These days it’s tough to walk out of any meeting with Business or IT organizations without touching the topic of Big Data or Analytics. Lot of people struggle with what this is – everyone BELIEVES that it can help if done rightly. But what is it?
The way as I look it: As organizations mature on their Business Intelligence capability, the questions they ask mature too. It’s not about only looking at what the data tells about problems you need to solve. But can data tell you to THINK OF NEW PROBLEMS that you can solve. Things you didn’t know. THINK of something different. Organizations are faced with ever increasing business challenges: Driving new sources of growth, Cost management and cash conservation, Increased business complexity and the need for operational excellence, or Business restructuring in an increasingly global business environment. Ubiquitous computing and technology capabilities have increased dramatically the volume of data at companies’ disposal, yet there remains little in the way of actionable insights (Big Data). Companies need timely, in-depth actionable insights if they are to remain competitive globally to effect a “whole business” approach to big data analytics to deliver business results. Analytics-driven optimization of key business processes
- Staking out distinctive market strategy (CRM Strategy and Loyalty programs)
- Finding the best customers, and charging them the right price (Revenue Management )
- Minimizing inventory and maximizing availability in supply chains (Inventory Optimization)
- Understanding and managing financial performance (Forecasting)
Business Intelligence technologies are deductive in nature validating the hypotheses of the business problems you want to solve. Examples:
- Product shortage by market
- Vendor spend by category
- Brand health by market
- Periodic trend analysis
- Periodic P&L and Financial Reports
Predictive Analytics is Inductive in nature – pull out meaningful relationships and patterns and tells you of different things that might be addressing the same or new problems. Example:
- Business Mix Optimization (Product, Geography, etc.)
- Price sensitivity by consumer segment
- Customer Behavior Modeling
- Performance/profitability analysis
As an example, NETFLIX, a US movie delivery company, asked engineers and scientists around the world to solve what might have seemed like a simple problem: improve Netflix’s ability to predict what movies users would like by a modest 10%. From $5 million revenue in 1999 reached $4.3 billion revenue in 2013 as a result of becoming an analytics competitor. By analyzing customer behavior and buying patterns created a recommendation engine which optimizes both customer tastes and inventory condition.
As another example, Analyzing Love: Data Mining on Match.com in AllAnalytics, Match.com, online dating service, tries to predict the likelihood of attractions between people. 95% of relationship can be predicted by analyzing as few as 10 characteristics in each profile. The find things like:
- Members with accounts on Twitter, which only allows for messages of no more than 140 characters, have shorter relationships.
- People identifying themselves as Republicans are more willing to connect with Democrats than the reverse