data foundation for analytics excellence by tanimura, cathy from okta

Data Foundation for Analytics Excellence

Cathy Tanimura

Director of Analytics & Big Data @ Okta

ctanimura@okta.com

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

Intro

Background

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

4 Million+

People

10 Million+

Devices

The Enterprise Identity Network

3,000+

Applications

On

Pre

m

Clo

ud

Mo

bile

1,600+ Organizations

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

Focus on the end-user

Data Foundation

Data Foundation

• Finding the Problem(s)

•Getting Started: Proof of Concept

•Picking the Technology

•Building Out: What to Expect

Finding the Problem

• First thing you want to tackle

•Prove value

•Research for long-term infrastructure

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

Common “Problems”

•Marketing optimization

•Multi-channel attribution

•User behavior

• Fraud detection

•Recommendations

•Viral / market penetration

•Retention / churn

•Resource allocation

Finding the Problem

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?

Finding the Problem

Activities: • Add directory • Import users • Add apps • Assign users • Rollout plan

Adoption

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?

Proof of Concept

• Find the data

• Simple, low cost tools

•Build something

•Get feedback

POC: Find the Data

Social

Cloud Apps

In-house Apps

On-Prem Databases

3rd party

Finding the Data Example

POC: Simple, low-cost tools

•What do you already have

•Open-source

• Trials / community editions

POC: Example Data Infrastructure

Building

•Define the metrics • Understandable • Measurable • Actionable

•Visualize

Building the Metric: Example

• At a high level, Adoption = Usage / Entitlement

• What is the best “usage” measure?

Showing the Metric Matters

• Some outliers, but adoption correlated with renewal

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

Feedback: Prototype Example

Pick the Technology

• The fun part (sort of)

• Start with requirements discovered during POC

•Be aware of the market, but not distracted

Data Store Decisions

Vs.

Vs.

ETL Decisions

Front-End Options

Tips on Selling the Technology

• Educate: what does each piece do (in layman’s terms)

•Present S,M,L cost options

Data Mining, Modeling, Stats

BI Tools Source Systems

Operational Systems (“Prod”)

Cloud Services

Web Data

External Data

Data Storage ETL / Data Integration

Streaming, Event Processing

“End to End”

Analysis, Viz Data Warehouse

(SQL)

Hadoop Platforms

Point Solutions

Example: Tech & Vendor Landscape

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

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

Building Out: What to Expect

•Stuff that happens: •People change •New data source •Holidays & vacations • Integrations break •Data quality

What to Expect

You never “finish” analytics…

Known Knowns Easy stuff

Unknown Knowns

Duh

Unknown Unknowns

Uh-oh

Known Unknowns

Aha!

People Foundation

Building the Team

•Who

•When

Building a Team

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

When to Build the Team

Delphi Analytics, April 1, 2013

When to Build the Team

• Scale with business

• Infrastructure in place

•Generate demand from clients

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

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

Putting It All Together

Tech Stack - Vision

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

Questions?