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Inspire 2015 - KPMG: Using Alteryx and Qlik for Data Discovery and Visualization


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Learn how KPMG is utilizing Alteryx and Qlik to deliver cutting edge data discovery and data visualization solutions to its customers. You'll see examples of data and use cases from their customers, such as the analysis of time and expense data, sales and operations data and machine data, and come away with an understanding of how Alteryx integrates disparate data sources, enriches the data with third party data sources, performs advanced analytics, and seamlessly outputs the analytic dataset into Qlik for visual data discovery.

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Inspire 2015 - KPMG: Using Alteryx and Qlik for Data Discovery and Visualization

  1. 1. #inspire15 Using Alteryx and Qlik for Data Discovery and Visualization Thomas Haslam Tuesday, May 19th Director of Innovation
  2. 2. Our Strategy and Tools • KPMG’s D&A Center of Excellence – The Lighthouse • Why Alteryx and QlikView? • How we are transforming our business Agenda Alteryx Adoption Strategy • People, Process, Technology and Solutions • Our Results Case Studies • Off Label Promotion • HR Analytics • Investor Portfolio Analysis • World Cup Tipping
  3. 3. KPMG’s D&A Center of Excellence – the Lighthouse KPMG D&A Center of Excellence Strategic Partnerships Our people include data scientists, software and data engineers, and analytics experts, organized by industry verticals. In addition to serving our clients, we have a mandate to increase the innovative capacity and analytics capacity of all of our staff. Analytics strategy engagements typically involve analysis of data governance, technology architecture, organization capabilities and design, or analytics “use cases”. Analytics execution engagements involve finding and solving a client problem, and handing over or hosting the solution for our clients.
  4. 4. Why Alteryx and QlikView? Why Alteryx? • Support model • End to end data manipulation and analysis tool • Integration with QlikView • Easy to use • Predictive analytics toolset
  5. 5. Why Alteryx and QlikView? Why QlikView? • Data driven discovery supports detailed data analysis • Reporting functionality • Support model • Industry standard • Intuitive for users
  6. 6. Before and After the Lighthouse… Pockets of D&A excellence Different tools across the business No clear approach to prioritization title Duplicated effort especially around data manipulation Lots of reporting, little prediction Demand for our services Shared vision and strategic objectives Specialist functions More advanced analytics A common toolset Governance and prioritization More scalable solutions More innovation Sharing of best practices and peer review Improved staff retention title Improved access to data More value to our clients
  7. 7. #inspire15 Alteryx Adoption Strategy
  8. 8. You need a plan…
  9. 9. …that maximizes your chances of success • Industry specific use case prototypes • “Analytic gallery“ accessible to all staff • Alteryx server edition installed centrally to enable model sharing • Automated package install through standard desktop deployment • Training workshops to more effectively broaden tool adoption • On demand training available • Clear Alteryx processes documented • Minimum of 300 trained end users (Intermediate & Advanced) by the end of the year 1 • 100 consistent users across multiple service lines People Process / Training SolutionsTechnology
  10. 10. Our results 900+ People Trained 100+ Solutions Built 350+ Weekly Users Innovation
  11. 11. #inspire15 Case Study 1: Off Label Promotion
  12. 12. Analyzing Performance Case Study Case Study – Off-Label Pharmaceutical Promotion • The Problem: It is illegal for pharma companies to promote the use of their drugs for purposes that are not “on-label” – ie, authorized by the FDA • It is not illegal for doctors to prescribe the drugs for off-label use – eg, prescribing tricyclic antidepressants for chronic pain • How can pharma companies monitor whether their sales reps are promoting the drug for “off-label” usage? Combining Multiple Complex Data Sources and Analyzing the Data in Alteryx • Doctors, sales reps and regions • Prescriptions issued data • Sales reps expense and call data • Training attendance data Visualizing Results in QlikView • Reporting comparative performance • Predicting results
  13. 13. Preparing the Data 1 – Import the data 2 – Perform analytics 3 – Output .qvx
  14. 14. Visualizing the Results 1 – Off-label sales by territory 2 – Sales rep score cards
  15. 15. #inspire15 Case Study 2: HR Analytics
  16. 16. HR Analytics How do I prevent high performers from leaving?
  17. 17. Preparing the Data 1 – Import the data 2 – Perform analytics 3 – Output .qvx The Variables contributing to the overall turnover of high performers include the following attributes: • Salary • Office Commute • Years service • CAGR • Years in Business Unit • Fiscal Year • Job Level • Vesting in 401k • Ethnicity • Hire Type • Improvement in Review Score • Gender • Increased Bonus • Last Raise Higher > CAGR • Diversity • Remote from Home Office
  18. 18. Visualizing the Results 1 – Overall turnover dashboard 2 – Impact of variables
  19. 19. #inspire15 Case Study 3: Investor Portfolio Analysis
  20. 20. Visualizing data case study Complex investor portfolio • The Problem: Providing clients with a complex spreadsheet providing factual information but in a format that is difficult to understand • How can we provide more meaningful insights without adding to our workload? Rebuilding the Excel spreadsheet in Qlikview • Set-up an Alteryx application which rebuilds the excel spreadsheet in Qlikview • Enable it to be run as an app, so you don’t need Alteryx skills to use
  21. 21. Preparing the Data 1 – Import the data 2 – Perform analytics 3 – Output .qvx
  22. 22. Visualizing the Results
  23. 23. #inspire15 Case Study 4: World Cup Tipping
  24. 24. Predicting Outcomes Predicting Outcomes • Simulation modeling using Alteryx • Visualizing results in QlikView • Case Study – World Cup 2014
  25. 25. Predicting Outcomes World Cup Prediction Models Accuracy Ranking – Top 10 Rank Model Author Total correct matches (/63) Versus Naïve baseline Rof16 teams to advance correct (/16) Knockout correct (/15) 1 Andrew Yuan (KPMG) 45 +4 11 13 =2 Bloomberg 43 +2 11 12 =2 ELO 43 +2 11 12 =2 Hassan/Jiminez 43 +2 10 12 5 Danske Bank 42 +1 11 12 =6 41 0 10 10 =6 Infostrada 41 0 10 11 =6 Naïve 41 n/a 8 12 9 538.Com 40 -1 8 13 10 Goldman Sachs 39 -2 9 12
  26. 26. #inspire15 Thomas Haslam Direction of Innovation – Data & Analytics