Presentation regarding: Key capabilities for analytic development Stages of analytic development in organizations Organizational approaches to analytic teams Evolving models of analytic roles and leadership Implications of these developments

1. Enterprise analytics:
Strategies and partnerships
William O’Shea
Pacific University
Presentation to DAMA - Portland Metro Chapter
January, 2015 Chapter Meeting
2015/01/20

2. 2000 2005 2010 2015
2000 2005 2010 2015
SQL
Background
Technical Experience

3. What do you think
about analytics?

4. Ferguson, March 24, 2014 , http://sloanreview.mit.edu/article/rent-the-runway-organizing-around-analytics/

8. Goals
• That you will learn about
• Key capabilities for analytic development
• Stages of analytic development in organizations
• Organizational approaches to analytic teams
• Evolving models of analytic roles and leadership
• That we will discuss the implications of these
developments

9. What are analytics?
• “Extensive use of data, statistical and quantitative
analysis, explanatory and predictive models, and
fact-based management to drive decisions and
actions”
Davenport and Harris, 2007

10. Analytics as continuum
Optimization What is the best that can happen?
Predictive Modeling What will happen next?
Forecasting What if these trends continue?
Statistical Analysis Why is this happening?
Alerts What needs to be done now?
Strategic Reports Are we effecting our goals?
Normative Reports How do we compare?
Standard Reports What happened?
Advanced
Analytics
Access and
Reporting
Degrees of Insight
AdvancedDecisionMaking
Adapted from Davenport and Harris, 2007

11. Why analytics?
Adapted from Bostic, 2014

12. What is being data/analytic
driven?
• “Analytic competitors, then, are organizations that have
selected one or a few distinctive capabilities on which to
base their strategies, and then have applied extensive
data, statistical and quantitative analysis, and fact-based
decision making to support the selected capabilities.”
Davenport and Harris, 2007
• “A data-driven organization acquires, processes, and
leverages data in a timely fashion to create efficiencies,
iterate on and develop new products, and navigate the
competitive landscape.”
DJ Patil, 2011

13. What are key factors and
milestones for developing
the analytic capacity of
an organization?

14. DELTA Model of Success Factors
D Accessable, high quality data
E Enterprise orientation
L Analytic leadership
T Strategic targets
A Analysts
From Davenport, Harris, & Morison, 2010

15. Accessible, high quality data
• Data management efforts form a foundation for analytics
• Key considerations
• Uniqueness
• Integration
• Quality
• Access
• Governance
From Davenport, Harris, & Morison, 2010

16. Key data considerations
from a data management
perspective?

17. Enterprise orientation
• Broad business perspective
• Impact across organization
• Data across silos
• Enterprise wide analytic infrastructure
From Davenport, Harris, & Morison, 2010

18. Analytical leadership
• Leader support for analytics key to success
• Articulate what needs to be accomplished and how
to measure success
• Build analytical ecosystem
• Know the limits of analytics
From Davenport, Harris, & Morison, 2010

19. Strategic targets
• Target distinctive capabilities
• Find opportunities
• Big-picture thinking
• Inventory how processes are structured and
function
• Prioritize based on benefits and capabilities
From Davenport, Harris, & Morison, 2010

20. Analysts
• “Workers who use statistics, rigorous quantitative or qualitative
analysis, and information modeling techniques to shape or
make business decisions”
• Skills
• Quantitative and technical
• Business knowledge and design
• Relationship and consulting
• Coaching and staff development
• Manage as strategic resource
From Davenport, Harris, & Morison, 2010

21. DELTA Model of Success Factors
D Accessable, high quality data
E Enterprise orientation
L Analytic leadership
T Strategic targets
A Analysts
From Davenport, Harris, & Morison, 2010

22. Stages of analytic development
Stage 5
Analytic
Competitors
Stage 4
Analytic
Companies
Stage 3
Analytic
Aspirations
Stage 2
Localized
Analytics
Stage 1
Analytically
Impared
From Davenport and Harris, 2007

23. Stage 1: Analytically impaired
• Negligible analytics
• Questions focus on the past - what happened
• Need accurate data for operations
Adapted from Davenport and Harris, 2007

24. Moving From Stage 1 to 2
• Data - Master important data; develop data marts
• Enterprise - Find allies for small projects; partner with IT
• Leadership - Encourage development of analytic
leaders across units
• Targets - Start with low-hanging fruit
• Analysts - Find pockets of analysts; enlist managers to
engage analytic employees
From Davenport and Harris, 2007

25. Stage 2: Localized analytics
• Analytics limited to small pockets; opportunistic
• Questions regarding better understanding and how
to improve
• Need to move to more systematic application of
analytics
• Can start to measure ROI at project level
Adapted from Davenport and Harris, 2007

26. Moving From Stage 2 to 3
• Data - Consensus on data needs for analytic targets and further
develop warehouses/marts; motivate cross-functional data
• Enterprise - Begin building enterprise infrastructure and initial policies
• Leadership - Create shared vision for analytics and necessary
capabilities
• Targets - Target business processes; start systematic inventory of
analytic opportunities
• Analysts - Define and fill analytic positions; provide coaching and
support for analysts
From Davenport and Harris, 2007

27. Stage 3: Analytical aspirations
• Beginning integrated data and analytics
• Questions are more timely and seek to forecast
trends
• Focus analytics more on distinctive capabilities
• Assess value on broader performance gains and
support for mission
Adapted from Davenport and Harris, 2007

28. Moving From Stage 3 to 4
• Data - Further development of data warehouses/marts with senior
management involvement; cultivate unique data
• Enterprise - Develop analytics strategy and roadmap for enterprise;
establish analytics governance
• Leadership - Engage senior leaders in developing analytical capabilities
(e.g., data, technology, analysts)
• Targets - Work with major process owners; evaluate opportunities on an
enterprise basis; develop collaborative targeting process
• Analysts - Raise analytic capabilities of all information workers; cross-
train and develop communities of analysts (e.g., user groups)
From Davenport and Harris, 2007

29. Stage 4: Analytical companies
• Enterprise level analytics
• Driving analytics to innovate and differentiate
• Broadening analytic practice and advancing
support of strategic differentiation
• Analytics seen as an important driver of
organizational value and mission fulfillment
Adapted from Davenport and Harris, 2007

30. Moving From Stage 4 to 5
• Data - Educate and engage senior executives in competitive value of
data; exploit unique data; advance data governance
• Enterprise - Manage analytic priorities and review; extend analytic
infrastructure broadly and deeply across the organization
• Leadership - Encourage leaders to show analytic capabilities and
communicate importance of analytics
• Targets - Work with executive team on strategic initiatives; integrate with
strategic planning process
• Analysts - Hire for analytic mindedness across organization; organize
and deploy analysts centrally; recognize analytic contributions
From Davenport and Harris, 2007

31. Stage 5: Analytic competitors
• Enterprise-wide analytics; support for distinctive
capabilities to create sustainable advantage
• Questions regarding how best to innovate with
analytics to sustain advantage
• Focus on competing on analytics
• Analytics as the primary/major driver of
performance and mission fulfillment
Adapted from Davenport and Harris, 2007

32. Stages of analytic development
Stage 5
Analytic
Competitors
Stage 4
Analytic
Companies
Stage 3
Analytic
Aspirations
Stage 2
Localized
Analytics
Stage 1
Analytically
Impared
From Davenport and Harris, 2007

33. About what stage do you
think your organization is
at?

34. How might such analytic
development affect the
structure and roles in an
organization?

35. Implications of analytic
development
•Organizing analytic teams
•Leadership roles
•Analyst roles

36. Organizing analytic teams
• Centralized
• Consulting
• Functional
• Center of excellence
• Decentralized
From Davenport, Harris, & Morison, 2010

37. Centralized analytics model
From Davenport, Harris, & Morison, 2010
Division Function
Corporate
Analytics
Analytic Project Analytic Project

38. Consulting analytics model
From Davenport, Harris, & Morison, 2010
Division Function
Corporate
Analytics
Analytic Project Analytic Project

39. Functional analytics model
From Davenport, Harris, & Morison, 2010
Division Function
Corporate
Analytics
Analytic Project
Analytic Project

40. Center of excellence model
From Davenport, Harris, & Morison, 2010
Division Function
Corporate
Analytics COE
Analytic Project Analytic Project
Analytics Analytics

41. Decentralized analytics model
From Davenport, Harris, & Morison, 2010
Division Function
Corporate
Analytic Project Analytic Project
Analytics Analytics

42. New Leadership Roles
• Chief Data Officer (CDO)
• Chief Analytics Officer (CAO)
• Chief Data Scientist (CDS)
CAO
CDS
CDO

43. Chief Data Officer
• Leads strategic
data
management
and use
• Focused on
leveraging data
as an asset
Usama Fayyad
Barclays Bank
Build and operate global data
infrastructure
Examples
Todd Cullen
Ogilvy & Mather
Identifying unique and emerging
data sources and techniques
Inderpal Bhandari
Cambia Health
Lead the development of data strategy

44. Chief Analytics Officer
• Leads strategic
application of
analytics
• Focused on
decision
making
Andrea Marks
Catamaran
Advance analytics to improve
outcome and efficiencies
Examples
Bill Franks
Teradata
Accountable for strategic analytic
decisions
Vijay Subramanian
Rent the Runway
Modeled demand, longevity, and use

45. Chief Data Scientist
• Leads
development of
algorithm-
based
products/
services Chris Wiggins
New York Times
Leading “machine learning team”
Examples
Hillary Mason
bitly
Finding value and building systems
John Foreman
MailChimp
Build tools to improve the application

46. Expanded Understanding
of Analysts
• Analytical champions
• Analytical professionals
• Analytical semiprofessionals
• Analytical amateur
From Davenport, Harris, & Morison, 2010

47. Implications for collaboration
among analytic, data
management, and other IT
functions?

48. Review of Goals
• That you will learn about
• Key capabilities for analytic development
• Stages of analytic development in organizations
• Organizational approaches to analytic teams
• Evolving models of analytic roles and leadership
• That we will discuss the implications of these
developments

49. Resources
Davenport, Harris, & Morison (2010) Analytics at
Work
Davenport and Harris (2007) Competing on Analytics
International Institute for Analytics, iianalytics.com

50. Questions and answers
Q: Do the various analytic organization approaches
scale to larger companies?
A: Some examples
Centralized: Mars, Expedia
Consulting: United Airlines, eBay
Functional: Fidelity
Center of Excellence: Capital One, Bank of America
From Davenport, Harris, & Morison, 2010

51. Questions and answers
Q: What indicators are there that companies using
analytics perform better?
A: From Brynjolfsson, Hitt, and Kim (2011)
“Using detailed survey data on the business practices and information
technology investments of 179 large publicly traded firms, we find that firms
that adopt DDD have output and productivity that is 5- 6% higher than what
would be expected given their other investments and information technology
usage. Furthermore, the relationship between DDD and performance also
appears in other performance measures such as asset utilization, return on
equity and market value.”
Brynjolfsson, Erik and Hitt, Lorin M. and Kim, Heekyung Hellen, Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm
Performance? (April 22, 2011).

52. William O’Shea
osheawa@pacificu.edu
www.linkedin.com/pub/william-o-shea/24/216/74b/