Following diagram outlines our Data Analytics process, where data is processed to be structured as information and then, presented as decision-making knowledge sets. While good data management forms the foundation of good returns, Data Analytics is needed to draw the value out of the data maintained.
Stopping with data preservation is like resting after collecting crude oil. Without the processes to draw gasoline, the usability of crude oil is highly limited. Data too has to be processed and analyzed for it to be effective.
The Analysis of data covers a myriad of processes such as inspecting, cleaning, transforming, and modeling data. This is done with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names (source: Wikipedia).
Traditional systems such as ERP, CRM, Lead Management Systems, Invoicing Systems are all rich in data of varying kind. The first stage in data analytics involves the identification of all appropriate sources of data and the extraction of the same to a unified database for assessment.
Following this, several methods are used to generate knowledge from the data obtained. Some of the well-known methods employed include cluster analysis, spatial analysis, exploratory analysis, confirmatory analysis, etc.
Business analysts then study the analysis and generate models to predict trends and patterns based on internal and external variables. These patterns and trends form the core of the processed information we present as Knowledge for timely decision making.
Knowledge generated off of data analytics helps in the identification of unique marketing and targeted product sales opportunities, detection of fraudulent behavior, prediction of product success in a particular market, detection of money flow patterns, identification of high value customers and more.