Techaisle Blog

The Big Data Analytics' promise: enable “data monetization” through timelier, more accurate, more complete, more granular, more frequent decisions. So, what exactly are the types of business problems big data analytics likely to solve? For this one may need a mini-MBA in Big Data Use Cases.

First let’s define what makes data Big.

Big Data, Little Data
We live in a world of data: transactions, feedback and real-time interaction with customers, partners, suppliers, and employees. In addition to brick, click and mobile transactions, the new variable in the mix is Human generated data – explosive growth of blogs/reviews/messages/emails/pictures. Social graphs such as product recommendations based on circle of friends, jobs you may like, products you have looked at, people who are your contacts etc. also create “second order” data that can be mined for sentiment analytics on products or companies or fact discovery.

Another new variable is computer generated data. Computers generate data as byproduct of interacting with people or with other devices. More the interactions, more is the data and this data comes in a variety of formats from semi-structured log files to unstructured binaries. This “exhaust fumes” of data can be extremely valuable. It can be used to understand and track application or service behavior so that one can find patterns, errors or sub-optimal user experience. One can mine it for statistical patterns and correlations to generate insights.

However, if one listen to the hype, companies can harness this information learn faster, make better decisions, and stay one step ahead of their competitors. Unfortunately, harnessing big data (and separating the signal-from-noise) is trickier than it looks. It takes a lot of skill and superb understanding of use cases.

Big Data Use Cases
The key to exploiting Big Data Analytics is focusing on a compelling business opportunity as defined by a use case — What (What exactly are we trying to do?). Use cases are emerging in a variety of industries that illustrate different core competencies around analytics.

E-tailing/E-Commerce – Online Retailing Use Cases

• Recommendation engines


• Cross-channel analytics


• Event analytics


• Right offer at the right time

Retail/Consumer Use Cases

• Merchandizing and market basket analysis


• Campaign management and customer loyalty programs


• Supply-chain management and analytics


• Event- and behavior-based targeting


• Market and consumer segmentations

Financial Services Use Cases

• Compliance and regulatory reporting


• Risk analysis and management


• Fraud detection and security analytics


• CRM and customer loyalty programs


• Credit risk, scoring and analysis


• High speed Arbitrage trading


• Trade surveillance


• Abnormal trading pattern analysis

Web & Digital Media Services Use Cases

• Large-scale clickstream analytics


• Ad targeting, analysis, forecasting and optimization


• Abuse and click-fraud prevention


• Social graph analysis and profile segmentation


• Campaign management and loyalty programs

New Applications

• Sentiment Analytics


• Mashups – Mobile User Location + Precision Targeting


• Machine-generated data, the exhaust fumes of the Web

Health & Life Sciences Use Cases

• Health Insurance fraud detection


• Campaign and sales program optimization


• Brand management


• Patient care quality and program analysis


• Supply-chain management


• Drug discovery and development analysis

Telecommunications Use Cases

• Revenue assurance and price optimization


• Customer churn prevention


• Campaign management and customer loyalty


• Call Detail Record (CDR) analysis


• Network performance and optimization


• Mobile User Location analysis

So, What’s the Big Deal?

The big deal is that if analytics is done well there is room for margin expansion and additional profit.

Shirish Netke
(Republished with permission)