Data Foundation for Analytics Excellence by Tanimura, cathy from Okta
-
Upload
tin-ho -
Category
Data & Analytics
-
view
236 -
download
4
Transcript of Data Foundation for Analytics Excellence by Tanimura, cathy from Okta
Data Foundation for Analytics Excellence
Cathy Tanimura
Director of Analytics & Big Data @ Okta
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?