Building an Effective Organizational Analytics Capability

Building an Effective Organizational
Analytics Capability
Jeff Crawford, PhD, PMP
Director of Graduate Programs & Associate Professor
School of Computing and Informatics
Lipscomb University
jeff.crawford@lipscomb.edu
http://technology.lipscomb.edu/
Master of Science (MS) in Informatics and Analytics  Information Security  IT Management 
https://www.linkedin.com/in/crawdoctor
Presented at the PMI Nashville 2014 Spring Symposium
April 11, 2014 @ 12:30pm
Music City Center, Nashville, TN

Presentation Thesis
• For organizational analytics to be
maximally effective, you must:
–Take a holistic, long-term view of analytics
• Think in terms of competencies, capabilities
and facilitating conditions
–Practice intentional implementation
• Take a cue from IT

What is analytics, exactly?

A reasonable view of analytics
• What?
– using data to understand the past and/or address the
present and/or predict the future
• Why?
– data -> information -> decision-making -> effective
decision-making
– competitive necessity
– it’s in the trade press…

What is analytics, exactly?
Gartner’s 2013 Hype Cycle - http://www.gartner.com/newsroom/id/2575515

The Analytics Process
Figure 2.2: The Cross Industry
Standard Process (CRISP) for data
mining
Provost, F., & Fawcett, T. (2013).
Data science for business: What
you need to know about data
mining and data-analytic thinking.
Sebastpol, CA: O'Reilly Media.

The Analytics Process (by time)
From p. 255 of Klimberg, R., & McCullough, B. D. (2013). Fundamentals of
predictive analytics with JMP. Cary, NC: SAS Institute.
Data Mining Phase % Time Spent*
Project definition (5%)
Data collection (20%)
Data preparation (30%)
Data understanding (20%)
Model development and evaluation (20%)
Implementation (5%)
* Remember the saying, “95% of all statistics are false”

ORGANIZATIONAL ANALYTICS
Maturity through Competencies and Capabilities

Organizational Analytics?
Prahalad, C. K., & Hamel, G. (1990). The
Core Competence of the Corporation.
Harvard Business Review, 68(3), 79-91.
Ulrich, D., & Smallwood, N. (2004).
Capitalizing on Capabilities. Harvard
Business Review, 82(6), 119-127.
“the diversified
corporation is a large
tree…the root system that
provides
nourishment, sustenance,
and stability is the core
competence” (Prahalad &
Hamel, 1990, p. 81)
“*capabilities are] the
collective skills, abilities
and expertise of an
organization” (Ulrich &
Smallwood, 2004, p. 119)
Facilitating Conditions
• Corporate culture
• Executive support
• Trends and “hype”
• Degree of competition
• Law, policy, ethics
• Others?
competencies
aka
who you are
capabilities
aka
what you do

Analytics Competencies
Business
knowledge
Analytic
knowledge
Information
Sharing
Tools /
Applications
Infrastructure
Project
management

Business Knowledge
• Analytics efforts flow from a context
– Must know the questions that need answering
– Should know the questions that don’t need
answering
• Analytics efforts have an objective
– Should be aligned with business strategy
– A SWOT perspective

Analytics Knowledge
• Classical statistics
– Contemporary application
• Classical research methodology
– Contemporary application
• Mathematics
• Information structures
• Blue sky thinking (CAVU)
• Efficiency perspective

Information Sharing
• Knowledge processes (Tryon, 2012)
– Discovery
– Capture
– Organization
– Use
– Transfer
– Retention
• Communication capabilities
– Data visualization (Few, 2012)
– Media richness (Daft & Lengel, 1986)
Daft, R.L. & Lengel, R.H. (1986).
Organizational information
requirements, media richness and
structural design. Management
Science 32(5), 554-571.
Few, S. (2012). Show me the
numbers: Designing tables and
graphs to enlighten. (2nd ed. ed.).
Burlingame, CA: Analytics Press.
Tryon, C. A. (2012). Managing
organizational knowledge: 3rd
generation knowledge
management and beyond. Boca
Raton, FL: CRC Press.

Tools / Applications
• Data mining / analysis
– Custom – Java, Python, .NET, etc.
– Off the shelf - SAS, SPSS, R, Oracle, Microsoft, etc.
• Data visualization
– Tableau, Crystal Reports, etc.
• Data extraction / preparation
– Generalist tools
• Spreadsheet, personal database, etc.
– Data interaction standards
• SQL, JSON, XML, etc.

Infrastructure
• Contemporary information structures require
significant, sometimes novel investments in
– Software & hardware
• Compute
• Storage
• Communications
– Human capital
• Those producing analytics and those supporting
infrastructure activities are likely not the same
• Acquisition, retention and development

Project Management
• Analytics work (typically) has
– Defined objectives
– Duration (deadlines)
– Stakeholders that need “managing”
– Financial implications
– Sourcing arrangements
• PM methodologies can help keep work on
track
– Can also cause a bottleneck…

Analytics Competencies
Business
knowledge
Analytic
knowledge
Information
Sharing
Tools /
Applications
Infrastructure
Project
management
Where do you
fit?
Where is your
organization?
NOTE: The
distance between
areas is shrinking

Discussion
• What is the opportunity for a project manager
that is new to analytics?
• What are the tangible barriers?

Organizational Analytics?
Prahalad, C. K., & Hamel, G. (1990). The
Core Competence of the Corporation.
Harvard Business Review, 68(3), 79-91.
Ulrich, D., & Smallwood, N. (2004).
Capitalizing on Capabilities. Harvard
Business Review, 82(6), 119-127.
Facilitating Conditions
• Corporate culture
• Executive support
• Trends and “hype”
• Degree of competition
• Law, policy, ethics
• Others?
competencies
aka
who you are
capabilities
aka
what you do

Analytics Capabilities
Product / Process
Improvement
Research &
Development
CommercializationFinance and Fraud
Business Operations

–Product / Process Improvement Analytics
• Refining existing products / processes
–Research & Development Analytics
• Uncovering new competitive opportunities
–Commercialization Analytics
• Enhancing market opportunities for existing
products / processes
Core capability areas adapted from
Burke, Jason. Health Analytics: Gaining the
Insights to Transform Health Care. Hoboken, NJ:
John Wiley & Sons, 2013.

–Finance and Fraud Analytics
• Exposing financial risks and opportunities
–Business Operations Analytics
• Clarifying areas of operational improvement
Analytics Capabilities (cont.)
Core capability areas adapted from
Insights to Transform Health Care. Hoboken, NJ:
John Wiley & Sons, 2013.

Product / Process
Improvement
Research &
Development
CommercializationFinance and Fraud
Business Operations
Where are the
opportunities
for your
organization?

Competencies & Capabilities Maturity
Figure from
Insights to Transform Health Care. Hoboken,
NJ: John Wiley & Sons, 2013.

DIFFUSION OF ORGANIZATIONAL
ANALYTICS
Learning from IT’s (many and repeated) mistakes…

Common failures within IT
1. Assuming the value will be obvious
2. Pushing the artifact over the rationale (i)T
3. Creating an IT silo
4. Making a poor process faster
5. Ignore / downplay the business problem
6. Fail to acknowledge the diffusion process
Adapted from Marchand, D.A. and Peppard, J., 2013. Why IT Fumbles Analytics.
Harvard Business Review. 91, 1, 104-112.

1. Actively communicate value
• Value is a perception defined by the individual
– “Selling” is a key part of the process
• What you see as value, others might see as
– Change
• Process change
• Culture change
• Power change
– Complexity & Chaos
• The language of data
• The order of logic
– A threat

2. De-emphasize the tools focus
• Train for problem solving first
– Systematic thinking
– Blue sky thinking
– Collaborative thinking
• Unleash tools only after necessary skills
have been developed
– “More time on the I, less on the T”
(Shah, Horne and Capella, 2012)
– Allegiance to a solution, not a vendor
• The IT “agnostic”
• Invest in implementing the process, not just
the IT tools / infrastructure
Shah, S., Horne, A., &
Capellá, J. (2012). Good
Data Won't Guarantee
Good Decisions. Harvard
Business
Review, 90(4), 23-25.

3. Properly structure analytics
• Refine the silo approach to analytics
– Centralized expertise
• Application of specialized analytics knowledge
with generalized context
– Localized expertise
• Application of generalized analytics knowledge
with specialized context
– External expertise
• Analytics as a source of competitive advantage
(Dewhurst, Hancock and Ellsworth, 2013)
• Analytics as a commodity (Carr, 2003)
Carr, N. G. (2003). IT Doesn't
Matter. Harvard Business
Review, 81(5), 41-49.
Dewhurst, M., Hancock, B., &
Ellsworth, D. (2013).
Redesigning Knowledge Work.
Harvard Business
Review, 91(1), 58-64.

4. Nurture a learning culture
• Solving today’s problems is not always the right
approach
– How do you get people to think where the ball is
going?
• Allow experimentation
– An agile perspective on failure
• Fail fast
– Sandboxes for “playing”
• Train “informed skeptics” (Shah, Horne and
Capella, 2012)
– Question common assumptions, challenge authority
• Enforce the scientific method
Shah, S., Horne, A., &
Capellá, J. (2012). Good
Data Won't Guarantee
Good Decisions. Harvard
Business
Review, 90(4), 23-25.

5. Focus on the business problem
• It’s not enough to have a question to answer
– Does the question have weight?
– Would the answer clearly contribute to the
organization’s bottom line?
– How important is the question among the
universe of other questions you might address?
• Adding value through exploitation activities
– Allow progressive elaboration of the problem
• Attack the problem in short iterative cycles (e.g., agile)
• Adding value through exploration activities
– Uncovering new and important questions through
experimentation
March, J. G. (1991).
Exploration and
exploitation in
organizational
learning. Organization
Science, 2(1), 71-87.

6. Practice intentional implementation
• Theory of Reasoned Action (Fishbein & Ajzen, 1977)
– Behavior driven by intentions
– Intentions fed by
• Attitudes
• Subjective norms
• Perceived behavior control
– An extension - Technology Acceptance Model (Davis, 1989)
• Attitudes as “ease of use”, “usefulness”
• Rogers’ Diffusion of Innovations (2003)
– Rate of adoption tied to understanding of adopter
categories (innovators to laggards)

A few remaining thoughts…

Get Involved in the Analytics Community
• NTC Analytics Peer Network
– site on LinkedIn
• Nashville Tech Breakfast
– 7/15/14 @ Spark in Cool Springs: From Japan to Nashville, Mexico,
Brazil and Beyond: Lessons learned during the geographic expansion of
IT capabilities - a panel discussion with Nissan Americas Vice President
of Information Systems, Steve Lambert, and team.
• Data Science Nashville
– http://www.meetup.com/Data-Science-Nashville/
• Greater Nashville Healthcare Analytics
– https://www.yammer.com/greaternashvillehealthcareanalytics
• Nashville R Users Group
– http://www.meetup.com/Nashville-R-Users-Group/

Get Educated
Lipscomb’s School of Computing and Informatics
offers the following graduate programs:
– MS in Information Security
– MS in IT Management
– MS in Informatics & Analytics
– MS in Software Engineering
Programs are designed with working professionals in mind. Earn a MS degree in as
little as 12 months. GRE is waived for those with 5 or more years work experience
in their area of study. Now taking applications for August, 2014.
Visit http://technology.lipscomb.edu/ to apply

CONCLUSION
Drawing it all together…

Building an Effective Organizational Analytics Capability

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