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Predictive vs Prescriptive Analytics

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Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.

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Predictive vs Prescriptive Analytics

  1. 1. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 1 Unlock Potential William McKnight McKnight Consulting Group Predictive vs Prescriptive Analytics @williammcknight
  2. 2. Looker Overview Elena Rowell Sr. Product Marketing Manager
  3. 3. 1 https://emtemp.gcom.cloud/ngw/globalassets/en/information-technology/documents/trends/gartner-2019-cio-agenda-key-takeaways.pdf Digital-fueled Growth is the Top Investment Priority For Technology Leaders.1 Rebalance your technology portfolio toward digital transformation Percent of respondents increasing investment Percent of respondents decreasing investment Cyber/information security 40%1% Cloud services or solutions (Saas, Paa5, etc.) 33%2% Core system improvements/transformation 31%10% How to implement product-centric delivery by percentage of respondents DigitalTransformation Business Intelligence or data analytics solution 45%1%
  4. 4. 1 https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848 Insights-driven business harness and implement digital insights strategically and at scale to drive growth and create differentiating experiences, products, and services.1 7x Faster growth than global GDP 30% Growth or more using advanced analytics in a transformational way 2.3x More likely to succeed during disruption
  5. 5. 1 in 2 customers integrate insights/experiences beyond Looker 2000+ Customers 5000+ Developers 800+ Employees Santa Cruz San Francisco New YorkChicago Boulder Tokyo Dublin London Empower people with the smarter use of data
  6. 6. Technology Layers Built on the cloud strategy of your choice In-database architecture Semantic modeling layer ‘API-first’ extensibility
  7. 7. Unified Data Platform Governed metrics | Best-in-class APIs | In-database | Git version-control | Security | Cloud Fully custom application to 1M merchants for granular drillable analytics at scale Productize self-serve analytics at scale Best-in-class in-app analytics to compete upmarket and achieve net new growth Monetize your data to drive new growth Build better data products Data Lake Modernize business intelligence Consolidated customer data - from the web, apps, print, and more - for a 360-degree view of customers Deliver best-in-class Business Intelligence Expand customer base with proactive communications from sales and customer success Tailor data experiences for any department Smarter customer acquisition with dynamic AI-powered bid engine Fully automate optimization in real-time Increase trust and revenue by delivering a more transparent experience to their clients Align your company behind data Infuse workflows with data
  8. 8. ©Looker2018.Confidential “Data allows us to better interact and connect with our customers.” Kate Caputo Senior Manager, Business Intelligence
  9. 9. Resources: https://info.looker.com/ Blog: https://looker.com/blog Upcoming Events: https://looker.com/events Request a personal demo: https://looker.com/demo Email us: hello@looker.com JOIN 2019: https://looker.com/events/join-2019 Thank You! © 2019 Looker. All rights reserved. Confidential.
  10. 10. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 2 William McKnight President, McKnight Consulting Group Frequent keynote speaker and trainer internationally Consulted to Pfizer, Scotiabank, Fidelity, TD Ameritrade, Teva Pharmaceuticals, Verizon and many other Global 1000 companies Hundreds of articles, blogs and white papers in publication Focused on delivering business value and solving business problems utilizing proven, streamlined approaches to information management Former Database Engineer, Fortune 50 Information Technology executive and Ernst & Young Entrepreneur of the Year Finalist Owner/consultant: Data strategy and implementation consulting firm 2
  11. 11. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 3 McKnight Consulting Group Offerings Strategy Training Strategy ▪ Trusted Advisor ▪ Action Plans ▪ Roadmaps ▪ Tool Selections ▪ Program Management Training ▪ Classes ▪ Workshops Implementation ▪ Data/Data Warehousing/Business Intelligence/Analytics ▪ Master Data Management ▪ Governance/Quality ▪ Big Data Implementation
  12. 12. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 4 Analytics is Moving
  13. 13. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 5 Analytics Have Evolved • From Business Initiative to business imperative
  14. 14. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 6 • Business Intelligence Rearview mirror showing what happened • Predictive Analytics Tells you what is going to happen Real-time Summaries of data • Technology is different • Questions are different What is the difference between business intelligence and predictive analytics?
  15. 15. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 7 Analytics Formed from SUMMARIES of data i.e., Customer Segmentation and Profit Tied to Business Actions Continual Re-evaluation Adding Big Data!
  16. 16. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 8 The Future Trusted knowledge of an accurate future is undoubtedly the most useful knowledge to have That future is one that you would want to intervene into and tune to your preference Analytics is the deep systematic examination of a company's information Analytics are key to predicting the future
  17. 17. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 9 Analytics Examples Number of customers in each customer state (optionally by product or multiple products) Average balance of customers by geo Average start date in each customer lifetime value decile by geo and device New Number of customers in each state Propensity to churn by age band and device Cost of acquisition by age and gender Average session duration by cost of acquisition Session duration differences between first and tenth session Network with highest up time last month Number of calls per session Best performing ad network by day part in a geo, age band and device And on and on and on and on….
  18. 18. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 10 Big data + analytics = big value Personalized recommendations based on history Best time to buy; average fare by airline, date & market Customized energy management for customers Proactive health insurance that identifies at-risk patients Optimize the siting of wind turbines by mining larger volumes of data Analyzes data from viral “listening posts” to prevent pandemics Custom auto premiums based on actual driving habits via sensors
  19. 19. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 11 Commodity Purchasing Application Example Streaming Data Solution • Business relies upon a critical commodity. • There are multiple suppliers of this commodity. • The goal is to always buy from the optimal supplier. • Considerations – Quality/condition of the commodity – Minimization of risk – Supply and Demand – Storage cost and availability – Impact of weather on supply Chain
  20. 20. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 12 Children’s Hospital Monitoring Premature Infants in the ICU • Correlating blood oxygenation with blood pressure to predict “Baby crashing” • Infection Prediction – Monitoring heart rate variability with other information to predict sepsis – Up to 24 hours earlier than experienced ICU Nurses
  21. 21. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 13 Analytics in Action Prescriptive Analytics
  22. 22. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 14 • More data, more predictions • Go back in time • No guarantee of 100% correct prediction (and that’s OK!) • Getting “a little better” can mean a lot to the business Las Vegas is built on “51%” How much can predictive analytics truly predict?
  23. 23. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 15 Prescriptive Analytics Topics Real-Time Analytics Artificial Intelligence Data Architecture Self-Service
  24. 24. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 16 Data Ready For Analysis Value Action Time ValueLost Analysis Latency Action Time Capture Latency Business Event Taken Decision Latency The Time-Value Curve How does the business value change through time? Richard Hackathorn, Bolder Technology, Inc.
  25. 25. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 17 AI Enhances Analytics Artificial Intelligence is key to Predicting the future Intervening into that future Deeper analytics Self-service data discovery Intelligent recommendation of new data AI to cluster data (i.e., photo tagging) 17
  26. 26. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 18 Enhance in-car navigation using computer vision Reduce cost of handling misplaced items improve call center experiences with chatbots Improve financial fraud detection and reduce costly false positives Automate paper-based, human-intensive process and reduce Document Verification Predict flight delays based on maintenance records and past flights, in order reduce cost associated with delays AI-Based Analytics in Action
  27. 27. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 19 Get Data Under Management 19 In a leveragable platform In an appropriate platform For the data For the usage Used effectively by multiple business groups High NFRs Availability, performance, scalability, stability, durability, secure Granular capture Data at data quality standard As defined by Data Governance
  28. 28. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 20 Analytical Workflows Analytical database (DW) Source Systems Analytical tools “Capture all data” Extract, transform, load “Capture analytic structured data” Explore data Report and mine data Data Lake
  29. 29. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 21 Self-Service: Four Key Objectives Make it easy to access data Make solutions fast to deploy & easy to manage Make tools easy to use Make results easy to consume & enhance Self-Service
  30. 30. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 22 Tools Fit For Self-Service Analytics • They work with the heterogenous data stores necessary today, both SQL and NoSQL • They provide data virtualization functions for the many distributed queries necessary • They accept the results of and participate in data governance • They provide secure data access • They provide collaboration functions that enhance the use of data • They can be up and running quickly and can pivot with agility
  31. 31. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 23 Analytics Manager (mid-level maturity) Analytics Strategy Analytics Architecture Analytics Modeling Analytics Processes Analytics Ethics Data scientist on staff less than 6 months. Concerted efforts to plan the analytics that will benefit the company. Basic understanding of analytic architecture. Data architecture is satisfying non-analytic demand adequately but still imperfect and misunderstood. “Black box” models where processes not completely understood and harbor bias. Acceptance of unstable input signals. Analytic systems with mixed signals make improvement cumbersome. Models are dependent on other models. Models have prediction bias. Amateurish development, where the systems are not developed by analytic professionals and unintended consequences result. Improving a model or signal can degrade other models. No central knowledge of all model usage. Not considering analytics ethics.
  32. 32. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 24 Analytics Operator (high-level maturity) Analytics Strategy Analytics Architecture Analytics Modeling Analytics Processes Analytics Ethics Multiple data scientists on staff. New team members brought up to speed in weeks, not quarters. Analytics contributions to all major projects is considered. Central catalog to track all models along their lifecycle. Enterprise data is cataloged, accessible, well- performing and managed. Hard to make manual errors. Logic within analytics is transparent. Model expansion in the enterprise. Output from analytics is predictable and consistent, with auditable outcomes. Models are reproducible. Unused and redundant settings are detectable. Access restrictions applied to models. Data is tested for model applicability. Easy to specify a configuration as a small change from a previous configuration. Analytic applications monitored for operational issues. Production analytic flow includes packaging, deployment, serving and monitoring. Scoring runs on a periodic basis. Good faith attempts to remove biased variables from models. Potential for malicious use of analytics considered in analytics lifecycle.
  33. 33. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 25 Moving Forward with Prescriptive Analytics Advance these four high-value initiatives 1 2 3 4 Grow, retain and satisfy customers Increase operational efficiency Transform financial processes Manage risk, fraud & regulatory compliance Examples: • Churn management • Social media sentiment analysis • Propensity to buy/next best action • Predictive maintenance • Supply chain optimization • Claims optimization • Rolling plan, forecast and budget • Financial close process automation • Real-time dashboards • Operational and financial risk visibility • Policy and compliance simplification • Real-time fraud identification
  34. 34. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 26 Top 6 Considerations for Taking Advantage of Prescriptive Analytics 1. Simplify a Data Environment that Includes Big Data 2. Data Virtualization 3. Data Governance 4. Collaboration Functions 5. Shorten Time-to-Value 6. Self-Service
  35. 35. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 27 Challenges to Prescriptive Analytics Requirements for success • Access to diverse, massive-scale data - Incorporation of non-relational data with relational data - Direct access to full data sets, not limited to just samples - Immediate access to fresh data without complex data pipelines • Ability to apply diverse analytics at scale - Analysis co-located with data - Flexibility to apply diverse analysis to diverse data - Access to broad variety of languages • Enabling on-the-fly exploration and analysis - Tools to accelerate development and testing - Performance and scalability to support rapid, iterative analysis - Enable easy reuse across multiple use cases
  36. 36. Copyright © 2019 McKnight Consulting Group, LLC All Rights Reserved – Confidential and Proprietary Slide 28 Unlock Potential William McKnight McKnight Consulting Group Predictive vs Prescriptive Analytics @williammcknight

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