BIG DATA MBA. Understanding How Big Data and Data Science Drive Data Monetization

Transcription

1 BIG DATA MBA Understanding How Big Data and Data Science Drive Data Monetization Bill Schmarzo, CTO IoT and Analytics, Hitachi Vantara University San Francisco, School Of Management Executive Fellow 1

2 Organizations do not need a big data strategy; they need a business strategy that incorporates big data. Bill Schmarzo, CTO IoT and Analytics, Hitachi Vantara University San Francisco, School Of Management Executive Fellow

3 Big Data Business Model Maturity Index Key Business Processes How Effective is Your Organization at Leveraging Data and Analytics to Power your Business? BUSINESS METAMORPHOSIS Big Data Economics INSIGHTS MONETIZATION BUSINESS MONITORING BUSINESS INSIGHTS BUSINESS OPTIMIZATION Prescriptive Recommendations

4 Digital Transformation: Start with the Business BUSINESS INITIATIVE STAKEHOLDERS KEY DECISIONS PREDICTIVE ANALYTICS DATA BIG DATA ARCHITECTURE & TECHNOLOGIES

5 What is Big Data and Why is it Important? Big Data is the Fuel for Data Monetization

6 Every Two Days We Create As Much Information As We Did From The Dawn Of Civilization Until Eric Schmidt, Google CEO A Billion People Visit Facebook Every Day - Business Insider By 2018, Leading Enterprises Will Support 1,000 10,000 TIMES More Customer Touch Points 6 - IDC 6

7 Internet of Things (IOT) Causing Data Explosion 30 Billion CONNECTED DEVICES 44 Zettabytes OF DATA Source: Gartner Group, Billion CONNECTED PEOPLE Millions OF NEW BUSINESSES

8 Customer Experience Changing Customer Expectations Recommends movies, restaurants, friends, spouses, books, routes, etc.

9 Big Data Isn t About Big it s About Small AFFINITIES INCLINATIONS PROPENSITIES PASSIONS INTERESTS BIASES ASSOCIATIONS AFFILIATIONS TASTES PREFERENCES BEHAVIORS TENDENCIES Source: Me, Myself and Digital Twins, University of San Francisco School of Management Big Data MBA

10 Big Data isn t about Big it s about Small AFFINITIES PROPENSITIES INCLINATIONS AFFILIATIONS BIASES PREFERENCES PATTERNS TRENDS TENDENCIES BEHAVIORS

11 Demystifying Data Science Data Science: The Data Monetization Engine

12 What is Data Science? Data Science is about identifying those variables and metrics that might be better predictors of performance

13 Analytics Value Chain Learning to Think Like a Data Scientist Descriptive Questions (What happened?) What were revenues and profits last year? How much fertilizer did I use last planting season? How much downtime did I have last month due to unplanned equipment maintenance? How many workers did I use last month? Predictive Analytics (What is likely to happen?) What will revenues and profits be next year? How much fertilizer will I need next planting season? When will my equipment need maintenance next month? How many workers will I need next month and when will I need them? Prescriptive Actions (What should we do?) Plant X and Y crops across N acres Pre-order X amount of fertilizer at 5% discount Service your harvester and tractor #2 in January Hire X number of workers for Y days

14 Business Intelligence Engagement Process Step 1: Pre-build data schema (schema-on-load) Step 2: Define question to be answered (queries) Step 3: Use Business Intelligence (BI) tool s graphical user interface (GUI) to construct query) Step 4: BI tool creates SQL Step 5: SQL is run against data warehouse to create report DW

15 Data Science Engagement Process Supports rapid exploration, rapid testing, continuous learning Scientific Method Step 1: Define Hypothesis () to test or Prediction to make Step 2: Gather data and more data (Data Lake: SQL + Hadoop) Historical Kronos Epic Lawson Weather Forecast CDC Google Trends Physician Notes Local Events Step 3: Prepare data; Build schema (schema-on-query) REPEAT Step 4: Visualize the data (Tableau, Spotfire, ggplot2, ) Step 5: Build analytic models (SAS, R, MADlib, Mahout, ) Step 6: Evaluate model goodness of fit (coefficients, confidence levels) Source: Scientific Method: Embrace the Art of Failure, University of San Francisco School of Management Big Data MBA

16 Analytics Level 1: Insights and Foresights Level 1 Advanced Analytics quantify cause-and-effect (strength of relationships) and goodness of fit (model accuracy) Statistics are used to support hypothesis (decision) testing and provide credibility to model outcomes (confidence levels, p-values) Predictive Analytics and Data Mining include anomaly detection, clustering, classification, regression and association rule learning Predictive analytics and data mining uncover statistically significant patterns and relationships in large data sets to quantify risks and opportunities Graph Analytics to Identify Relationships Regression to Quantify Cause-and- Effect

17 Analytics Level 2: Augment Human -making Level 2 Advanced Analytics prescribe actions (recommendations) to improve human decision-making Clustering to Codify Similarities Deep Learning (Neural Networks) recognizes things out of complex data formats - images, photos, voice, audio, video, text, handwriting Machine Learning identifies relationships and patterns in the data, builds models, and predicts things without programming Supervised Machine Learning identifies known unknowns relationships and patterns from labeled outcomes (e.g., fraud, attrition) Unsupervised Machine Learning identifies unknown unknowns relationships and patterns from data with no known outcomes Deep Learning to Identify Things

18 Analytics Level 3: Learning and Intelligent Enterprise Level 3 Advanced Analytics learn and adapt within continuously changing environments (robots, autonomous vehicles) Reinforcement Learning takes actions within controlled environment to maximize rewards while minimizing costs; uses trialand-error to map situations to actions to maximize rewards (Hotter/Colder game) Reinforcement Learning Train Autonomous Vehicles Artificial Intelligence algorithms acquire knowledge about specific environment, apply the knowledge to successfully interact within that environment, and learn from the resulting interactions so that subsequent interactions get more effective Artificial Intelligence to win GO

19 Advanced Analytics Simplified Statistics & Predictive Analytics quantifies cause-and-effect (correlation coefficient) and goodness of fit (Chi-squared test) Deep Learning (Neural Networks) recommends things from complex data formats (images, audio, video, text, handwriting) Supervised Machine Learning identifies known unknowns relationships that drive discrete outcomes (e.g., fraud, attrition, maintenance,) Unsupervised Machine Learning identifies unknown unknowns relationships - clusters, segments, associations hidden in the data Reinforcement Learning & Artificial Intelligence learns and adapts operating within continuously changing environment (e.g., chess, cars)

20 Advanced Analytics Simplified (Even More!) Statistics & Predictive Analytics quantifies cause-and-effect (correlation coefficient) and goodness of fit (Chi-squared test) Deep Learning (Neural Networks) recognizes things from complex data formats such as images, photos, voice, audio, video, text and handwriting Supervised Machine Learning identifies known unknowns relationships that drive labeled outcomes (e.g., fraud, attrition, maintenance) Unsupervised Machine Learning identifies unknown unknowns relationships - clusters, segments, associations hidden in the data Reinforcement Learning & Artificial Intelligence learns and adapts operating within continuously changing environment (e.g., robots, autonomous vehicles, inventory management)

21 Determining Economic Value of Data University of San Francisco Economic Value of Data Research Project

22 Data is an unusual currency. Most currencies exhibit a one-toone transactional relationship. For example, the quantifiable value of a dollar is considered to be finite - it can only be used to buy one item or service at a time, or a person can only do one paid job at a time. But measuring the value of data is not constrained by transactional limitations. In fact, data currency exhibits a network effect, where data can be used at the same time across multiple use cases thereby increasing its value to the organization. This makes data a powerful currency in which to invest. Source: Determining the Economic Value of Data

23 Economic Multiplier Effect: Data Where an increase in spending produces an increase in national income and consumption greater than the initial amount. Every time there is an injection of money into the economy, there is an economic multiplier effect. Sales Marketing Call Center Product Dev Promotional effectiveness Customer acquisition Customer retention New product intro Customer point of sales data +2.5% +2.0% +3.5% +2.6%

24 Intellectual Capital Rubik s Cube Challenges How does the organization determine the economic value of its data in order to drive prioritization and investment decisions? How does the organization avoid data silos, shadow IT spend and unmanaged data proliferation that thwart the potential value of data? How does the organization avoid the disillusionment of orphaned analytics? How do you re-tool the organization to establish a technical and cultural environment for collaborative value creation? How does one leverage assets that appreciate (not depreciate) with usage, and can be used simultaneously across multiple business processes?

25 Intellectual Capital Rubik s Cube Solution USE CASES Clusters of decisions around common subject area in support of organization s key business initiatives DATA Detailed historical transactions coupled with internal unstructured and publiclyavailable data sources ANALYTICS Data transformed into actionable analytic insights (scores, rules, propensities, segments, recommendations)

26 Start with a Business Initiative Chipotle 2012 Annual Report Chipotle Business Initiatives Build people culture that attracts and empowers top performers Grow revenues (up 20.3% in 2012) opening new stores (opened 183 in 2012) Increase comparable restaurant sales growth (7.1% in 2012) Marketing building Chipotle brand and engaging with our customers

27 Identify Use Cases Business initiative: Increase same store sales Increase shopping bag revenue Increase corporate catering Increase noncorporate catering Increase Store Traffic via local events marketing Improve promotional effectiveness Increase Store Traffic via Loyalty program Improve New Product Introduction Effectiveness

28 Map Data Sources to Use Cases Data is the fuel of the modern, intelligent organization an asset to be gathered, enriched and re-used across multiple Use Cases Increase Store Traffic Local Events Increase Store Traffic Loyalty Increase Shopping Bag Revenue Increase Corporate Catering Increase Noncorporate Catering Improve New Product Introductions Improve Promotional Effectiveness Data Sources $62M $56M $26M $24M $14M $18M $27M Point of Sales Market Baskets Store Demographics Local Competition Store Manager Demo Consumer Comments Social Media Weather Local Events Traffic

29 Analytic Profiles Capture Analytics for Re-use Analytic Profiles standardize the collection and re-application of analytics about Business Entities across multiple Use Cases Bill Schmarzo Chipotle Analytic Profile NCE Score Var Trend

30 Analytic Profile: Customer Create Scores that support the s that comprise each Use Case, and store those scores in the Analytic Profile Bill Schmarzo Chipotle Analytic Profile Demographic segments 1.0 NCE Score Var Trend Behavioral segments Loyalty Index Frequency Index Recency Index Use Case #1 Improve campaign effectiveness Use Case #2 Increase customer loyalty Use Case #3 Increase customer store visits Use Case #4 Reduce customer attrition

31 Analytic Profile: Customer Over time as more data is available, the analytics stored in the Analytic Profiles get refined and fine-tuned across multiple use cases Traditional Data Purchases Product Preferences Add-on Preferences Drink Preferences Visit Frequency Visit Recency Visit Monetary Market Basket Group Size Coupons Consumer Comments Store Manager Notes Bill Schmarzo Chipotle Analytic Profile NCE Score Var Trend Demographic segments Behavioral segments Loyalty Index Frequency Index Recency Index Lifetime Value Calc Event Propensity Promotion Propensity Advocacy Propensity Attrition Propensity Non-traditional Data Social Media Posts Home Value Employment history Job Change Frequency Job Change Recency Industry certifications Industry awards Social Media Connections Education degrees Rank of college College donations Volunteer activities Parking tickets

32 Data Lake 3.0: Collaborative Value Creation Platform Improve Campaign Effectiveness Optimize Store Remodeling Increase Customer Loyalty Improve Manager Retention Increase Customer Store Visits DATA LAKE Improve Hiring Effectiveness Reduce Customer Attrition Improve New Product Introductions Increase Customer Advocacy Increase Customer Cross-sell

33 Creating the Intelligent Enterprise

34 Big Data Business Model Maturity Index Key Business Processes How Effective is Your Organization at Leveraging Data and Analytics to Power your Business? BUSINESS METAMORPHOSIS Big Data Economics INSIGHTS MONETIZATION BUSINESS MONITORING BUSINESS INSIGHTS BUSINESS OPTIMIZATION Prescriptive Recommendations

35 Advanced Analytics Drives Business Transformation The Learning & Intelligent Enterprise Self-diagnosis and Self-learning Augmenting Enterprise Intelligence (Reinforcement Learning, Artificial Intelligence, Cognitive Computing) Key Business Processes Measures effectiveness of leveraging data and analytics to power your business models Optimized Human -making Prescribing Actions & Recommendations (Neural Networks, Deep Learning, Machine Learning) Insights and Foresight Quantify Cause-and-Effect Economic (Statistics, Clustering, Classification, Regression) Drivers Predictive Analytics Prescriptive Analytics INSIGHTS MONETIZATION Cognitive Analytics BUSINESS METAMORPHOSIS BUSINESS MONITORING Descriptive Analytics BUSINESS INSIGHTS BUSINESS OPTIMIZATION Prescriptive Recommendations

36 Thank You! BILL SCHMARZO Hitachi Vantara, CTO IoT and Analytics Executive Fellow, University San Francisco School of Management University San Francisco Research: Economics of Data and Analytics Blogs: To Achieve Big Data s Potential, Get It Into The Boardroom Big Data Business Model Maturity Index Big Data For Competitive Differentiation User Experience: the new king of the business How I ve Learned To Stop Worrying And Love The Data Lake Thinking Like A Data Scientist Contact Information bill.schmarzo@hitachivantara.com