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Connecting Apache Kafka to Cash

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View this talk here: https://www.confluent.io/online-talks/connecting-apache-kafka-to-cash-lyndon-hedderly

Real-time data has value. But how do you quantify that value in order to create a business case for becoming data, or event driven? This talk explores why valuing Kafka is important - but covers some of the problems in quantifying the value of a data infrastructure platform.

Despite the challenges, we will explore some examples of where we have attributed a quantified monetary amount to Kafka across specific business use cases, within Retail, Banking and Automotive.

Whether organizations are using data to create new business products and services, improving user experiences, increasing productivity, or managing risk, we’ll see that fast and interconnected data, or ‘event streaming’ is increasingly important. We will conclude with the five steps to creating a business case around Kafka use cases.

Publicada em: Tecnologia
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Connecting Apache Kafka to Cash

  1. 1. 1 Connecting Kafka to Cash K ↔ $ Lyndon Hedderly, Confluent
  2. 2. 22 Agenda 1. Measuring Value 2. Three Real-World Examples 3. Recap & Recommendations 1. Why? 2. Why Kafka? 3. Problems!
  3. 3. 33 Why Measure Value? 1. ROI
  4. 4. 44 Why Measure Value? 2.Stakeholder Commitment
  5. 5. 55 Why Measure Value? 3.Benefits Realization
  6. 6. Investing $50kin 10 companies using data to deliver Great User Experience The UX Fund: 4.
  7. 7. $50k in 10 companies in 2006 became $306k in 2016. A 503% gain S&P 50%
  8. 8. Gain as a % $157k >3,000% return! Almost 900% return! S&P Return c.50%
  9. 9. 99 So, why?
  10. 10. 1010 Innovate or be disrupted... 1. Create New Business Models 2. Deliver new, real-time customer experiences 3. Deliver massive internal efficiencies ● Retail: Rent rather than buy clothing ● Recommendations on Netflix ● Seeing real-time ETA of Uber driver ● Text alert - credit-card fraud ● Global bank consolidating data silos ● Regulatory reporting ● Corporate office seeing worldwide sales in real-time Making More Money Saving or Protecting Money New Ways of Making Money
  11. 11. 1111 Transportation ETA Real-time sensor diagnostics Driver-rider match Banking Fraud detection Trading and risk systems Mobile applications / customer experience Retail Real-time inventory Real-time POS reporting Personalization Entertainment Real-time recommendations Personalized news feed In-app purchases
  12. 12. 1212 The Problems with measuring value
  13. 13. 1313 Problems with measuring value? 1. Poor Credibility of business cases - Dodgy assumptions / flaky estimates
  14. 14. 1414 Problems with measuring value? 2. What are we actually measuring?
  15. 15. 15
  16. 16. It’s not just the data that is important, it is how it is moved around - and how timely it is → The ability to move the oil becomes almost as valuable as the oil itself → “Data is only as valuable as your ability to quickly act on it” - Jay Kreps, co-founder and CEO, Confluent.
  17. 17. 17
  18. 18. 1818 Problems with measuring value? 3. Value changes - and what does it really mean anyway?
  19. 19. 19Confidential 3. What does value mean? - The Subjective Element of valuing something - Value changes... $940 k 1999 $65 M 2018 $91 M 2019 97x in 20 yrs
  20. 20. 3. What does value mean? - The Subjective Element of valuing a (contextual) piece of technology - Value changes... 1. Cost: The iWatch? $83 2. Price: $350 3. With fitness information? Medically accurate electrocardiogram?
  21. 21. 2121 So, valuing is hard 1. We have to predict the future - with assumptions and estimates 2. We have to agree on what we’re actually measuring - the engine or the car? The oil or the pipeline? Perhaps they’re the same thing? 3. Value is subjective and it changes - we have to be ‘situationally aware’ and be comfortable with that.
  22. 22. 2222 Three Examples of measuring Kafka’s business value
  23. 23. 2323 5-step value assessment process 1. Baseline As-Is State & Negative Consequences (3-5 yr costs) 2. Target State Desired Solution 3-5 yr costs + Project Costs 3. Benefits Top-line business Benefits & Bottom-line Savings (TCO comparison / ROI) 4. Soft Benefits Non-Financial Impact & Analysis: Incl. Scenarios, Sensitivities & Risks 5. Proof- Points Metrics (definition of success) Case Studies ● Below the line - Universal Pipeline benefits of Kafka ● Above the line - Contextual Business Applications
  24. 24. 2424 ATM Disputes
  25. 25. 2525 7-9,000 disputes / month Agents = 3-5 hrs to resolve a dispute Significant number paid off Costs c. £4M / yr ATM Disputes
  26. 26. 2626 ATM Disputes
  27. 27. 27 Universal Event Pipeline Data Stores Logs 3rd Party Apps Custom Apps/Microservices from ATM’sContextual Information - Bank Accounts Analysis / Historic info - feeds from other ATM / banks Universal event pipeline --- high throughput, persistent, ordered, and has low latency --- All events and systems are connected ATM Disputes Benefits: 1. From batch to real-time 2. Single Enterprise wide source of events 3. Built for scale 4. Store events while maintaining real-time replay 5. ...
  28. 28. 28 Universal Event Pipeline Data Stores Logs 3rd Party Apps Custom Apps/Microservices ATM Disputes App Real-Time Fraud Detection Real-Time Customer 360 Machine Learning Models Real-Time Data Transformation ... Contextual Event-Driven Apps ● 50% reduction in agent time ● 75% reduction in avoidable payments ATM Disputes
  29. 29. ATM Disputes
  30. 30. ATM Disputes
  31. 31. Breakeven: H1 ATM Disputes Additional benefit of improved customer experience & loyalty.
  32. 32. ATM Disputes
  33. 33. A one-point improvement in CX Index score results in (By Industry):
  34. 34. Customer 360
  35. 35. 35 Universal Event Pipeline Data Stores Logs 3rd Party Apps Custom Apps/Microservices Real-Time Fraud Detection Real Time Inventory Real-Time Customer 360 Machine Learning Models Real-Time Data Transformation ... Contextual Event-Driven Apps From on-line click throughsStock / Warehouse / Inventory Info Analysis / Weather info - feeds from other data sources ● On-line; Personalized, targeted offers: uplift revenue ● Better manage inventory - for offers, with dynamic pricing Customer 360
  36. 36. Customer 360
  37. 37. Customer 360
  38. 38. Additional benefit of simplifying back-end systems - not modelled here Customer 360
  39. 39. Customer 360
  40. 40. OK Sir... Here you go… ...but don’t you know you can do all this much more easily online. Fraud
  41. 41. Fraud
  42. 42. 42 Universal Event Pipeline Data Stores Logs 3rd Party Apps Custom Apps/Microservices Real-Time Fraud Detection Real Time Inventory Real-Time Customer 360 Machine Learning Models Real-Time Data Transformation ... Contextual Event-Driven Apps Detection & Prevention Mobile Banking AppsMainframe Core Banking Apps Analysis / location information, other Cyber Sec info ● Detection and prevention of Fraud (real-time) Fraud
  43. 43. Fraud
  44. 44. Fraud
  45. 45. Fraud Additional benefit of improved customer experience & loyalty. Decreased insurance premiums.
  46. 46. Fraud
  47. 47. 4747 Summary of Value Customer 360 £10.4M 1.93x ROI Fraud Prevention £23.275M 6.25x ROI ATM Disputes £6.125M 2.13x ROI 1. Create New Business Models 2. Deliver new, real-time customer experiences 3. Deliver massive internal efficiencies
  48. 48. 4848 Recap & Recommendations
  49. 49. 4949 Recap 1. Measuring Value 2. Three Real-World Examples - ATM Disputes - save money - Customer 360 - make money - Fraud - save money 3. Recommendations... 1. Why? 2. Why Kafka? 3. Problems! 1. Credibility? 2. What we’re measuring? 3. Be comfortable with subjective elements and change
  50. 50. 51
  51. 51. 52 Real-Time Inventory Real-Time Fraud Detection Real-Time Customer 360 Machine Learning Models Real-Time Data Transformation ... Contextual Event-Driven Apps
  52. 52. 5353 Thank You lyndon@confluent.io We offer this service - contact Sales@confluent.io

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