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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.

Define the Big Data Analytics 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.

  • The business strategy should align itself with the enterprise quality and performance goals. 
  • It should focus on measurable outcomes.
  • It should transform your company’s capabilities through data-driven decision making.

Choosing the right data

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.

  • Ingest: This includes gathering data from various sources and to bring them on a common platform. The data may come in different forms including CRM, point-of-sales, and call records which are structured data while documents, logs, emails, photos, comments and social media feeds are unstructured data.
  • Transform: Upon acquisition of data, it is refined and organized to help analysts apply the right data science tools for further analysis.

Using right data science tools

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.

Define an analytical process

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. For example, predictions about consumer buying behavior on web or stores based on their past history require a model to solve broader optimization problems across functions and business units. However, it is important to note that the analytical model requires filtering the data as too many variables will create complexity in the process.

Align with the cloud operating model

The analytical processes within organizations should be designed in a collaborative way. The resource management needs to have a control of the entire-data flow right from pre-positioning, integration, summarization and analytical modeling. This entire process must be synchronized with private and public cloud provisions backed with data security. The advantage of cloud-based operating models is that it can be scaled-up based on business requirements.

Run a pilot project

Pilot projects make it possible to test the outcomes before making a firm investment. It helps organizations to manage and minimize risks. Testing a business model allows the team to examine the effects and discover the unexpected results.

A big data and data science project should aim at combining internal data from various sources with external data sourced from social media or third party sources. Since pilot projects pave the way for larger big data initiatives, it is imperative to keep the following goals in mind while defining the requirements.

  • The data should be accessible, safe and familiar.
  • The project must have data privacy and security policies into the plan.
  • Organizations must have contingency plans in case of any discrepancies.
  • Capturing the ROI measures in qualitative or quantitative form, or both.
  • Building an existing strategy at a point where the returns are diminishing.

Embed analytics into decision-making

The big data and data science environment requires thoughtful experimentation. In order to gain a competitive advantage, companies need to embed analytics into business decision-making. With the explosive growth of big data, the ability to model and forecast data is becoming a norm. These outcomes are part of the day-to-day operations and help organizations stay ahead on the growth track.


Big data and data science technologies are gaining a lot of traction in the market. The ultimate success of big data projects lies in reaping business value from it. As businesses are wholeheartedly embracing these technologies to gain competitive advantage, the prognosis is bright and fruitful. However, effective big data governance which maintains a data-driven culture is a key ingredient to the success of the big data and data science implementation programs. The guiding principles described in this article will help you in embarking on an insightful journey that blends quality and competitiveness of data to analyze future.

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