Data Foundation for Analytics Excellence. Cathy Tanimura Director of Analytics & Big Okta

Transcription

1 Data Foundation for Analytics Excellence Cathy Tanimura Director of Analytics & Big Okta

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 Mobile On Prem Cloud The Enterprise Identity Network 4 Million+ People 3,000+ Applications 10 Million+ Devices 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 Adoption Activities: Add directory Import users Add apps Assign users Rollout plan

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 Proof of Concept to achieve scale, paradoxically, you need to focus. Trying to orchestrate change in all of a company s daily decision-making and operating approaches is too overwhelming to be practical Better to pursue scale that s achievable than to overreach and be disappointed or to scatter pilots all over the organization. from Getting big impact from big data, McKinsey Quarterly, January 2015

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

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

22 Simple, Low-Cost Tools

23 POC: Example Data Infrastructure

24 Building Define the metrics Understandable Measurable Actionable Visualize

25 Building the Metric: Example At a high level, Adoption = Usage / Entitlement What is the best usage measure?

26 Showing the Metric Matters Some outliers, but adoption correlated with renewal

27 Feedback: Prototype Example Explain Listen Adjust

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 Building a Team

33 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

34 When to Build the Team Scale with business Infrastructure in place Generate demand from clients

35 Tips: Educate on Tools in Layman s Terms Source Systems Operational Systems ( Prod ) ETL / Data Integration Data Storage Data Warehouse (SQL) BI Tools Analysis, Viz Data Mining, Modeling, Stats Cloud Services End to End Hadoop Platforms Web Data Point Solutions External Data Streaming, Event Processing

36 Tips: Present S,M,L Cost 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

37 Tips: Keep Selling the Vision

38 Building Out: What to Expect It will never go as expected People change Requirements change Time will be more than expected Holidays & vacations Data quality Integrations break $ will be more than expected

39 Tips: Partners & Champions Easily overlooked but key to success Partners Your clients: Marketing, Finance, Product, BD Teams you rely on: IT, Engineering, Product Champions Execs: want clear and simple benefits, costs People on the ground who spread the word: never know the might be - don t miss opportunities to help people out!

40 Putting It All Together

41 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

42 You Never Finish Analytics Known Knowns Easy stuff Known Unknowns Aha! Unknown Knowns Duh Unknown Unknowns Uh-oh

43 Enjoy the Adventure! Cathy Tanimura