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Making Money Out of Data

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In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data.

Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques.

Key Takeaways:

(i) Demystify the myths of analytics

(ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars.

(iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.

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Making Money Out of Data

  1. 1. Shailendra Kumar, Chief Evangelist, SAP May 1, 2019 Making Money Out of Data The art & science of analytics
  2. 2. Keynote Speaker, Global Data Science and Analytics Influencer, BestSelling Author & International article contributor and has experience in Business and Customer Analytics across Europe, United Kingdom and in Australia, generating incremental value benefits of over $1 Billion Industry Experience: ▪ Woolworths, Largest Retailer, Australia: Chief AnalyticsOfficer ▪ Coles, Grocery Retailer, Australia: Senior Business Executive,Analytics ▪ Swisscom, Telecom Operator: Senior Manager © 2019 SAP SE or an SAP affiliate company. All rights reserved. ǀ PUBLIC Selected Professional Experience Shailendra Kumar Chief Evangelist, Analytics & Leonardo APJ&GC Based out of Sydney,Australia Shailendra is the author of the Amazon bestseller “Making Money Out of Data” and is the Evangelist for APJ&GC and helps SAP’s Chief client organisations in creating incremental value using data
  3. 3. Analytics provides you with the tools to benefit from a series of business imperative questions Imagine if you could … … understand demand across the regions …know which half of your marketing dollar gives returns … understand use of network capacity and the reasons behind it … know price elasticity of demand in advance … know customer behavior from social media originating information … know which part / process is going to fail, before it fails Analytics
  4. 4. Analytics is not new Increasing Analytic Sophistication & Capabilities Ad Hoc Query Scorecards DW Lifecycle Mgmt Collaboration & Workflow Batch Reporting Online Analytic Processing Dashboard & Visualizations Process Awareness ETL /Data Quality Data Models Alerting Cognitive Analytics Predictive Analysis Data Warehousing Static Reporting Event Automation So what is different now? Definition: Analytics is the process of using quantitative methods to derive actionable insights and outcomes from data 1975 1989 1990 2004 2005-2020 Templates 5
  5. 5. WE STRUCTURE OUR APPROACH TO INTEGRATED ANALYTICS INTO FOUR MUTUALLY REINFORCING TIERS VALUE TO THE ENTERPRISE BUSINESSAGILITY Product Segmentation, Sales performance dashboards, Social MediaAnalytics, Mobile Data & BI Supply Chain Optimisation,Trigger Based Modeling, Social NetworkAnalysis, Next best offer / action MANAGING INFORMATION Managing information to improve business process with information strategy and governance while achieving a ‘single source of the truth’ for customer information DESCRIPTIVE ANALYTICS Deliver business intelligence and insight generation capabilities and leverage timely deployment of actionable information and industry focused intelligence AD-HOC ANALYTICS SERVICES Answer business questions to address day to day business needs in a timely manner to service business units. PREDICTIVE ANALYTICS Improconfirm that analytics insights are turned into both actions and measurable outcomes proactively, driving high performanceve the speed and quality of decision making to What if scenarios, simulations, adhoc analysis, combatingtactical issues Manage Customer / Product related data for sales performance, Integrate data across multiple channels Single view of customer / product, revenue byregion Analytical Competitive Strategy
  6. 6. Business and Technology changes are opening up new opportunities for innovating around analytic capabilities y Business Context • Greater volatility • New waves of growth & innovation • Governance in a multi-speed recover • Different questions Innovation Opportunity 1.Measure what matters, while what matters is changing 2.Next Practice not Best Practice 3.Innovate through Decision Process Reengineering 4.Automate the Outcomes Technology and Data Context • Technology mega-trends • Data driven innovations What has Changed? So What? Now What? 7
  7. 7. High Performing Organisationsare able to realize outcomes better using analytics High Performing companies are satisfied with the contribution analytics has made to financial performance, strategic direction, addressing growth opportunities, informing critical decisions and managing risk, compared with Low Performers (onaverage) LOW PERFORMERS HIGH PERFORMERS THEANALYTICSJOURNEYTOROI FOCUS ON DATA TO INSIGHTS Commit to analytics Manage talent from end-to-end Use advanced analyticaltechniques Embed analytics into the decisionprocess FOCUS ON INSIGHTS TO OUTCOMES ✔Commit to analytics ✔Manage talent fromend-to-end ✔ U s e advanced analytical techniques ✔Embed analytics into the decision process
  8. 8. Moving to predictive analytics and insights * Percentage of time 9 Advanced analytics Analysis and insights Reporting and data collection Analysis and insights Reporting and data collection • Report rationalization and automation • KPI alignment • Issue-driven insights and measures • Data virtualization/ centralized data access layer • Business intelligence and data visualization toolkit • Advanced Analytics tools and solutions • Information and analytics organization design and operating model • Talent sourcing and delivery model Advanced analytics
  9. 9. 9 9 Data Collection & Preparation Clean, fully mapped and validateddataset delivered to modelling team Modelling Preliminary results delivered tofor use in Business workshops. Business Discussions Final modelling results incorporating business recommendation Insights & Recommendations Results & insights presentationsprepared and socialised after incorporating the business PoV Execution Incorporate insightsinto Business Processes to get results Tracking Track the actuals against the recommendedinsights Objective Outcome Data Science process currently followed is linear and is designed to work in Silos
  10. 10. Challenges in Execution of Analytics Insights Your Analytics Model doesn’t make sense and I don’t think it will work! I know more about the business than your analytics can tell me! I am told to align the data but I don’t know why? Analytics is just yet another jargon… and does nothing!! 11 Business Business Team Finally
  11. 11. Illustrative Benefit Areas 12 Business Value Income Growth Risk Management Operational Efficiency 12. Reduced staff costs 13. Optimized sales & marketingspend 14. Reduced operational and administrativecosts 3. Increased cross-sale & share of wallet 5. More effective salesforce 7. Improved campaign and offer effectiveness 6. Optimised leadsmanagement 8. Improved risk management 9. Improved fraud identification & mitigation 10. Improved staffproductivity Analytics Benefits Driver Tree 2. Improved customer retention and loyalty 1. Improved customerexperience 11. Improved employeeengagement 4. Improved customeracquisition ↓ Staff costs pertransaction ↓ campaign cost per campaignsale ↓ Operational cost by totalbook ↑ Products per customer ↑ Sales per staffmember ↑ Offer to saleconversion ↑ Lead to sale conversion ↓ Breaches ↓ Fraud cost ↑ Transactions per staff member ↓ Account closures ↑ NPS ↓ Complaints ↑ Satisfaction ↓ Attrition ↑ Engagementsurveys ↑ New customers Outcome Measure
  12. 12. There are huge benefits when we bring it all together Challenge A large retailer in the US wanted to know whatis the profile of the employee that are likely to leave the organisation in less than a year A large CPG had thousands of KPIsacross functions, geographies and categories There was difficulty in understanding gaps in the performance Approach Brought together : Social Media data, Internal employee data, Performance Data Built a predictive model and then scored each employee with an attrition score Collected data from Financial Systems, Operational Systems, BI Systems, etc. Created Early Warning System and a visualisation of the most impactful KPIs Outcome The retailer created a profile of an employee who is likely to leave in less than year, which helped them introduce additional questions totheir interview process. It significantly reduced attrition rate. Alerts are generated about an issue before it happens and furthermore, the board focusses on the KPIs that matter the most towards the growth of the organisation 13 Human Resources IT Operations
  13. 13. Important characteristics of a successful analytics environment Live & Connected 14 Story- Telling Realtime Updates Powerful Visuals Integrated Predictive Collaborative
  14. 14. Embed Analytics in Processes: From “Craft” Analytics to “Industrial” 15 CRAFT INDUSTRIAL Pattern Ad hoc, project-oriented Embedded Purpose One-time decision or event support Ongoing process performance Benefit One-time Recurring Investment Higher, recurring Lower, One-time Speed of Analysis Same as time to implement Fast or instantaneous Staff Labor-intensive Informed or automated Memory of analysis Saved for reuse or lost Maintained and improved
  15. 15. Critical Success Factor: Start from the End Identify the Opportunity Define the Outcome & the Execution Quantify the Value $ 16
  16. 16. Analytics driving innovation in China 17
  17. 17. We have a huge opportunity to shape the future together Contact information: Shailendra Kumar Chief Evangelist Analytics | Leonardo @meisShaily linkedin.com/in/shaily CognitiveToday.com shaily.kumar@sap.com QR Codes Linkedin Twitter Weibo 18

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