turning big data to business advantage

Mohan Sawhney McCormick Tribune Professor of Technology Kellogg School of Management [email protected] Big Data Analytics 2012 - Chicago June 28, 2012

What is Big Data?

What is the Big Deal?

How does Big Data link to business outcomes?

What are the use cases for Big Data?

What can we learn from the Big Data leaders?

WHAT?

SO WHAT?

NOW WHAT?

Understanding Big Data

Relating Big Data to Business Advantage

Industry Use Cases for Big Data

Putting Big Data to Work for you

The technologies and practices of handling structured and unstructured datasets so large, diverse and dynamic that they cannot be processed and analyzed with existing data management systems.

Data moves from structured to unstructured Sources of data proliferate Real-time creates too much information Quantity does not trump quality Data becomes contextual based on roles, processes, location, time, and relationships.

The “what” is shifting from “transaction processing” to “interaction processing” with social media services like Facebook, Twitter and LinkedIn. The “how” of computing is adapting from desktop computers to context and location-aware mobile devices. The “where” is moving from on-premise computing to cloud computing

Volu

me

Semi-structured Streaming

Zettabytes

Data Marts

EDW Extended Data Warehouse

Functional Systems

ERP Suite E-Business

• Big Data is a response to the evolution of the Social, Local and Mobile data-driven enterprise that will be required to sense and respond in “right-time” to events in its ecosystem.

• Big Data leads to business advantage through faster, smarter and more cost-effective decisions

• Big Data’s ultimate business outcome is Agility

• Smarter decision making comes from the ability to combine new sources of data to enhance existing analytics and predictive models in operational systems and data warehouses.

• New insights emerge from synthesis of multi-structured data from sensors, system and web logs, social computing web sites, text documents, etc. that are difficult to process using traditional analytical processing technologies.

Embedded CPUs

Embedded Sensors

CRM Systems

Dealer Systems

Product Design Systems

Quality Part Failure

Performance Analysis

Safety Airbag data Crash data

Unstructured Data

Structured Data

Extended Data Warehouse

Faster decisions are enabled because big data solutions support the rapid analysis of high volumes of detailed data. Analysis at this scale is been difficult to date because it takes too long or is too costly Traditionally, enterprises have had to aggregate or sample the detailed data before it can be analyzed, which adds to data latency and reduces value of the results.

Faster time to value is possible because organizations can now process and analyze data that is outside of the enterprise data warehouse. Enterprises can integrate large volumes of machine-generated data from sensors and system and web logs into the enterprise data warehouse for analysis.

Function Big Data Application

Marketing • Cross-selling • Location-based advertising • In-store behavior analysis • Customer micro-segmentation • Sentiment analysis • Attribution analysis

Merchandising • Assortment optimization • Pricing optimization • Placement and design optimization

Operations • Performance transparency • Labor inputs optimization

Supply Chain • Inventory management • Logistics optimization

New Business Models • Price comparison services • Web-based markets • Usage and location-based pricing

Analyze performance variation Enable automated decision making Optimize operations Detect and reduce fraud

Operations and Finance

Marketing and Sales

Product Development

Analyze product performance Optimize product features Develop personalized offerings Innovate business models

Discover customer insights Predict customer behavior Optimize marketing campaign ROI Fine-tune customer segmentation

LinkedIn uses data from its more than 100 million users to build new social products based on users’ own definitions of their skill sets. Silver Spring Networks deploys smart, two-way power grids for its utility customers that allow homeowners to send information back to utilities to help manage energy use and maximize efficiency. The Camden Coalition mapped the city’s crime trends to identify problems with its healthcare system, revealing services that were both medically ineffective and expensive.

Insurance : Individualize auto-insurance policies based on vehicle telemetry data. More accurate assessments of risks Individualized pricing based on actual individual customer driving habits; Influence and motivate individual customers to improve their driving habits

Travel: Optimize buying experience through web log and social media analysis

Gain insight into customer preferences and desires; Up-sell by correlating current sales with subsequent browsing behavior Increase browse-to-buy conversions via customized offers and packages Personalized travel recommendations based on social media data

Gaming: Collect gaming data to optimize spend within and across games

Gain insight into likes, dislikes and relationships of its users Enhance games to drive customer spend within games Recommend content based on analysis of player connections and similar “likes”

Target analyzed its baby-shower registry to observe changes in shopping habits changed as a woman approached her due date. Target analysts found interesting patterns. For instance, women buy larger quantities of unscented lotion around the beginning of their second trimester. In the first 20 weeks, pregnant women buy supplements like calcium, magnesium and zinc. They also buy hand sanitizers and washcloths close to their due date. Target identified 25 products that, when analyzed together, allowed them to assign each shopper a “pregnancy prediction” score and an estimated due date. Target can target women at very specific stages of a woman’s pregnancy. Target can also optimize the purchase funnel from emailed coupons to online buying and store visits.

• A business use case describes what a technology or product does. It describes the job to be done by end-users to achieve their business goals.

• The business use case describes a process that provides business value to the end-user

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.

Customer Experience Optimization: Deliver consistent cross-channel customer experiences; harvest customer leads from sales, marketing, and other sources Increase basket size: Increase average order size by recommending complementary products based on predictive analysis for cross-selling. Cross-channel Analytics: Sales attribution, average order value, lifetime value Event Analytics: What series of steps (golden path) led to a desired outcome (e.g., purchase, registration). Next Best Offer: Deploy predictive models in combination with recommendation engines that drive automated next best offers and tailored interactions across multiple interaction channels.

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

Threat detection: Federal law enforcement agencies monitor threat (or criminal) behaviors and communications in order to raise awareness of interdiction opportunities while also exposing non-obvious relationships between terrorist actors/agents Infrastructure Threats: As utilities in the U.S. add information technology to their grids, new threats are emerging. Efficiency is also making the grid even more vulnerable to security concerns as the grid could be hacked

What are the questions that need to be asked? What are the answers that help us move from data to decisions? Can we shift insight into action? How do we tie information to business process? Who needs what information at what right time? How often should this information be updated, delivered, and shared?

Educate: Identify people who are both technically adroit and analytically creative. Combine business, analytical and technical expertise Develop the team through training and certifications in Big Data Analytics and Data Science.

Acquire: Bring in individuals from outside your four walls and outside your industry Diversity ensures complementary skills and the ability to challenge existing mental models

Empower Challenge the team with creating measurable impact Provide the team with support of senior management. Protect the team when it runs into resistance

Big Data is characterized by volume, variety and velocity Big Data analytics “extends” the Data Warehouse with new data types and new analytics techniques Big Data creates business advantage through smarter, faster decisions and faster time to value Big Data should be leveraged with a clear understanding of business use cases Big Data teams should combine creativity and analytics

turning big data to business advantage

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