Big Data Concept & Industry Impact

Aug 19, 2016


In Today’s digital economy world, BigData is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.

Big Data is essentially the data that you analyze for results that you can use for predictions and for other uses. When using the term Big Data, suddenly your company or organization is working with top level Information technology to deduce different types of results using the same data that you stored intentionally or unintentionally over the years.

Big Data Concepts

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

This is another point where most people don’t agree. Some experts say that the Big Data Concepts are three Vs:

  • Volume
  • Velocity
  • Variety

Importance of Big Data

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

  • Determining root causes of failures, issues and defects in near-real time.
  • Generating coupons at the point of sale based on the customer’s buying habits.
  • Recalculating entire risk portfolios in minutes.
  • Detecting fraudulent behaviour before it affects your organization.

Examples of Big Data:

Data will comes mainly in three forms –

  • Structured data: Structured data has semantic meaning attached to it whereas unstructured data has no latent meaning. The growth in data that we are referring is most unstructured data. Few examples of unstructured data calls, text, tweet, net surf, browse through various website each day and exchange message via several means (Relational data).
  • Semi Structured data: XML data.
  • Unstructured data: Word, PDF, Text, Media Logs.

Benefits of Big Data

Big data is increasingly proving to be a critical element in business environment and it’s emerging as one of the most important technologies in modern world. Following are just few benefits which are very much known to all of us:

  • Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.
  • Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.
  • Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.


Big Data has become ubiquitous in modern society. It challenges state-of-the-art data acquisition, computation and analysis methods. Much focus has been placed on the application of Big Data methods, less of a focus on the theoretical underpinning of the field.

Big Data Industry Applications

Big data supports analytics that can then be applied to industry-specific applications. The most common applications are being in the areas of customer analytics, operational analytics, and fraud and compliance.

Some of the Industry Specific use Cases are as below:

  • eCommerce – Online retailers will extend use of predictive analysis in recommendation engines. They’ll make use of cross-channel analytics in order to link sales to specific marketing campaigns. Big data analytics will help give customers more personal experiences on the websites they visit.
  • Retail – Retail usage of big data analytics is increasing, with sales analytics usage expected to increase by 58% in 2016. The motivation? Four times as many high-performing teams use predictive analytics. Other big data uses in the retail business include fraud detection, personalization, supply chain management, dynamic pricing, and forecasting. With these applications, retailers can target their marketing, effectively manage loyalty programs, and optimize their yield.
  • Financial Services – Fraud detection usage will continue to expand. Big data analytics for risk management will also expand. Trades will be made based on analytic assessments of data; big data analytics will ensure that trades are in compliance with all appropriate regulations.
  • Healthcare – The healthcare industry is looking for big wins from small improvements in efficiency; a 1% efficiency gain might lead to $63 billion in savings. Big data in healthcare will improve treatment studies by reviewing database records of patients not officially enrolled in the study. Cancer diagnosis and monitoring will become quicker and easier, through using analytics techniques in order to assess genetic information in blood that indicates tumor response to treatment. Big data analytics will also help pharmaceutical companies review data for drug discovery.
  • Utilities – The usage of smart meters will spread, as will the use of building automation in order to manage energy usage.
  • Digital Media – Analytics will underlie digital marketing, including click stream analytics and targeted ads. Social media analytics will be used in order to understand the audience and derive and monitor campaigns and loyalty programs.
  • Government – Fraud detection, threat analysis, and other security concerns are primary uses of big data in government. Big data also has a role in politics; campaign managers use social data analysis in order to target voters. With 2016 being a presidential election year, that may be the big data use case with the biggest impact in the near future.
  • Insurance – Insurance companies are committed to big data projects, with 39% of billion-dollar companies having projects underway. The biggest current use cases are in risk evaluation; newer projects will apply to customer acquisition and customer service work. For automobile insurance, expect companies to start using data collected by vehicles in order to provide information that will inform pricing based on actual behaviors rather than assumed group norms.
  • Travel – Online travel sites will use big data in order to personalize the user’s experience, by making recommendations based on social media analysis. They’ll also make more sales by analyzing the customer’s browsing behavior.

Facts and statements in this article are taken from Gartner, IDC, and Big Data research, etc and are publicly available information. Some of the statements are “forward-looking statements” and are subject to material risks and uncertainties.

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