Decentralizing Analytics - A Strategy for Organizing Effective Analytics Teams

© 2019 Think Data Insights, LLC© 2019 Think Data Insights, LLC
Decentralizing Analytics
A Strategy That Works
Ken Raetz
CEO
Think Data Insights, LLC © 2019 Think Data Insights, LLC

• Data Platform architects; Azure, SQL Server
• Data Storytellers
• Power BI
• Business Consultants – We see the pain in the processes
• IT Consultants – We know the challenges
• “Data Culture Disrupters” “Data Wranglers”
Who are we?

Who are you?
Programmer/Architect
Executive LeaderSales/Marketing
IT Manager/Supervisor
Recruiting
Data Scientist
Business Manager IT/Infrastructure

A little about me…
• Have lived in Nashville, TN for 32 years
• Married 24 years
• I have 4 children
• I’m a nerd… I really like technology
• I am a small business owner
• I love helping entrepreneurs

Agenda
Our time today…
• Traditional Analytics teams – Structure, Roles, Challenges
• Analytics market trends
• Decentralizing Analytics
• Customer Examples
• Organizational Strategy
• Personal/Individual Strategy

© 2019 Think Data Insights, LLC© 2019 Think Data Insights, LLC 6
Analytics in an Organization
Data
Management
Business
Intelligence
and
Reporting
Data Science

UX Designer
7
Analytics Team - Roles
Director/
Manager
Architect
Business
Analyst
Project
Manager
ETL/Report
Developers
DBA/Data
Modeler
Trainer
Quality
Assurance

Analytics Team - Structure
8
Data Services/
BI Team
Shared Resources Other Teams
• Director/Manager
• ETL Developer
• Visualization/Report
Developer
• Architect
• Project Manager
• Business Analyst
• DBA
• Data Modeler
• UX Designer
• QA

Organizational Workflow
Company
determines
objectives
Business leaders
drive requirements
Business requests
data result
IT-owned BI
responds
BI Team scrubs and
prepares
data/report
BI Content
Developed
User
Testing/Acceptance
Deployment

• Insufficient BI team resources
• Poor data quality and lack of integration
• Insufficient scalability of the team
• Limited user skills (and patience to wait)
• So Users wait…
10
Challenges
and wait…
and wait…
and wait…
and wait…
and do it on their own…

Big Data/Analytics Market Trends
$189b 2019
$274b 2022
Automation
Rise of the
data citizen
Back to the
basics
3rd party data
Sources:
https://www.idc.com/getdoc.jsp?containerId=prUS44998419
https://www.cio.com/article/3251720/4-data-analytics-trends-that-will-dominate-2018.html?upd=1561460668231

Big Data/Analytics Predictions
Sources:
https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/
40%
Data Science Tasks Automated 90%
Large enterprises will
generate revenue from
data as a service
20%
IT specialists reduction by 2023

• By 2022, 90% of corporate strategies will explicitly mention information as a critical
enterprise asset and analytics as an essential competency.
• By 2023, data literacy will become an explicit and necessary driver of business value,
demonstrated by its formal inclusion in over 80% of data and analytics strategies and
change management programs.
• By 2022, 30% of CDOs will partner with their CFO to formally value the organization’s
information assets for improved information management and benefits.
• By 2023, 60% of organizations with more than 20 data scientists will require a
professional code of conduct incorporating ethical use of data and AI.
Sources:

• By 2022, more than half of major new business systems will incorporate continuous
intelligence that uses real-time context data to improve decisions.
• Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents
will not scale in the organization.
• By 2021, analytic projects using quantum computing infrastructure will have
outperformed traditional analytic approaches by at least a factor of 10
• By 2022, 30% of consumers in mature markets will rely on AI to decide what they eat,
what they wear or where they live.
• By 2022, 30% of organizations will use explainable AI models to build trust with
business stakeholders, up from almost no usage today.
Sources:

• By 2023, a Fortune 1000 antitrust case will hinge on whether tacit cooperation among
autonomous AI agents in competitive markets constitutes collusion.
• By 2023, over 75% of large organizations will hire AI behavior forensic, privacy and customer trust
specialists to reduce brand and reputation risk.
• By 2022, 50% of cloud buying decisions will be based on the data assets provided by cloud
service providers rather than on the product capabilities.
• By 2023, AI-enabled automation in data management will reduce the need for IT specialists by 20%.
• By 2023, 75% of all databases will be on a cloud platform, reducing the DBMS vendor landscape
and increasing complexity for data governance and integration.
Sources:

• By 2022, organizations … that dynamically connect, optimize and automate data
integration processes will reduce time to data delivery by 30%.
• By 2021, enterprises using a cohesive strategy incorporating data hubs, lakes and
warehouses will support 30% more use cases than competitors.
• Through 2023, computational resources used in AI will increase 5x from 2018,
making AI the top category of workloads driving infrastructure decisions.
• Through 2022, only 15% of use cases leveraging AI techniques (such as ML and DNNs)
and involving edge and IoT environments will be successful.
Sources:

• Data/BI work is often not as exciting
• People want to be challenged
• Organizational frustration – IT vs. Business
• Companies looking for ways to cut costs; reduce
traditional teams
• When you do your job, BI/Reporting becomes “stable”
17
Ken’s Organizational Observations
There is a better way…

Decentralizing Analytics – The team of the future
Director/Manager
Analytics
Consultant
Data Science
Data
Storytellers
Citizen Data
Scientists

Citizen Data Scientist

Citizen Data Scientist
“A person who creates or generates models that use
advanced diagnostic analytics or predictive and
prescriptive capabilities, but whose primary job
function is outside the field of statistics and analytics.”
Gartner

Organizational Strategy – Where do I start?
Approach
• Identify/hire/train Citizen Data Scientists in the organization
• Build data foundations for clean, accessible and scalable data
• Agile, iterative development
• Solve immediate challenges
• Build toward future strategy
• Lean Data Services/BI team – Fewer specialties, more diversity of skills
• Commitment to pursue deeper data science
• Repair/build relationship between IT/Business

Customer Examples – Restaurant/Hospitality
West Coast Restaurant Chain
• Financial Reporting/Forecasting/Planning App
• IT Team involved Finance team (Sr. Financial Analyst) throughout project
• IT Team built integrations, central data platform, calculation library
Sr. Financial Analyst – Patrick
Sr. Operational Analyst – John
Citizen Data Scientists in the making

Customer Examples – Commodity/Manufacturing
Milling – 15 Locations in US
• Centralized ERP solution
• IT team building one report per request
• Deep SQL and SME knowledge required for each report
Now?
• IT Team led with vision for next generation platform
• Move to cloud data platform
• Selected different analytics/reporting tool
• Focus shifting to strategic approach;
• Multiple Citizen Data Scientists developing now

Individual/Personal Strategy
Executive
• Identify data opportunities (monetization, productize)
• Topgrade your IT/Business leadership
• Budget for exploratory data science
• Find/keep Data Services or “BI” Director
• Promote culture of Citizen Data Science with peers

BI Director/Manager
• Engage business
• Recruit Citizen Data Scientists in business
• Data quality and Data governance initiatives
• Reduce BI workload/feature requests
• Move to Agile approach
• Learn the art of data storytelling
• Recruit data storytellers

Operational/Financial Director/Manager
• Engage IT and Data Services team
• Identify your existing “Power Analysts”
• Create backlog of data quality issues;
• Take ownership of data quality/governance
• Promote best practices to business
• Recruit data storytellers

BI Developer
• Learn data science (school, online video)
• Learn business – MBA, books, videos
• Sharpen data quality, data governance skills
• Learn UX principles
• Master the art of data storytelling

Analyst
• Learn languages: SQL, DAX, Python, R
• Communication skills
• IT principles (scale, security, standardization)
• Sharpen data quality, data governance skills
• Learn UX principles
• Master the art of data storytelling

History Lessons

• Speaks business and IT
• Rapid solutions
• Growing in data science
• Adheres to standards
• Support data quality initiatives
• Trains/mentors others
• Promotes culture of Data
Democratization
Citizen Data Scientist (aka “Data Wrangler” or “Data Analyst”)
Analytics – A New Role

Decentralizing Analytics
A Strategy That Works
Ken Raetz
CEO
Think Data Insights, LLC
QUESTIONS?
© 2019 Think Data Insights, LLC

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