An Introduction to Big Data
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An Introduction to Big Data

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Ken Gabriel, the acting director of DARPA, explained Big Data with a quite easy to understand analogy. He compared Big Data to Atlantic Ocean, ‘The Atlantic ocean is 350m kilometers in volume — 100 billion billion gallons.” Gabriel stated, “if each gallon represents a byte, the Atlantic ocean would only be able to store the data generated by the world in 2010.”

So Big Data literally means ‘big’ data- huge collection of data that traditional technology can hardly manage. As technology has become quite omnipresent at every point of our life and these technological tools are likely to capture or record some kind of information; it is no wonder that the amount of data and rate of data creation in the world is now increasing at an unprecedented level. These less structured huge amount of data acquired from non-traditional sources are called Big Data. One can argue that ‘Big Data’ is just a rebranding of Analytics, but Big Data goes beyond Analytics in terms of technological requirements and applications.

Wikipedia defines big data as:

In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage, search, sharing, analysis, and visualization.

This infographic explains how big is Big Data.

How BIG is Big Data?

Why Big Data is Important?

For last couple of years, Big Data has entered the mainstream marketing and suddenly everyone seems to get hyped about it. All the predictions and expert opinions about Big Data actually supporting this hype. Big Data is a real breakthrough for a number of businesses across various industries. Big Data, as an industry itself, is likely to experience phenomenal growth in the coming years as it is poised to grow to a $25 billion by 2015 and a $50 billion industry by 2017. This explosion of data and analysis of these large datasets or “big data” has become crucial to innovate, compete and get an edge over the competition.

 

Use-cases of Big Data

The ability to process much larger volumes of information enables the identification, and increased certainty, of trends, exceptions and other facts. In some cases, real-time processing makes a difference; computer trading, fraud detection and point of sale transactions to name a few.

In fact, the recent large advances in speech recognition are a result of having access to sufficiently large corpora. The software now has a much higher likelihood of finding the matches for usage, semantics and pronunciation needed to understand a speaker.

Big Data has opened up new opportunities for businesses. This new ability to process large volumes of data enables the companies to identify trends, exceptions and other usable facts in real-time. Real time processing is making a significant difference in terms of trading, fraud detection and point of sale transactions. In short, Big Data technology is allowing enterprises to find answers to questions they didn’t even know to ask. There are a number of identified use-cases for Big Data which enterprises like Google, Facebook, Amazon and Linkedin are applying everyday which are listed below. These are, of course, just a sampling of Big Data use cases. In fact, the most compelling use case at any given enterprise may be as yet undiscovered. Such is the promise of Big Data.

  • Recommendation Engine: Several web properties and online retailers are using Big Data to match and recommend products, services and people to the users on the basis of analysis of user profile and behavioral data. LinkedIn applies this approach for its ‘People You May Know’ feature, whereas Amazon applies it to suggest related products to purchase.
  • Sentiment Analysis: With advanced text analytics tools, now companies can analyze the unstructured text from social media posts including Tweets and Facebook posts to determine the macro level user sentiment related to particular brand or company.
  • Risk Modeling: Financial organizations, especially banks are using Big Data warehouses to analyze large volumes of transaction data to determine risk and exposure of financial assets and getting prepared for potential what-if scenarios on the basis of simulated market behavior.
  • Fraud Detection: Big Data analysis combined with customer behavior and historical transaction data is helping Credit card companies to identify fraudulent activities. These companies can use Big Data analysis approach to detect irregular transactional behavior that indicates a high likelihood of a stolen card.
  • Marketing Campaign Analysis: Big Data is facilitating marketing teams to incorporate higher volumes of increasingly granular data, like click-stream data and call detail records, to increase the accuracy of marketing campaign analysis.
  • Customer Churn Analysis: Enterprises use Big Data technologies to analyse customer behavior data to identify patterns that indicate which customers are most likely to leave for a competing company. On the basis of such analysis, they can take proper strategic action to save the most profitable of these customer segment.
  • Social Graph Analysis: Social networking data mining has opened up many new avenues for enterprises. Now companies can identify which users are most influential over others inside the social networks, so that they can plan social media marketing strategy accordingly.
  • Customer Experience Analytics: Consumer-facing enterprises use Big Data technologies to integrate data from previously siloed customer interaction channels such as call centers, online chat, Twitter, etc. to gain a complete view of the customer experience. This enables enterprises to understand the impact one customer interaction channel has on another in order to optimize the entire customer life cycle experience.
  • Network Monitoring: Different Big Data technologies are used to ingest, analyze and display data collected from servers, storage devices and other IT hardware to allow administrators to monitor network activity and diagnose bottlenecks and other issues. This type of analysis can also be applied to other forms of networks, include transportation networks to improve fuel efficiency.
  • Research And Development: Enterprises, such as pharmaceutical manufacturers, use Big Data technologies to comb through enormous volumes of text-based research and other historical data to assist in the development of new products.

Is Big Data Worth the Hype?

Considering all the astounding applications of Big data, we can only imagine that more will be invented that we cannot even imagine at this point since it is only the beginning of Big Data technologies. The Big Data market is surely one of the most lucrative business sectors for future. In all considerations, Big Data is worth the hype it is currently enjoying. Now, small scale businesses are also increasingly starting to develop strategies to use Big Data technologies. Want to know more? Check out the very interesting TEDx talk video on Big Data.

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