Data Centric Information System

By Sushmita Rai, 3EA
Data Centric Information System

Data Centric Information System: Design architecture for the Digital era

Why there is a need of adopting a data- centric approach and why should today's leading incumbents adopt a data-centric approach? In the fast-moving digital era, many organizations and companies sometimes get confused in adopting or implementing the right business rule. The only thing that seems certain is that for taking good decisions they will need to use data. Therefore, it's very important for the companies and organizations to adopt data-driven approach.

The key characteristics of a data-centric architecture of a data-centric architecture is that the data is the central asset and it also remain static and constant, while the application around it may come and go. The data will be around and valid long after the consuming applications are gone. In this kind of data design architecture, the loading and classification of the data are the first step of the process, and precede the creation of any given application. The time has gone when companies and organizations use data for the purpose of extracting responses to their daily questions, now data is used for the purpose of gaining valuable information to drive businesses. Data centric information system is an innovative approach that has been adopted by many organizations and companies to upgrade their operational and analytical processes based on all available data.

Nowadays, it becomes very important to involve or adopt some data evolved techniques to solve the basic problems within seconds and take real time decisions. Some of the business-enablers, companies and organizations are trying to solve the problems related to data silos by bringing their various data source together in a single repository. Data warehouse technique can be used to expedite the process of reporting of information. Information architecture are struggling a lot to speed up corporate reporting, reduced information-flow cycles, and shed light on the obscurity of departments, stressing on operational and analytical problems and allowing business to capture productivities that would have been hard to obtain otherwise. Data models are the prerequisites of data warehouse and the data need to be converted or translated to the data models. Data need to go through various stages of modification, aggregations and filtrations to make it understandable and standardized at a business level. Data warehouses only keep a summary of the company's altered data, rather than the full analytical part. This is very useful for business organizations that want to carry out simple reporting functions to respond to questions linked to their day-to-day needs, but it lacks the level of granularity required for accurate forecasting and predictive analysis. Indeed, the higher the level of detail, the better the result.

The new emerging technique of Data Lake considered as a possible solution for the basic issues of information. It is designed to complement data warehouses and helps to lower the cost of storage and computation. With a data lake, companies are capable of storing huge volumes of data with a higher level of granularity in order to perform more accurate forecasting and predictive modelling.

Data is normally stored in a centralized place in its original form and only given an appropriate structure by the analysis process actually using it. This innovative approach is adopted by many leading companies and organizations so that they can quickly respond to the rapid changes within a business.

Many business organizations are adopting a data-centric approach to solve or overcome the problems of data reporting and storage. Therefore, for this reason 3rd Eye Advisory® brings together the latest open source big data technologies, providing a platform that can overhaul the legacy architecture of big companies in small tactical steps.

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Article by: Sushmita Rai, 3EA
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