Data Analytics to Drive Business Value

The metamorphosis taking place in the analytics world has been rapid and transformational. The analytics space has moved from business intelligence to the age of big data analytics. These transformations have been affected in the recent years as more and more companies are opting for digitization. The technology, infrastructure and the philosophy of organizations have changed as well. It is now evident, that organizations will have to keep pace with the changes or perish. It has all the more become important to embrace data analytics to develop an agile IT framework and build a strong base for data science.

The Challenge

The question here is not the data but what has to be done with the data. The challenge is to utilize data in such a way so as to address key businesses challenges. In today’s world, if organizations need to succeed and innovate digitally, they have to be prepared to utilize big data effectively. Organizations need to also bring under their ambit areas such an artificial intelligence(AI), Internet of Things (IoT)and data services tools. The need of the hour is to develop an IT framework that is able to analyze data along with identifying the most relevant data, monitor and manage the quality of data and gain an in-depth understanding of the data. Adding new parameters and understanding to the data in hand is more important. The challenge can be broadly mentioned as a) identifying the problem in hand, b) Innovative methods using data analytics to solve the problem.

The way out

The few things that organizations can do to fabricate a strong bond between analytics and business results are:-

• Identifying business goals – Organizations face problem while measuring the return on investment on big data analytics project. The number of variables is multiple on a project related to big data, which becomes a cause for concern for organizations. Confusion rules and often organizations shift from their ultimate goals. Thus, it becomes extremely essential for an organization to define measurable goals that can be monitored right at the beginning. If ROI(return on investment) is calculated based on clearly defined measurable goals, organizations will be clear about their standing.
• Understanding the Uniqueness of the project – Big Data Analytics projects vary from each other, for example, a project based on predictive analytics will be inherently different from a project based on the non-predictive Organizations need to adapt themselves according to the project in hand. Offering unique and best in the breed solutions to clients is the key to staying ahead in a competition.
• Build a bridge – Organizations need to build a bridge between conventional data sources along with endorsing cloud based analytics, Internet of Things to generate both human and machine data analytics. The need of the hour is to continuously evolve their thinking process and keep on correlating unrelated data points to generate unique outcomes.

Apart from opting for the above-mentioned steps, organizations need to also understand that just adopting big data analytics will not solve all problems and drive business benefits. However, organizations will become efficient after adopting data analytics in this digital era. Data Analytics service providers will definitely have an edge over their competitors.