Implementing Successful Big Data and Data Science Strategy

Big Data and Data Science are two of the most exciting areas in the business today. While most of the decision makers understand the true potential of both the fields, companies remain skeptical on how to implement a successful big data strategy for their enterprises. This roadmap can help you in defining and implementing the right big data strategy in your organization.

There are many ways to incorporate big data and data science process in your company’s operations, but the following practices outlined here would guide businesses make a perfect blueprint of their big data and implementation strategy.

Organizations first need to define a clear strategy in synchronization with their core business objectives for the big data implementation. A strategy may include improving operational efficiency, boosting marketing campaign, analyzing consumers for prediction or counter fraud to mitigate risk and drive business performance. The business strategy should adhere to the following points to effectively solve business problems.

With the voluminous increase in data, it has become problematic for organizations to choose the right kind of data which tackle their business problems. Social media generates petabytes of unstructured data in the form of messages, videos, and conversations. So, the best way to prompt broader thinking about potential data is to know what decision the organization could make using the data available. The data acquisition is carried out in two phases.

As big data tools and technologies become pervasive in the marketplace, choosing the right tool is important to address the business problem. Apache Hadoop is a popular framework designed to process petabytes of unstructured and structured data efficiently. However, there are other vendors available in the marketplace to process a large amount of data in a matter of seconds. Further, organizations can leverage an array of programming tools including R, Python, SAS, Tableau to analyze the data and gain actionable insights.

After zeroing in on the programming tools, organizations must define a process, analytical models in order to establish critical success factors. Advanced analytical models are needed to enable data-driven optimization.