Building an Effective Organizational Analytics Capability

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

mailto:[email protected]

http://technology.lipscomb.edu/

https://www.linkedin.com/in/crawdoctor



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




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 ANALYTICSMaturity 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?



competenciesaka

who you are

capabilitiesaka

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?



competenciesaka

who you are

capabilitiesaka

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 Burke, Jason. Health Analytics: Gaining the 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 Burke, Jason. Health Analytics: Gaining the 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 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.






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





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/



http://www.meetup.com/Data-Science-Nashville/

http://www.meetup.com/Data-Science-Nashville/

https://www.yammer.com/greaternashvillehealthcareanalytics



http://www.meetup.com/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




http://technology.lipscomb.edu/

CONCLUSIONDrawing it all together…






Education

Building an Effective Organizational Analytics Capability