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Turning Big Data to Business Advantage


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Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more:

Turning Big Data to Business Advantage

  1. 1. Mohan SawhneyMcCormick Tribune Professor of TechnologyKellogg School of Managementmohans@kellogg.northwestern.eduBig Data Analytics 2012 - ChicagoJune 28, 2012
  2. 2. 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?
  4. 4. Understanding Big DataRelating Big Data to Business AdvantageIndustry Use Cases for Big DataPutting Big Data to Work for you
  5. 5. The technologies and practices of handlingstructured and unstructured datasets solarge, diverse and dynamic that they cannotbe processed and analyzed with existing datamanagement systems.
  6. 6. Data moves from structured to unstructuredSources of data proliferateReal-time creates too much informationQuantity does not trump qualityData becomes contextual based on roles,processes, location, time, and relationships.
  7. 7. The “what” is shifting from “transactionprocessing” to “interaction processing” withsocial media services like Facebook, Twitter andLinkedIn.The “how” of computing is adapting fromdesktop computers to context and location-aware mobile devices.The “where” is moving from on-premisecomputing to cloud computing
  8. 8. Zettabytes VolumeSemi-structured Streaming
  9. 9. E-Business ERP SuiteFunctionalSystems Extended EDW Data Warehouse Data Marts
  10. 10. Understanding Big DataRelating Big Data to Business AdvantageIndustry Use Cases for Big DataPutting Big Data to Work for you
  11. 11. • 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
  12. 12. • 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.
  13. 13. Unstructured Data Embedded CPUs Quality Embedded Extended Part Failure Sensors Data Warehouse Performance AnalysisStructured Data CRM Systems Safety Airbag data Dealer Crash data Systems Product Design Systems
  14. 14. Faster decisions are enabled because big datasolutions support the rapid analysis of highvolumes of detailed data.Analysis at this scale is been difficult to datebecause it takes too long or is too costlyTraditionally, enterprises have had to aggregateor sample the detailed data before it can beanalyzed, which adds to data latency andreduces value of the results.
  15. 15. Faster time to value is possible becauseorganizations can now process and analyze datathat is outside of the enterprise datawarehouse.Enterprises can integrate large volumes ofmachine-generated data from sensors andsystem and web logs into the enterprise datawarehouse for analysis.
  16. 16. Function Big Data ApplicationMarketing • Cross-selling • Location-based advertising • In-store behavior analysis • Customer micro-segmentation • Sentiment analysis • Attribution analysisMerchandising • Assortment optimization • Pricing optimization • Placement and design optimizationOperations • Performance transparency • Labor inputs optimizationSupply Chain • Inventory management • Logistics optimizationNew Business Models • Price comparison services • Web-based markets • Usage and location-based pricing
  17. 17. Analyze performance variationOperations and Enable automated decision making Finance Optimize operations Detect and reduce fraud Discover customer insightsMarketing and Predict customer behavior Sales Optimize marketing campaign ROI Fine-tune customer segmentation Analyze product performance Optimize product features Product Develop personalized offeringsDevelopment Innovate business models
  18. 18. LinkedIn uses data from its more than 100 million usersto build new social products based on users’ owndefinitions of their skill sets.Silver Spring Networks deploys smart, two-way powergrids for its utility customers that allow homeowners tosend information back to utilities to help manageenergy use and maximize efficiency.The Camden Coalition mapped the city’s crime trendsto identify problems with its healthcare system,revealing services that were both medically ineffectiveand expensive.
  19. 19. 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 habitsTravel: Optimize buying experience through web log and socialmedia 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 dataGaming: Collect gaming data to optimize spend within andacross 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”
  20. 20. Target analyzed its baby-shower registry to observechanges in shopping habits changed as a womanapproached her due date.Target analysts found interesting patterns. For instance,women buy larger quantities of unscented lotionaround the beginning of their second trimester. In thefirst 20 weeks, pregnant women buy supplements likecalcium, magnesium and zinc. They also buy handsanitizers and washcloths close to their due date.Target identified 25 products that, when analyzedtogether, allowed them to assign each shopper a“pregnancy prediction” score and an estimated duedate. Target can target women at very specific stages ofa woman’s pregnancy.Target can also optimize the purchase funnel fromemailed coupons to online buying and store visits.
  21. 21. Understanding Big DataRelating Big Data to Business AdvantageIndustry Use Cases for Big DataPutting Big Data to Work for you
  22. 22. • 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
  23. 23. Merchandizing and market basket analysis.Campaign management and customer loyaltyprograms.Supply-chain management and analytics.Event- and behavior-based targeting.Market and consumer segmentations.
  24. 24. Customer Experience Optimization: Deliver consistent cross-channel customer experiences; harvest customer leads fromsales, marketing, and other sourcesIncrease basket size: Increase average order size byrecommending complementary products based on predictiveanalysis for cross-selling.Cross-channel Analytics: Sales attribution, average order value,lifetime valueEvent Analytics: What series of steps (golden path) led to adesired outcome (e.g., purchase, registration).Next Best Offer: Deploy predictive models in combinationwith recommendation engines that drive automated next bestoffers and tailored interactions across multiple interactionchannels.
  25. 25. Compliance and regulatory reportingRisk analysis and managementFraud detection and security analyticsCRM and customer loyalty programsCredit risk, scoring and analysisHigh speed Arbitrage tradingTrade surveillanceAbnormal trading pattern analysis
  26. 26. Threat detection: Federal law enforcementagencies monitor threat (or criminal) behaviorsand communications in order to raiseawareness of interdiction opportunities whilealso exposing non-obvious relationshipsbetween terrorist actors/agentsInfrastructure Threats: As utilities in the U.S.add information technology to their grids, newthreats are emerging. Efficiency is also makingthe grid even more vulnerable to securityconcerns as the grid could be hacked
  27. 27. Understanding Big DataRelating Big Data to Business AdvantageIndustry Use Cases for Big DataPutting Big Data to Work for you
  28. 28. What are the questions that need to be asked?What are the answers that help us move fromdata to decisions?Can we shift insight into action?How do we tie information to business process?Who needs what information at what righttime?How often should this information be updated,delivered, and shared?
  29. 29. 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 modelsEmpower Challenge the team with creating measurable impact Provide the team with support of senior management. Protect the team when it runs into resistance
  30. 30. Big Data is characterized by volume, variety andvelocity Big Data analytics “extends” the DataWarehouse with new data types and newanalytics techniquesBig Data creates business advantage throughsmarter, faster decisions and faster time to valueBig Data should be leveraged with a clearunderstanding of business use casesBig Data teams should combine creativity andanalytics