Quick Tips on Building a Data Science Org for Executives

1. Building
a Data
Science
Org
Ad meliora

2. How Businesses Use Data Science
•Data Publication
•Sentiment Analysis
•Adaptive
Marketing
•Mass Customer
Feedback Analysis
•Personalized Analytics
•Benchmarking
•Predictive /
Prescriptive Analytics
(How to improve Credit
ratings etc..)
•Fraud Detection
•Customer
Identification and
Segmentation
•Risk Management
•Advanced Business
Intelligence
•Competitive Analysis
•Trends & Forecasting
•Market Performance
Internal
Informational
Services
Business
Operational
Marketing
Product
Development

3. Team Structure
DataScience Business
Analyst
Machine
Leaning SME
Engineering
Quality Control
• Business Analyst / Product Manager
 Strong understanding of the business unit, corporate vision
and customer or corporate facing.
 Functions as an internal champion for a seldom
understood team.
 Delivers and brings awareness of accomplishments and
capabilities.
 Capable of defining a vision and roadmap for Data Science
• Machine Learning SME
 Brings Data Analytics skills
 Strong Story Telling with Data & Technology skills
 Constantly updating technology skills in a bleeding edge,
rapidly moving sector.
• Engineering
 Go To Market, Productionize and Automate
 Eliminate the burn of reproducing output
• Quality Control
 Outside of functionality, validate the reasoning and
likelihood of results.
 Data fluent and customer centric
 Focused on does the data & story make sense

4. Challenges to Data Science Success
• Corporate Maturity
 Data Science is a mid to long term
investment
 Data Ownership must transform from
siloed to centralized
 Output is frequently a Numeric KPI
 Expectations are often unknown and
unrealistic, hard to make data sexy for
average person
 Budgeting a department that is
advanced & traditional research based
requires CFO buy in
• Data Science takes about 18 – 24
months for ROI
 Requires mapping out all available data
in a company
 Span across Organizational Silos
 Navigate legal regulations, privacy
policies, license agreements
 Identify possibilities and match with
Corporate needs
 Develop vision and Roadmap
 Achieve buy, funding and resourcing
 Product Development requires existing
Product Owners to co-own priority and
delivery.
 Source additional 3rd party data and fill
gaps

5. How to succeed
• The first 3 – 6 months are critical to get
introduced to the company
• Data is political in most companies
 Identify projects that are not conflicting and have
recognizable early wins
 Set teams up for immediate success and future buy
in & collaboration
• Define a moonshot
 Starter projects help build synergy
 Familiarize the team with the business and
customers
 A moonshot brings focus and objective and drives
the creation of a roadmap.
• Buy Data
 Data enhancement is key to growing opportunity
 In B2C it provides adaptive behavior that increases
interaction
 In B2B is provides Benchmarking that is impossible
for your customer to build about themselves or
competitors.
• Common Starter Projects
 Customer Support issue identification /
product deficiency / product usage
 What works in your product
 What doesn’t
 What has high value, requires investment to
improve
 Sales
 Customer churn factors
 Usage or lack of
 Support calls - resolved / unresolved
 Market Identification
 Product / Customer Success
 Customer Opportunities