building an effective organizational analytics capability
DESCRIPTION
Thoughts on organizational structures that are needed to build an effective analytics capability.TRANSCRIPT
Building an Effective Organizational Analytics Capability
Jeff Crawford, PhD, PMPDirector of Graduate Programs & Associate Professor
School of Computing and InformaticsLipscomb University
[email protected] http://technology.lipscomb.edu/
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
Presented at the PMI Nashville 2014 Spring SymposiumApril 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
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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What is analytics, exactly?
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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…
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
What is analytics, exactly?
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
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.
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Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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%)
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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* Remember the saying, “95% of all statistics are false”
ORGANIZATIONAL ANALYTICSMaturity through Competencies and Capabilities
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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?
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
competenciesaka
who you are
capabilitiesaka
what you do
Analytics Competencies
Business knowledge
Analytic knowledge
Information Sharing
Tools / Applications
Infrastructure
Project management
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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Analytics Knowledge
• Classical statistics– Contemporary application
• Classical research methodology– Contemporary application
• Mathematics• Information structures• Blue sky thinking (CAVU)• Efficiency perspective
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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…
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
Analytics Competencies
Business knowledge
Analytic knowledge
Information Sharing
Tools / Applications
Infrastructure
Project management
Where do you fit?
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
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?
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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?
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
competenciesaka
who you are
capabilitiesaka
what you do
Analytics Capabilities
Product / Process Improvement
Research & Development
CommercializationFinance and Fraud
Business Operations
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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–Product / Process Improvement Analytics• Refining existing products / processes
–Research & Development Analytics• Uncovering new competitive opportunities
–Commercialization Analytics• Enhancing market opportunities for existing
products / processes
Analytics Capabilities
Core capability areas adapted from Burke, Jason. Health Analytics: Gaining the Insights to Transform Health Care. Hoboken, NJ: John Wiley & Sons, 2013.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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– 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 Burke, Jason. Health Analytics: Gaining the Insights to Transform Health Care. Hoboken, NJ: John Wiley & Sons, 2013.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
Analytics Capabilities
Product / Process Improvement
Research & Development
CommercializationFinance and Fraud
Business Operations
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
Where are the opportunities for your organization?
Competencies & Capabilities Maturity
Figure from Burke, Jason. Health Analytics: Gaining the Insights to Transform Health Care. Hoboken, NJ: John Wiley & Sons, 2013.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
DIFFUSION OF ORGANIZATIONAL ANALYTICS
Learning from IT’s (many and repeated) mistakes…
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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Common failures within IT1. Assuming the value will be obvious2. Pushing the artifact over the rationale (i)T3. Creating an IT silo4. Making a poor process faster5. Ignore / downplay the business problem6. 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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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 threatMaster of Science (MS) in Informatics and Analytics Information Security IT
Management Software Engineering
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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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
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.
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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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)
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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A few remaining thoughts…
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
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/
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
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Get Educated
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor
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
CONCLUSIONDrawing it all together…
Master of Science (MS) in Informatics and Analytics Information Security IT Management Software Engineering
[email protected] https://www.linkedin.com/in/crawdoctor