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.

1. Data Foundation for
Analytics Excellence
Cathy Tanimura
Director of Analytics & Big Data @ Okta
ctanimura@okta.com

2. Agenda
• Intro
• Data Foundation
• 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

3. Intro

4. Background

5. Okta?
“In meteorology, an okta 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
The Enterprise 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 the end-user

9. Data Foundation

10. Data Foundation
•Finding the Problem(s)
•Getting Started: Proof of Concept
•Picking the Technology
•Building Out: What to Expect

11. Finding the Problem
•First thing you want to tackle
•Prove value
•Research for long-term infrastructure

12. What Makes a Good 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-channel attribution
•User behavior
•Fraud detection
•Recommendations
•Viral / market penetration
•Retention / churn
•Resource allocation

14. Finding the Problem

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
Why do 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
•Find the data
•Simple, low cost tools
•Build something
•Get feedback

19. POC: Find the Data
Social
Cloud Apps
In-house Apps
On-Prem Databases
3rd party

20. Finding the Data Example

21. POC: Simple, low-cost tools
•What do you already have
•Open-source
•Trials / community editions

22. POC: Example Data Infrastructure

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 Metric Matters
• Some outliers, but adoption correlated with renewal

26. Get Feedback
•Share
•Listen
•Pay attention to where the data doesn’t
fit the “smell test”. At first your clients
will have a better sense than you do

27. Feedback: Prototype Example

28. Pick the Technology
•The fun part (sort of)
•Start with requirements discovered
during POC
•Be aware of the market, but not
distracted

29. Data Store Decisions
Vs.
Vs.

30. ETL Decisions

31. Front-End Options

32. Tips on Selling the Technology
•Educate: what does each piece do (in
layman’s terms)
•Present S,M,L cost options

33. Data Mining,
Modeling, Stats
BI ToolsSource 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: What to 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: What to Expect
•Stuff that happens:
•People change
•New data source
•Holidays & vacations
•Integrations break
•Data quality

37. What to Expect
You never “finish” analytics…
Known
Knowns
Easy stuff
Unknown
Knowns
Duh
Unknown
Unknowns
Uh-oh
Known
Unknowns
Aha!

38. People Foundation

39. Building the Team
•Who
•When

40. Building a Team

41. Who
Data Analyst
Focus:
• Analysis,
reports,
dashboards
Aligned to:
• 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 Build the Team
Delphi Analytics, April 1, 2013

43. When to Build the Team
•Scale with business
•Infrastructure in place
•Generate demand from clients

44. Partners & Champions
•Easily overlooked 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
•Champions are 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 All Together

47. Tech Stack - Vision

48. What are the Effects?
• 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

49. Questions?