Steps for A Powerful Big Data Analytics Program

by September 26, 2018

Organisations pushing on big data analytics programs demand an effective plan to make sure that the analytics process is the best fit for them. Opting the right technology is a great leap for the company. The trio of right database software and analytical tools will aid the organization in devising a competitive strategy for its success.

The following are the steps advised on businesses to create a successful big data analytics plan.


1. The Data to be Included and Excluded

The curse of dimensionality will play its part in every data analytical program. Organisations need to understand the data which could be converted as valuable insights for them. For example, what kind of combination of information can be used to improve customer satisfaction or what kind of data is required to solve the patterns in a stock market?


2. Effective Business Rules

In the program, it is of prime importance to get the right analytical outputs and it is essential to involve business focussed participants in the process to make sure that all of the necessary rules are identified beforehand. When the ground rules are documented, the technical staffs can assess how much the complexity is created and work is demanded to turn data input into valuable insights. Also, the analytics professionals are required to create the analytical algorithms or queries to generate the desired outputs. The better and more accurate the queries are in first place, less redevelopment will be required later.


3. Sound Maintenance Plan

A successful ongoing analytical project demands a lot of care and attention. Regular query maintenance and changing business requirements are an important part of an analytics program. As data volume continues to increase, business users will become more acquainted with the analytical process. Hence the analytical team must be ready for continuous improvement and efficient maintenance of the project.


4. Bigger Picture at the End

The focus on end users is one of the key factors in services of business intelligence. A robust information technology infrastructure can handle the curse of dimensionality of data which could be structured or unstructured. To provide a differentiated service to the customer it is important that the interface or system developed is meeting the requirement of the customers. In different organization different people from the top brass of the organization to operational workers, business analyst and statisticians will be accessing big data analytics applications in one or another and their adoption of the tool will ensure the overall success of the project. This requires a different level of interaction that aligns with the user expectation with the analytical tools. For instance, building dashboards and data visualizations to present the insights in an easy way to the managers and to workers who are not much aware of data analytics queries.


There is no doubt that data analytics is of huge success. But following the set of framework and guidelines can help the organization to keep their big data initiative on the right track. The technical aspect of the analytical project will of a broader scope. There should a union of sound technical aspects and business factors to help the organization achieve the desired outcome from big data and analytics.