The Sentient Enterprise: The Future of Analytics and Business Decision Making

Transcription

1 The Sentient Enterprise: The Future of Analytics and Business Decision Making MOHAN SAWHNEY McCormick Foundation Professor of Technology Kellogg School of Management

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4 57% Important business data is not captured 42% Data is cumbersome and not user-friendly 75% Time wasted from data discrepancies

5 TOWARDS THE SENTIENT ENTERPRISE

6 SHARE IDENTIFY KNOW DISCOVER ANALYZE DESIGN SEARCH INTERACT A COMPANY AS A SINGLE ORGANISM

7 FIVE STAGES

8 PLATFORM DEFINITION OF A A platform does not refer to hardware but rather a capability that is inclusive of people, process, and technology to achieve agility.

9 FIVE STAGES 5 The agile data platform moves traditional central DW structures to a balanced decentralized framework built for agility

10 AGILE DATA PLATFORM AGILITY AT SCALE RETAIN DATA Loosen roadblocks, democratize data, breakdown silos and analyze data at massive scale

11 EMPTY SANDBOX 2X DUPLICATED DATA BY USER1 USER2 ANALYZING THE OLD DATA ORIGINAL DATA DATA DUPLICATED IN DATA SILO

12 RECORDI ALL ACTI EFFICIENT SANDBOX Security Governance Privacy Dev Ops Data Management Data Access

13 RECORDING ALL ACTIVITIES Track every touchpoint at which users request and use data

14 LAYERED DATA ARCHITECTURE USERS BUSINESS ANALYSTS POWER USERS DATA SCIENTISTS DATALAB Virtual Sandboxes & Prototypes PRESENTATION Application Specific Views AGGREGATIONAL BU Specific Rollups CALCULATION Key Performance Indicators INTEGRATION Integrated Model at Lowest Granularity STAGING 1:1 Source Systems USER OWNED BUSINESS RULES ATOMIC DATA

15 LAYERED DATA ARCHITECTURE TIGHTLY COUPLED LOOSELY COUPLED NON - COUPLED DATALAB Virtual Sandboxes & Prototypes PRESENTATION Application Specific Views AGGREGATIONAL BU Specific Rollups CALCULATION Key Performance Indicators INTEGRATION Integrated Model at Lowest Granularity STAGING 1:1 Source Systems

16 CHALLENGES OUTDATED ANALYTICS SILOED IT +200 DATA MARTS

17 OUTCOMES One agile data environment Improved decision-making New data-centric culture

18 FIVE STAGES 5 From transactional to behavioral data. Value comes from behaviors rather than transactions

19 BEHAVIORAL DATA PLATFORM BEHAVIOR AND INTERACTIONS UNDERSTAND Use patterns and context in human and machine behavior to predict performance and inform new strategies.

20 TRANSACTIONAL DATA

21 BEHAVIORAL DATA BEHAVIORAL PATTERNS 10 to 100X HIGHER DATA VOLUMES

22 CUSTOMER BEHAVIOR CUSTOMER BEHAVIOR BANK STATEMENT FEE BANK POLICIES -$20 -$25 -$40 -$55 BANK Customer is charged a fee Customer contacts the call center Customer searches the web for bank policies Account transactions declined Customer interacts with the bank in person Customer cancels his account and leaves the bank

23 MACHINE BEHAVIOR CUSTOMER BEHAVIOR AIR DENSITY & AIR TEMPERATURE Low level short duration turbine directional changes Medium level turbine directional changes Highest turbine directional changes SQUALLS WINDS GUSTS

24 MACHINE BEHAVIOR CUSTOMER BEHAVIOR AIR DENSITY & AIR TEMPERATURE Low level short duration turbine directional changes The most turbine directional Medium level changes turbine directional changes WINDS SQUALLS GUSTS Sensor data that is collected from the wind parks Wind power capacity Wind speed at hubs Gearbox readings Hydraulic measurements Turbine brakes quality All readings are analyzed to get the optimal pitch on the blades

25 MACHINE BEHAVIOR CUSTOMER BEHAVIOR CUSTOMER BEHAVIOR MACHINE BEHAVIOR SANDBOX SANDBOX SANDBOX SANDBOX CUSTOMER BEHAVIOR SANDBOX CUSTOMER BEHAVIOR SANDBOX MACHINE BEHAVIOR CUSTOMER BEHAVIOR

26 CHALLENGES STAY RELEVANT UNDERSTAND BEHAVIOR PROTECT REVENUE EROSION

27 OUTCOMES Predictive modeling for customer churn Data-driven pricing campaigns Avoided millions in loss

28 FIVE STAGES 5 LinkedIn for analytics. From centralized metadata to crowd-sourced collaboration. Social interactions connect the data within the enterprise

29 COLLABORATIVE IDEATION PLATFORM INNOVATE AND WORK TOGETHER Foster an analytics environment where many different people can collaborate on data and share ideas

30 Intelligent Answers

31 Query Suggestions

32 Collaboration

33 Storytelling

34 FIVE STAGES Analytical apps. From static applications and ETL to agile self-service apps. From extraction of data to enterprise listening.

35 ANALYTICAL APPLICATION PLATFORM INSIGHT INTO ACTION TURN Bring the app-style economy into the enterprise and give everyone access to analytics they can use right away at scale.

36 apple.com facebook.com

37 STRESS FREE IT

38 APP IDEA FROM A NON-EXPERT app APP FRAMEWORK app APP app DEPLOY HOUR 1 HOUR 2 HOUR 3 HOUR 4 HOUR 5 HOUR 6 HOUR 7 HOUR 8 HOUR 9 HOUR 10

39 app App Platform

40 FIVE STAGES Predictive technologies and algorithms. Decision making with the help of automated algorithms. 1

41 SALES AGGREGATIONS SOCIAL MEDIA AUTOMATION MOBILE INTEGRATION KPIs INTERSECTIONS SEO PRODUCT CATEGORIES 90 SOCIAL MEDIA % TESTING TIME WASTED SIFTING THROUGH DATA OPTIMIZATION Messy Data

42 INTEGRATION Cleansing and refining sources identifying signals and patterns 90 % TIME SPENT ON DECISION MAKING ALGORITHMIC APPS Messy Data Data Presented Simply

43 ALGORITHMS - PATTERN ANALYTICS DECISION MAKING BY HUMANS

44 AUTOMATED CARS Photo: commons.wikimedia.org

45 AUTOMATED TRADING

46 CHALLENGES CREATING NEXT- GEN CAR EXPERIENCE ACHIEVING VISION THROUGH ANALYTICS TAP INTO IoT AND AoT

47 OUTCOMES AI for self-driving cars Pioneered safety innovations Shift towards Transportation-aaS

48 Undetected Problem (Minor) Undetected Problem (Major) Q1 Q2 Q3 Q4

49 Problem Detected (Solved) Q1 Q2 Q3 Q4

50 Autonomous Decisioning Platform 5 Analytical Application Platform Collaborative Innovation Platform 4 3 Problem (Minor) Behavior Data 2 Agile Data Platform Q1 1

51 Crisis Averted Q1 Q2 Q3

52 SHIFTS OF THE FIVE STAGES From Transactional to Behavioral Data. Value comes from behaviors rather than transactions LinkedIn for Analytics. From centralized Meta Data to Crowd Sourced Collaboration. Social interactions connect the data within the enterprise 4 5 The Agile Data Warehouse moves traditional central DW structures to a balanced decentralized framework built for agility Analytical Apps. From static applications and ETL to agile Self Service Apps. From Extraction of Data to Enterprise Listening. Predictive Technologies and Algorithms. From focusing only 10% of time on decision making and 90% of sifting through data to 90% of decision making with the help of automated algorithms

53 Scalable Autonomous SENTIENT ENTERPRISE Evolving Frictionless Proactive

54 Evolution of Business Operations Management Automate selected processes to create embedded products Create new monetization approaches to capture product benefits Optimize automated processes through embedded analytics

55 Embedded Product Management Discover Develop Monetize Identify repeatable patterns across service engagements Triage opportunities for automating selected repeatable processes Develop a prototype for an embedded product that automates a selected process Add analytics and machine learning to the product Evolve towards Robotic Process Automation Create transaction o outcome-based business models Select leading-edge clients to pilot the product and business model Scale across client base

56 Evolving the Business Model Inputs- Based Pricing Transaction- Based Pricing Capturing Benefits from Automation Outcome- Based Pricing Capturing Benefits from Analytics

57 Thank you