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Advancing the analytics maturity curve at your organization

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Presented at the DCD Mexico 2017. The digital era is characterized by the omnipresence of data and analytics across the value proposition of the organization from being a core offering to an add-on or as a competitive advantage or the optimization support. This has led to an Analytics that is a living & breathing organism, something that grows and changes with time - in the role it plays for the various stakeholders (which changes itself), the forms of delivery, the ownership and finally the size of impact. The "Analytics Maturity Curve" provides a guiding vision and framework for the Analytics programs across the industry. The presentation will focus on the evolution of "Analytics Maturity Curve" itself with time, the need for it, the challenges and finally the lessons learnt during the transition from one phase to another. The success criteria for this presentation is that the audience leaves with a perspective on what differentiates the programs that successfully made the transition and have a best practice checklist to refer to in their own journey.

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Advancing the analytics maturity curve at your organization

  1. 1. Intended for Knowledge Sharing only Advancing the Analytics Maturity in your organization Sep 2017
  2. 2. > México | Ciudad de México Disclaimer: Participation in this summit is purely on personal basis and is not meant to represent VISA’s position on this or any other subject and in any form or matter. The talk is based on learning from work across industries and firms. Care has been taken to ensure no proprietary or work related information of any firm is used in any material. Director, Insights at Visa, Inc. Enable Decision Making at the Executives/ Product/Marketing level via actionable insights derived from Data. RAMKUMAR RAVICHANDRAN
  3. 3. > México | Ciudad de México Intended for Knowledge Sharing only Do these memes ring a bell?
  4. 4. > México | Ciudad de México Intended for Knowledge Sharing only IGNORANCE: I GOT DATA SCIENTISTS FOR PREDICTIONS, JUST GIVE ME MY REPORTS!
  5. 5. > México | Ciudad de México Intended for Knowledge Sharing only IDENTITY CRISIS: REPORTING, DATA ANALYSES, STATISTICS, ALGORITHMS, TESTS???
  6. 6. > México | Ciudad de México Intended for Knowledge Sharing only ACCOUNTABILITY: DATA BAD, ANALYTICS SCREWED UP! NUMBERS UP, “WE” DID IT!
  7. 7. > México | Ciudad de México Intended for Knowledge Sharing only BUDGET 101: COST SHOULD BE KEPT LOW & REVENUE SHOULD BE INCREASED!
  8. 8. > México | Ciudad de México Intended for Knowledge Sharing only AND YEAH - LET’S FIRE ANALYSTS AND GET ARTIFICIAL INTELLIGENCE!!!
  9. 9. > México | Ciudad de México Intended for Knowledge Sharing only Analytics is the bellweather for an organization
  10. 10. > México | Ciudad de México Intended for Knowledge Sharing only ANALYTICS IS A LIVING, BREATHING & GROWING ORGANISM… https://www.intel.com/content/dam/www/public/us/en/documents/guides/analytics-planning-guide.pdf
  11. 11. > México | Ciudad de México Intended for Knowledge Sharing only A TYPICAL EXAMPLE OF A “MATURE” ANALYTICS ORGANIZATION… From Business Operations side • Every major decision has been quantified for impact (expected incremental over run-rate), supported with context (user demand), validated in-market and any historical precedent. • Optimal paths planned out for next ‘x’ moves – Leading indicators monitored and response paths worked out (Stop/Scale/Change). • Delivery model for insights and Response model customized for stakeholders. • Agility of the business to respond to industry events, competitor actions, customer demands highest, since the business drivers and ownership have been modulated and multi variate tested & mapped - Pivoting, new Product/UX, Pricing, campaigns. • A knowledge bank/idea marketplace for employees to quickly prototype, iterate and innovate at scale. From the Analysts side • Analytics is a “Strategy” function not just Support or Product - everyone knows the “why” and “what will happen” and “what if we don’t”. • A driver of Organizational Culture – Accountability/Transparency, Collaboration/Assistance, Innovation/Change Management. • Doesn’t need to justify investment in learning, but everyone knows why Analytics is in their self-interest! • Analytics is so fine-tuned that it can be packaged and sold out as a Product to the industry, e.g., Hive/Google 360! • Cognitive ready
  12. 12. > México | Ciudad de México Intended for Knowledge Sharing only Interesting, but can you pull it off really?
  13. 13. > México | Ciudad de México Intended for Knowledge Sharing only CHANGE MANAGEMENT PLAYBOOK|STRATEGY www.theadanswer.com www.flaticon.comwww.aetholdings.com STRATEGY EXECUTION TRANSFORMATION Source:
  14. 14. > México | Ciudad de México Intended for Knowledge Sharing only FIRST STEP IS TO UNDERSTAND, WHY & WHAT WILL END STATE LOOK LIKE… • Cognitive Function • Productization/Monetization • Brand Capital COMPONENTS DETAILS Goals • Expected outcome: Business Agility, Strategic, Engagement • KPI: End-to-end speed, cost efficiency, ability to handle scale, Stakeholder NPS Success Criteria • Time bound, Program RoI, NPS (Transitionary), Direct Revenue Readiness Assessment • Baselining the current maturity level • Biggest bottlenecks across BUs • Customer “adopt”-ability • Capability sizing (People-Process-Technology-Culture) Evaluation Criteria for Use cases/BU • Data reliability: Sufficiency, complexity, pipeline reliability, signal noise/chaos • Brand Value of Partner BU (Executive Buy-in) • Need: Repetitiveness/portability, Scale, Speed, Complexity? • Speed & Resources • Cognitive readiness End State
  15. 15. > México | Ciudad de México Intended for Knowledge Sharing only …FOLLOWED BY A QUICK AUDIT EXERCISE Sl. No. Component Details 1 Estimated benefit sizing “x% strategic decisions based on run-rates and caused us to delay the launch of a major business line. By launching it earlier, we can increase Revenue by 10%” 2 Problem Statement “Different BUs use different measurement framework, data repositories and lack of understanding of system dynamics causes us to repeat ideas that had failed in the past” 3 Analytics Audit Input (data size/reliability/noise), Need(Automation, Prediction, Prescription, AI), Tech Stack, Estimated Opportunity missed, Engagement model, Delivery Framework 4 Partner BU Nirvana Automation, Self Serve, Cognitive, Productization, Branding 5 Partner BU Readiness People, Process, Technology, Culture, Strategic Vision 6 Support Executive, Leadership, Line Managers 7 Competitive benchmarking Can the current set-up work with some changes? Does it need transition? 8 What if we don’t? If we let it be way it is, does it impact big picture by much? 9 Change/Integration Management Costs/Speed/Dependencies & RoI 10 Project Management Delivery & Deployment steps, Milestones, Success Criteria, RASCI assignments, Executive Sponsors, Communications Management
  16. 16. > México | Ciudad de México Intended for Knowledge Sharing only …TO COME UP WITH AN ACTION PLAN TO PROVE BENEFIT OF UPLEVELING PICK ✓ Interview: Stakeholder discussions to find out pressing questions ✓ Evaluate: Per the checklist in the previous slide ✓ Prioritize: Requester; Urgency; Impact (RoI); Investment ✓ Choose “highest PR potential” problem for POC PROVE ✓ Create action plan – methodology, technology, timelines, expected outcome template, success criteria ✓ SWAT team – Partner BU rep, Analyst & Technologist ✓ Check-ins & documentation of what worked and did not, do’s/don’ts, challenges & nuances and their workarounds ✓ Insights communication & Impact estimation ✓ Champion vs. Challenger measurement SELL ✓ Highlight victories and call out incremental benefits ✓ Ramp plans: hiring, cost, time, other areas where it can be used ✓ Branding – Internal, and if possible, external too, make it ‘cool’ and desirable
  17. 17. > México | Ciudad de México Intended for Knowledge Sharing only CHANGE MANAGEMENT PLAYBOOK|EXECUTION www.theadanswer.com www.flaticon.comwww.aetholdings.com STRATEGY EXECUTION TRANSFORMATION Source:
  18. 18. > México | Ciudad de México Intended for Knowledge Sharing only ANALYTICS VALUE CHAIN: STRATEGY DRIVES EVERY INITIATIVE & ANALYTICS MEASURES ITS EFFECTIVENESS! Analytics provides insights into “actions”, Research context on “motivations” & Testing helps verify the “tactics” in the field and everything has to be productized… Strategy Data Tagging Data Platform Reporting Analytics Research Cognitive Iterative Loop Key benefits Focus on Big Wins Reduced Wastage Quick Fixes Adaptability Assured execution Learning for future initiatives Optimization
  19. 19. > México | Ciudad de México Intended for Knowledge Sharing only …WITH PROGRESSIVE SOPHISTICATION, THE COMPLEXITY OF QUESTIONS & IMPACT OF ANALYTICS SHOULD BE BIGGER & MORE INTEGRAL TO THE ORGANIZATION… 60% 20% 10% 5% 5% 20% 30% 15% 10% 5% 20% 25% 25% 25% 20% 25% 25% 20% 15% 25% 20% 20% 20% 20% 15% YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5 Primary source of insights for Decision Making along the Analytics Maturity Curve Reporting Data Analytics User Research A/B Testing Advanced Analytics/Machine Learning Data Products Cognitive Analytics ILLUSTRATIVE
  20. 20. > México | Ciudad de México Intended for Knowledge Sharing only …& PROGRESSIVELY MORE VALUABLE MILESTONES BE MADE POSSIBLE Dimensions Year 1 Year 2 Year 3 Year 4 Year 5 Major deliverable indicating success at this phase Foundational Data Lake • Operational Analytics, i.e., Automated workbooks for analytics (Self Serve) • Test & Learn Process and Framework • Predictive Model for KPIs and consequent simulations (prescriptive recommendations) • Outcome Focused Framework for Portfolio Management • Early Data Science Platform work • Data Science Platform • Cross integration of Analytics, Research and Testing at App Layer (API) • Streaming Analytics • Cognitive User Facing Product • Monetizeable products Learning objectives met • Data knowledge Repository: Metrics, lineage, governance • Strategic KPIs and their definitions • What worked vs. not: Initiatives impact, Testing results/learning • Engagement Model, delivery framework and who to go to • Champion Challengers • Experimentation Results • Driver relationships • Data Driven Design • Innovation Scaling/ Strategy Testing • System Dynamics • Champion- Challenger on Data Products and Cognitive solutions • Customer receptiveness Business objectives met • Benchmarking insights: Top Movers & Shakers, Segments, Conversion Funnel, User Journey, Engagement Trends, NPS, Brand Awareness, Customer Influencers, Risk, Platform Performance Metrics • High level Drivers identification: Growth segments/levers, Causation vs. Correlation, Cohort Maturity Curve, Value Migration Matrix, Networks & Influencers, Leading Indicators, Risk factors • Data Driven (Analytics, Research & Testing) impact • Conversion Rate Optimization • Lifecycle Management • Growth Hacking • Targeted Campaigns • Influencer Growth • Adaptive Models adoption across Business Units • Modular Self Serve Framework for Business • Impact from Streaming Analytics • Additional source of Revenue from Analytics • Scalability cost reduction from AI • Customer NPS improvement from AI Analytics KPI Speed of insights, %availability of reports, interactive dashboards Progressive Operational KPIs (YoY), Success rate from Testing Program, Business Impact measurement Progressive Operational KPIs, Business KPI impact, Stakeholder NPS, Employee Engagement, Progressive high Returns per Analytics team member ILLUSTRATIVE
  21. 21. > México | Ciudad de México Intended for Knowledge Sharing only CHANGE MANAGEMENT PLAYBOOK|TRANSFORMATION www.theadanswer.com www.flaticon.comwww.aetholdings.com STRATEGY EXECUTION TRANSFORMATION Source:
  22. 22. > México | Ciudad de México FLIP THE PRODUCT DEVELOPMENT FLOW WITHIN ANALYTICS FUNCTION… 5 Intended for Knowledge Sharing only Change Analytics delivery model from “Software Development” to… • (70%) Stakeholders requests what is needed and how • (30%) Analytics interviews on numbers/insights Performs Analytics, Create Reports UAT with stakeholders Stakeholders sign- offs …to a Product Development flow! • “Persona” interviews • Need Identification (speed, detail, frequency, where, visuals/numbers) • Analytics savviness • Possible Tradeoffs • Affluence/ Influence • Customer Rating • POC – Output Delivery System, Engagement Model, etc. • Identify fit, Satisfaction, • Business need met • Iterate • Impact, Usage • Drop-offs, Funnel, Repeat Usage • Platform performance • Next Best Products • Premium Support • Case Studies • Optimize & up-level offering • Brownbags • Productize/ Cognitive IDEATION DESIGN ROLLOUT GROW …and switch between Agile, Kanban, Continuous based on type of project
  23. 23. > México | Ciudad de México Intended for Knowledge Sharing only …& PAIR UP WITH BEST PRACTICES TO CREATE A SUSTAINABLE TRANSFORMATION PEOPLE • Embed Analytics Maturity Curve Graduation as an Analytics Leadership KPI • Analytics Maturity Curve Roadmap & Job Family foundational documents • Design Thinking Focused • Cross Functional ownership • 101 Trainings – Marketing, Product, Sales PROCESS • Borrow best practices from TPM world: Agile, Kanban and Continuous Delivery • Iterative Learning & Co-development of Analytics • Documentation: Impact Dashboards, Project Tracker, Knowledge Discovery Portal, Brand Home Page (Whitepapers, case studies, brownbag), • Formalized Customer Feedback Channel and Management Strategy • SMART Goals TECH • Data Science Platforms: Data Pipeline/ETL (e.g., UNIFI), Model development, deployment (Data Science Workbench) and Post Deployment Monitoring, Management and API framework (Thinkdeep) • Project Management Trackers CULTURE • Business Enablement • Customer Needs Focused • Entrepreneurial
  24. 24. > México | Ciudad de México Intended for Knowledge Sharing only Why this, Why now, why here?
  25. 25. > México | Ciudad de México Intended for Knowledge Sharing only BIGGER TRENDS THAT ARE SHAKING UP THE ANALYTICS WORLD FROM INSIDE OUT… Demand Pressures: Complexity and nature of problems and their solutions, type of audience & consumption framework evolving Monetization opportunities- Direct, Indirect, Recurring Artificial Intelligence, IoE and “Smart”ening of devices/systems faster than expected. Evolution of input data sources and integration of multiple insights sources into decision making (A/B Testing, Research, Predictions/Scores from other models) Evolution from Service to Product to Platform (Build Once, Use Everywhere APIs) …and without Analytics Maturity Curve progression as top priority, we will lose out!
  26. 26. > México | Ciudad de México Intended for Knowledge Sharing only The parting words…
  27. 27. > México | Ciudad de México Intended for Knowledge Sharing only KEY TAKEAWAYS Analytics is a bellweather of business maturity. More advanced the analytics function, more aware and agile is the organization. Insights (Analytics, Research & Testing) is a Strategy function that should drive execution and business transformation. Mature organization have realized that lack of “real” analytics education drives low engagement and under-utilization. This led to creation of education programs to best realize full potential of data assets. Mature organizations with analytics within the DNA are poised to reap benefits of Data Productization, Incremental Monetization, Industry beating performance and Artificial Intelligence revolution. Analytics leadership should chart out the Progression plan for the organization, lay out milestones/timelines, expected impact and keep the leadership focused on this transition. This is needed to help them understand the need for continuous investment and nurturing.
  28. 28. > México | Ciudad de México Intended for Knowledge Sharing only Appendix
  29. 29. > México | Ciudad de México Intended for Knowledge Sharing only THANK YOU! Would love to hear from you on any of the following forums… https://twitter.com/decisions_2_0 http://www.slideshare.net/RamkumarRavichandran https://www.youtube.com/channel/UCODSVC0WQws607clv0k8mQA/videos http://www.odbms.org/2015/01/ramkumar-ravichandran-visa/ https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a RAMKUMAR RAVICHANDRAN

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