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Predictive Intelligence
The document discusses challenges with managing and analyzing large amounts of data, and how predictive intelligence and "just enough" data can help businesses make better decisions. It covers how most predictions begin with an educated guess, and activities like "straw modeling" and "data by design" that use readily available information and common sense to form initial hypotheses. The key is validating guesses with real data, and designing data collection strategies to focus on the most relevant and actionable sources of information.
Predictive Intelligence
- 1. March 25, 2014 CharlesSayers Experience Innovation SapientNitro csayers@sapient.com predictiveintelligencemaking better business decisions with just enough data
- 2. what we’ll lookat coping with data volume and velocity dashboards: the good, the bad, the ugly the guesswork behind most predictions activity : straw modeling activity : data by design © 2014 SapientNitro
- 3. we’re generating a LOTof data REALITY © 2014 SapientNitro
- 4. % of the datain the world today was created in the last 2 years alone. - IBM - © 2014 SapientNitro
- 5. This is thevolume of photos uploaded to Flikr in a single day © 2014 SapientNitro
- 6. we’re generating too much,too fast PROBLEM © 2014 SapientNitro
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- 8. We are drowningin information and starving for knowledge. - John Naisbitt © 2014 SapientNitro
- 9. innovations in wearables andbiometrics will drive a surge in physiological data © 2014 SapientNitro a new wave of data is on the horizon
- 10. © 2014 SapientNitro 190,000peoplewith deep analytic sills as well as 1.5millionmanagers and analysts will be needed by 2018 to fill jobs in Big Data
- 11. © 2014 SapientNitro wecan only process so many numbers at one time PROBLEM
- 12. © 2014 SapientNitro % structuredandunstructureddata ofanalytics projects will deliver insights based on by 2015, more than
- 13. therearemanydatasolutions tohelpyoumanageandmine © 2014 SapientNitro REPORTING ANALYSIS MONITORING PREDICTION what happened? whydid it happened? what’s happening now? what might happen? predictive analytics dashboards and scorecards OLAP and visualization tools query, reporting and search tools BUSINESS VALUE COMPLEXITY BUSINESS INTELLIGENCE TECHNOLOGIES
- 14. Data types most beneficial for service and sales improvement Data types most beneficial for market and trend analysis Internal Databases External Website Content Social Media Content Internet‐Connected Device Data Location‐based Data Internal Log Files RFID Data Sensor‐Generated Data Competitive Intelligence Customer Segment Data Market Condition Models and Data Sales Results and Forecast Data Internal Customer Data Third Party Market Data Point of Sale Data Combined in an actionable, real‐time context, data can drive better market analysis and improve customer intelligence SOURCE: “Big Dataand the Democratisation of Decision,” Alteryx weneedtousedatatosolve problems,notfindproblems © 2014 SapientNitro
- 15. thevalueofintelligence companies that use predictive intelligence medianROI 145% annual customer retention +6% companies that don’t use predictive intelligence median ROI 89% annual customer retention -1% VS. © 2014 SapientNitro
- 16. trending data patterns of continuous change •sales • conversion • retention • journey dependent variables controllable factors that influence change • marketing • pricing • branding • response kpi’sthatinfluencethefuture behavioral data patterns of contextual interaction • churn • dwell time • engagement • task completion uncontrollable factors that influence change • weather • economy • seasonality • disasters independent variables © 2014 SapientNitro
- 17. what’sthedifference? analytics understanding the factual past intelligence VS.anticipating the near future © 2014 SapientNitro
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- 19. 3 types of dashboards 1.Strategic 2. Operational 3. Analytic © 2014 SapientNitro
- 20. © 2014 SapientNitro strategic simple,concise; contains aggregate metrics representing overall health (Executive Dashboards, Financial Dashboards, Planning Dashboards)
- 21. © 2014 SapientNitro operational monitorreal time operations; track and alert deviations from the “norm” (Network Monitors, Health Monitors, Manufacturing Dashboards)
- 22. © 2014 SapientNitro analytical interdependent,interactive; provide ability to futurecast and test what-if scenarios (Campaign Response, Sales Forecast, Investment Prioritization)
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- 25. © 2014 SapientNitro insight isthe key to predictive intelligence
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- 27. most predictions begin with a guess ©2014 SapientNitro
- 28. predictive guessing © 2014 SapientNitro makingstructured and informed guesses using common sense and insight
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- 33. straw modeling ACTIVITY 1 isabout creating a visual hypothesis based on readily accessible data, common sense and intuition © 2014 SapientNitro
- 34. guestimation worksheet Storesales -15 -10 -5 0 5 10 15 20 25 -10 -50 5 10 Net margin sales margin Abercrombie & Fitch Amazon.com Best Buy The Gap Home Depot JC Penney Macy’s Nordstrom’s Sears Target howwelldidtheseretailersperform? 1 2 3 4 5 6 7 8 9 10 © 2014 SapientNitro
- 35. takeaways © 2014 SapientNitro •“accuracy” is influenced by data we (think) we already have • initial straw models often point to logical starting points • all guesses should be validated by real data • it’s important to know what we (and others) are good at guessing
- 36. data by design ACTIVITY2 allows us to design educated perspectives that point us to relevant data clusters © 2014 SapientNitro
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- 41. PRACTICAL INNOVATIVE EMERGINGEXPERIMENTAL Experimental In-branch experiencesinvolving technologies and approaches that are in the early stages of development and exploration Practical Market-ready solutions already available in- branch to always-on consumers Emerging Promising in-branch experiences that will require greater investment in infrastructure to enable Innovative Future-facing experiences that offer greatest opportunity in-branch differentiation both the long- and short-term readiness value thisisourstraw model © 2014 SapientNitro
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- 43. taking the guesswork outof guesswork FINAL THOUGHTS © 2014 SapientNitro
- 44. 6 characteristics of valuabledata 1. Fast Acquire, analyze and adapt in real-time 2. Centralized Unify data repository and enable universal access 3. Actionable Collect what you can use (not just what you can collect) 4. Diverse Combine multiple data sources – across mode, place, device, time and action 5. Retained Data has value beyond original anticipation. Don’t throw it away! 6. Scalable Plan for exponential growth
- 45. capturethespark Accept the factthat our best guesses will require further refinement and validation. © 2014 SapientNitro
- 46. modelsimply Complexity is confusing.Get to the point. © 2014 SapientNitro
- 47. stayfocused Concentrate on finding actionabledata © 2014 SapientNitro
- 48. getdirty Predicting the futureis a messy business. You won’t call everything right. Maybe some of it will hit the mark. Maybe none of it. It’s always better to err on the side of action, than lose by doing nothing. © 2014 SapientNitro
- 49. avoiddeflation Don’t suck theair out of every prediction. Strive to strengthen the strength of your insights and rationale. © 2014 SapientNitro
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- 51. thank you Charles Sayers ExperienceInnovation SapientNitro csayers@sapient.com March 25, 2014 predictiveintelligence making better business decisions with just enough data