphical format that enables readers to grasp difficult concepts, identify patterns, tendency and correlations quickly and understand any insights easier.


This article originally appeared in marketing-interactive.com.  To read the full article, click here.