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Data Foundation for Analytics Excellence by Tanimura, cathy from Okta
Cathy Tanimura discusses building a data foundation for analytics excellence at Okta. She outlines finding problems to solve by researching high impact issues and engaging business partners. For a proof of concept, one should find available data, use simple tools, build something, and get feedback. When picking technology, consider requirements, educate on options, and present small, medium, large cost choices. Expect timelines and budgets to increase from initial plans. Building a team requires data, engineering, and science skills at different stages. Partners and executive champions are key to spreading awareness of the work. The end goal is using data and analytics to save time, increase revenue, and focus efforts.
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Data Foundation for Analytics Excellence by Tanimura, cathy from Okta
- 1. Data Foundation for AnalyticsExcellence Cathy Tanimura Director of Analytics & Big Data @ Okta ctanimura@okta.com
- 2. Agenda • Intro • DataFoundation • Finding the Problem(s) • Getting Started: Proof of Concept • Picking the Technology • Building Out: What to Expect • People Foundation • Building the Team • Partners and Champions • Bringing it All Together
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- 5. Okta? “In meteorology, anokta is a unit of measurement used to describe the amount of cloud cover at any given location such as a weather station. Sky conditions are estimated in terms of how many eighths of the sky are covered in cloud, ranging from 0 oktas (completely clear sky) through to 8 oktas (completely overcast).” - Wikipedia
- 6. 4 Million+ People 10 Million+ Devices TheEnterprise Identity Network 3,000+ Applications OnPremCloudMobile 1,600+ Organizations
- 7. Problems Okta Solves •User Password Fatigue • Failure-Prone Manual Provisioning & De-Provisioning Process • Compliance Visibility: Who Has Access to What? • Siloed User Directories for Each Application • Managing Access across an Explosion of Browsers and Devices • Keeping Application Integrations Up to Date • Different Administration Models for Different Applications • Sub-Optimal Utilization, and Lack of Insight into Best Practices
- 8. Focus on theend-user
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- 10. Data Foundation •Finding theProblem(s) •Getting Started: Proof of Concept •Picking the Technology •Building Out: What to Expect
- 11. Finding the Problem •Firstthing you want to tackle •Prove value •Research for long-term infrastructure
- 12. What Makes aGood Problem •Big business impact: $$’s, time •Data available •Someone has tried to tackle •Engaged business partner •Clear vision of what will change
- 13. Common “Problems” •Marketing optimization •Multi-channelattribution •User behavior •Fraud detection •Recommendations •Viral / market penetration •Retention / churn •Resource allocation
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- 15. Finding the Problem •“Virals” were major growth and retention tool • How many new users did we attract? • How many came back? • How effective was this feature at driving traffic? • How does play spread from friend to friend?
- 16. Finding the Problem Activities: •Add directory • Import users • Add apps • Assign users • Rollout plan Adoption
- 17. Finding the Problem Whydo we care about adoption? • Happy customers renewals, references, upsell opportunities Sub-Problems: • How many customers? • Does it really affect churn? • Can we influence?
- 18. Proof of Concept •Findthe data •Simple, low cost tools •Build something •Get feedback
- 19. POC: Find theData Social Cloud Apps In-house Apps On-Prem Databases 3rd party
- 20. Finding the DataExample
- 21. POC: Simple, low-costtools •What do you already have •Open-source •Trials / community editions
- 22. POC: Example DataInfrastructure
- 23. Building •Define the metrics •Understandable • Measurable • Actionable •Visualize
- 24. Building the Metric:Example • At a high level, Adoption = Usage / Entitlement • What is the best “usage” measure?
- 25. Showing the MetricMatters • Some outliers, but adoption correlated with renewal
- 26. Get Feedback •Share •Listen •Pay attentionto where the data doesn’t fit the “smell test”. At first your clients will have a better sense than you do
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- 28. Pick the Technology •Thefun part (sort of) •Start with requirements discovered during POC •Be aware of the market, but not distracted
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- 32. Tips on Sellingthe Technology •Educate: what does each piece do (in layman’s terms) •Present S,M,L cost options
- 33. Data Mining, Modeling, Stats BIToolsSource Systems Operational Systems (“Prod”) Cloud Services Web Data External Data Data StorageETL / Data Integration Streaming, Event Processing “End to End” Analysis, Viz Data Warehouse (SQL) Hadoop Platforms Point Solutions Example: Tech & Vendor Landscape
- 34. Example: S,M,L Options Small •$0k • 0 extra FTE • Rely on forums, learn as we go • Timeline: 12+ Months Medium • $100k • 1 FTE • Access to expertise • Timeline: 6-9 Months Large • $200k • 2 FTE • Access to expertise • Timeline: 3 – 6 Months
- 35. Building Out: Whatto Expect •It will never go “as expected” •Time will be more than expected •$ will be more than expected Develop the vision up-front, fill in details as you go Consider Agile development
- 36. Building Out: Whatto Expect •Stuff that happens: •People change •New data source •Holidays & vacations •Integrations break •Data quality
- 37. What to Expect Younever “finish” analytics… Known Knowns Easy stuff Unknown Knowns Duh Unknown Unknowns Uh-oh Known Unknowns Aha!
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- 41. Who Data Analyst Focus: • Analysis, reports, dashboards Alignedto: • Business Languages: • SQL, R, Excel Data Scientist Focus: • Data products • Modeling Aligned to: • Product Languages: • R, Python, SQL Data Engineer Focus: • Data infrastructure • Scalability Aligned to: • Engineering Languages: • Java, Python, MapReduce
- 42. When to Buildthe Team Delphi Analytics, April 1, 2013
- 43. When to Buildthe Team •Scale with business •Infrastructure in place •Generate demand from clients
- 44. Partners & Champions •Easilyoverlooked but key to success •Partners are your clients • Typically Marketing, Finance, Product, BizDev •And the teams you rely on • IT, Engineering, Product
- 45. Partners & Champions •Championsare execs and people on the ground who can spread the word • Execs want clear and simple messages: what are the benefits, how much will it cost • You never know who your other champions are going to be. Don’t miss opportunities to help people out
- 46. Putting It AllTogether
- 47. Tech Stack -Vision
- 48. What are theEffects? • Time savings • Time spent collecting & processing data by Customer Success, Renewals, Product • Time spent telling anecdotes • Revenue: • Save at-risk renewals: early awareness tells us where to intervene • Upsells: Visibility into usage lets sales people have more timely & informed discussions about upsells • Focus • On the features that matter (not ones that don’t) • Take the guesswork out of meetings
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