Building an Effective Organizational Analytics Capability

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Thoughts on organizational structures that are needed to build an effective analytics capability.

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Building an Effective Organizational Analytics Capability

  1. 1. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor Presented at the PMI Nashville 2014 Spring Symposium April 11, 2014 @ 12:30pm Music City Center, Nashville, TN
  2. 2. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  3. 3. What is analytics, exactly? Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  4. 4. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  5. 5. What is analytics, exactly? Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor Gartner’s 2013 Hype Cycle - http://www.gartner.com/newsroom/id/2575515
  6. 6. 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. jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor Master of Science (MS) in Informatics and Analytics  Information Security  IT Management 
  7. 7. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor * Remember the saying, “95% of all statistics are false”
  8. 8. ORGANIZATIONAL ANALYTICS Maturity through Competencies and Capabilities Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  9. 9. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor competencies aka who you are capabilities aka what you do
  10. 10. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  11. 11. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  12. 12. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  13. 13. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  14. 14. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  15. 15. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  16. 16. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  17. 17. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor Where is your organization? NOTE: The distance between areas is shrinking
  18. 18. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  19. 19. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor competencies aka who you are capabilities aka what you do
  20. 20. Analytics Capabilities Product / Process Improvement Research & Development CommercializationFinance and Fraud Business Operations Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  21. 21. –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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  22. 22. –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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  23. 23. Analytics Capabilities Product / Process Improvement Research & Development CommercializationFinance and Fraud Business Operations Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor Where are the opportunities for your organization?
  24. 24. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  25. 25. DIFFUSION OF ORGANIZATIONAL ANALYTICS Learning from IT’s (many and repeated) mistakes… Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  26. 26. 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. Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  27. 27. 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 Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  28. 28. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  29. 29. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  30. 30. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  31. 31. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  32. 32. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  33. 33. A few remaining thoughts… Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  34. 34. 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  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor
  35. 35. Get Educated Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu 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
  36. 36. CONCLUSION Drawing it all together… Master of Science (MS) in Informatics and Analytics  Information Security  IT Management  jeff.crawford@lipscomb.edu https://www.linkedin.com/in/crawdoctor

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