Unlocking Information Management for Better Decision Making

1. Qlik User Forum 17th June 2015

2. Agenda • Welcome – Tony Bell, Sales Director, Decision Inc. • It’s not just analytics. Embedding information management best practices across the organisation– Nick Bell, CEO, Decision Inc. • SAB Limited’s QlikView Journey– Grant McDavid, Systems Manager, MIS, SAB Limited • NPrinting– Gavin Sheehan, Operations Manager, Decision Inc. • Predictive Analytics– Paul Morgan, Director and Rousseau Kluever, QlikView Manager, Decision Inc. • Is Qlik Sense Enterprise Ready? – Willem Ahlers, Solution Architect, Qlik • Closing – Tony Bell, Sales Director, Decision Inc.

3. It’s not just analytics. Embedding information management best practices across the organisation - Nick Bell

4. Nick Bell • Nick holds a B.CommHonours degree from the University of Johannesburg • Established BusinessIntelligent in 2006\ • Nick built BusinessIntelligent into the largest Qlikview partner in South Africa • Started Decision Inc. through merger of BusinessIntelligent, ASYST Intelligence and DigiQuill Productivity.

5. Agenda • What is Information Management • Decision Inc.’s journey of understanding • Understanding Decision Making • Understanding the User • Where BI and IM integrate • BI in the Enterprise

6. What is Information Management • Information management (IM) concerns a cycle of organisational activity: • the acquisition of information from one or more sources • the custodianship and the distribution of that information to those who need it • its ultimate disposition through archiving or deletion Taming the Digital Dragon The 2014 CIO Agenda - Gartner

7. Understanding Information Management Better Decisions, Faster The opportunity cost of reporting provides the user with greater capability The user is provided with a greater decision making capability The time taken to provide insights is shorter than it was before

8. Understanding Information Management http://www.aiim.org/What-is-Information-Management

9. Understanding Information Management

10. Understanding Information Management http://www.aiim.org/What-is-Information-Management

11. Understanding Information Management Measure Store Insight Capture Engage

12. Understanding Information Management Measure Store Insight Capture Engage DECISION MADE

13. Information Management Components

14. Process of Understanding Context Wisdom is the recognition that knowledge patters arise from fundamental principles and the understanding of what those principles are. Knowledge is represented by patterns among data, information and other knowledge. The patterns don’t provide knowledge until they have been understood. Wisdom Predictive UNDERSTANDING PRINCIPLES Knowledge Information represented by relationships between data and other data sources to make it meaningful. Analytics UNDERSTANDING PATTERNS Information Reports UNDERSTANDING RELATIONSHIPS Data is an item without any context or relation to other things. Data Understanding

15. Understanding the User Level of Skill None Low High Expert Business Users and LOB Business Analysts Data Scientists 3% 97% 1% Level of Investment

16. Understanding the User Level of Skill None Low High Expert Business Users and LOB Business Analysts Data Scientists 1% 97% 3% • Static Reports • Email Distribution • Standard Measures • PDF Reports • Basic Excel Reports • Time specific reports • Query Reports • Excel Reports • Users ask Questions of the data • Drill down into detail reports • Standard weekly and monthly reports • Business Discovery • Data Discovery Tools • Visual representations • Dashboards • Drill down analysis • Predictive Analysis • Statistical Modelling • Optimization • What If analysis NPrinting provides capability Alteryx and Modeller

17. BI in the Enterprise • With the importance of Information Management within the organization as the solution to enabling decision making • As well as the need to expand the provision of information across the organization • A scalable, enterprise ready application is needed to provide the backbone for the organizations decision making needs • We believe QlikView has this capability • We also believe that the steps the organization is taking to deliver true enterprise BI position it as a leader in the market

18. Summary • Decision Inc. is investing in its organization to deliver greater capability to your business • We are acquiring firms that we believe will provide you with a better service offering • We will continue to invest in research and design of concepts and information that we believe will provide you with the best solution for your business • Thank you for your time and continued support!

19. SAB Limited’s QlikView Journey – Grant McDavid, Systems Manager, MIS, SAB Limited

20. Grant McDavid Grant joined SAB Limited initially in 1997  as a sales rep and joined SAB IS in 2001 after working as an analyst in Trade Marketing. During a break from SABMiller for 2 years , Grant participated in Projects in both Saudi Arabia and London and after an integration role on the Global SAP Template project for SABMiller in 2010, transitioned into MIS. Grant was promoted to MIS Manager in 2014

21. Creating Curiosity with Qlikview Grant Mc David MIS Manager: SABMiller GIS ZA

22. Have you ever considered what happens around us in 60 secs..

23. Agenda • SABMiller, the C21 Global Beverage organisation • 5 Steps to Effective Business Intelligence • Use Case: TTL BI in the Order to Cash Cycle • Multiple Platforms of information delivery at SABM • From the Customer’s viewpoint..

24. SABMiller plc is one of the world’s leading brewers with more than 200 beer brands and some 70,000 employees in over 75 countries.

25. Annual Revenue: ~$34bn Annual Beer Volumes: 242 mhl (SAB Limited 28.1mhl) Annual Soft drinks Volumes: 57 mhl (ABI 18mhl)

26. How did we get it right?

27. 5 Steps to Effective Business Intelligence

28. The Order to Cash BI Journey 2013 Never Lose sight of the goal…

29. The Qlikview Journey (Successes and Scars) 2014 Integration with SAP HANA to manage the Planning function Business Case stacks up instantly SAB Returned savings in excess of ZAR10m within 12 months of implementation

30. IM Vision for the Future – Ways of Working The IM Continuum

31. Business Intelligence Technology Platform (example) COLD WARM Data Temperature HOT EDW

32. From the Customers Viewpoint “Curiosity is the engine of achievement.” “If you're not prepared to be wrong, you'll never come up with anything original.” 

33. Questions..

34. Nprinting – Gavin Sheehan, Operations Manager, Decision Inc.

35. Gavin Sheehan • Gavin holds a BSc Hons (Computer Science) cum laude • He has been involved in the Business Intelligence industry for 9 years • Development Manager for Cybertrenz • 6 years at Bankserv as a BI Analyst and Senior Developer • Joined BusinessIntelligent in 2011 • Promoted to Engagement Manager in 2013 • Promoted to Operations Manager: Platforms in 2014

36. NPrinting What is NPrinting

37. NPrinting NPrintingis an advanced report generation, distribution and scheduling application for QlikView.

38. NPrinting Create great looking reports, fast! - Office reports and integration - PDF and Web reports Distribute the right reports to the right people - Managed report distribution - On-Demand reporting Drive Reporting Efficiencies - Eliminate legacy reporting systems - Engage with a single vendor

39. NPrinting NPrintingDemo

40. Predictive Analytics – Paul Morgan, Director and Rousseau Kluever, QlikView Manager

41. Paul Morgan & Rousseau Kluever • Paul holds a Bachelor of Science in Information Technology (Honours) from Loughborough University of Technology • He has 25 years experience in BI and data management. • Paul was previously Managing Director of ASYST Intelligence • In 2014 ASYST merged with Decision Inc., with Paul as a Director and Head of Platforms. • Rousseau holds Honours degrees in both Information Systems and Financial Management • Rousseau has 6 years experience in BI • Joined BusinessIntelligent in 2012 • Promoted to Engagement Manager in 2013 • Promoted to QlikView Manager in 2015

42. What is Predictive Analytics? Predictive analytics deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. (Wikipedia)

43. Types of Predictive Analytics • Predictive (propensity) What is our expected contract churn this quarter? • Descriptive (clustering & segmentation) Which customers would respond well to a discount offer? • Decision (optimisation & recommendations) What is the best layout for certain products in a store?

44. Maturity of Analytics Optimisation Predictive Modelling What is the best that could happen? Generic Predictive Analysis User Engagement Collective Insight Agile Visualisation Self Service BI What will happen? Ad Hoc Reports Standard Reports CleanedData RawData Why did it happen? What happened? Maturity of Analytics Capabilities

45. Using Predictive Analytics with QlikView • Consulting Engagement Building analytic models with R and storing the data in a new database for Qlikview to access • Third-party Tools (such as Alteryx) Using pre-built analytic components to load data directly into QlikView

46. BasketAnalysis • Potential Benefits of Basket Analysis • Aim of the analysis is to identify actionable information, such as: • Purchase profiles by uncovering consumer spending patterns, • Profitability of each purchase profile, • Insight about fast and slow moving products, • Design and layout of catalogues exploiting cross-sell and upsell opportunities, • Selection of appropriate products for promotion including bundling, coupons etc., and • Shelf space allocation and product placement e.g. affinity positioning. • Where should detergents be placed in the store to maximise their sales? • Are window cleaning products purchases when detergents and orange juice are bought together? • Are carbonated soft drinks typically purchased with fruit and vegetables? • Is there potential for cross-sell and up-sell of goods?

47. To understand which combinations of goods should be bundled, discounted or placed strategically together in order to increase volumes Cross-Sell Opportunity • ‘Bundle sugar and washing powder to increase the likelihood of a customer purchasing goods across product categories.’ • Consumers who buy sugar, have a high probability of purchasing associated goods such as creamersand maize, i.e. sugar is a ‘product driver’ • Bundling product drivers increases basket size, prevents consumers from purchasing associated products from competitors and creates affinity for the retailer

48. To understand which combinations of goods should be bundled, discounted or placed strategically together in order to increase volumes Discount Opportunity • ‘Discount Roll-ons to attract customers to buy at the retailer, increasing the likelihood that customers purchase associated products such as aerosols, toothpaste etc.’ • By incentivising customers to purchase Roll-ons at the retailer, there is an increased probability they will also buy the associated goods, such as aerosols, toothpaste, hand & body lotions etc.

49. To understand which combinations of goods should be bundled, discounted or placed strategically together in order to increase volumes Product Placement Opportunity • ‘Place associated products such as baking powder and custards far from jelly, compelling customers to walk through other parts of the store, increasing the likelihood of other products being purchased.’ • Customers purchasing jelly have a high probability to purchase associated items such as baking powder etc. By placing these items apart, we compel the customer to walk through the store to get both, thereby increasing the chance of other purchases.

50. As prices increase, customers tend to substitute Tastic rice for other similar goods or switch to competing stores Price Elasticity A 7% decrease in price will lead to a 29% increase in daily quantity purchased. Overall effect is a net gain. • Notes: • Rice is a KVI and part of a list of products with prices most remembered • The elasticity at different price points indicates that consumers are sensitive to the price Relatively Inelasticɛ = 1,7 Relatively Elasticɛ = 3,79 -7% 29% Consumers decrease the volumes purchased at higher price points. The slope indicates that prices above R22 are relatively more inelastic.