Finding your place in the analytics space (presentation at the Nashville Data Science meetup on...

Organiza(onal analy(cs: Finding your place in the analy(cs space world

Jeff Crawford, PhD, PMP Director of Graduate Programs & Associate Professor

School of Compu(ng and Informa(cs Lipscomb University

[email protected] hMp://technology.lipscomb.edu/

Master of Science (MS) in Informatics and Analytics w Information Security w IT Management w Software Engineering

[email protected] hMps://www.linkedin.com/in/crawdoctor

Presented at Data Science Nashville June 2, 2014

About me



•  Director of graduate programs in Lipscomb’s School of Compu(ng and Informa(cs – Associate Professor of IT Management and Informa(cs

•  Before academics, managed an intranet development group in a financial services organiza(on

•  And most importantly…

Crawford living out his rock and roll fantasies…

A Shameless Plug…



Lipscomb’s School of Compu(ng and Informa(cs offers the following graduate programs: – MS in Informa(on Security – MS in IT Management – MS in Informa(cs & Analy(cs – MS in SoUware Engineering

Programs are designed with working professionals in mind. Earn a MS degree in as liEle as 12 months. GRE is waived for those with 5 or more years work experience in their area of study. Now taking applicaKons for August, 2014.

Visit hMp://technology.lipscomb.edu/ to learn more and apply

What is analy(cs, exactly?



A reasonable view of analy(cs •  What? – using data to understand the past and/or address the present and/or predict the future

– Analysis suppor(ng data-‐driven decision-‐making

•  Why? – data -‐> informa(on -‐> decision-‐making -‐> effec(ve decision-‐making

– compe((ve necessity –  it’s in the trade press…





What is analy(cs, exactly?



Gartner’s 2013 Hype Cycle -‐ hEp://www.gartner.com/newsroom/id/2575515

The Analytics Process

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



The Analytics Process (by time)

From p. 255 of Klimberg, R., & McCullough, B. D. (2013). Fundamentals of predic(ve analy(cs with JMP. Cary, NC: SAS Ins(tute.

Data Mining Phase % Time Spent* Project defini(on (5%) Data collec(on (20%) Data prepara(on (30%) Data understanding (20%) Model development and evalua(on (20%) Implementa(on (5%)



* Remember the saying, “95% of all staKsKcs are false”

ORGANIZATIONAL ANALYTICS A holis(c view of



Organiza(onal Analy(cs?

Prahalad, C. K., & Hamel, G. (1990). The Core Competence of the Corpora(on. Harvard Business Review, 68(3), 79-‐91. Ulrich, D., & Smallwood, N. (2004). Capitalizing on Capabili(es. Harvard Business Review, 82(6), 119-‐127.

“the diversified corpora(on is a large tree…the root system that provides nourishment, sustenance, and stability is the core competence” (Prahalad & Hamel, 1990, p. 81)

“[capabili,es are] the collec(ve skills, abili(es and exper(se of an organiza(on” (Ulrich & Smallwood, 2004, p. 119) Facilita@ng Condi@ons

•  Corporate culture •  Execu(ve support •  Trends and “hype” •  Degree of compe((on •  Law, policy, ethics •  Others?



competencies aka

who you are

capabili,es aka

what you do

Analy(cs Competencies

Business knowledge

Analy(c knowledge

Informa(on Sharing

Tools / Applica(ons

Infrastructure

Project management



Business Knowledge

•  Analy(cs efforts flow from a context – Must know the ques(ons that need answering – Should know the ques(ons that don’t need answering

•  Analy(cs efforts have an objec(ve – Should be aligned with business strategy – A SWOT perspec(ve



Analy(cs Knowledge •  Classical sta(s(cs – Contemporary applica(on

•  Classical research methodology – Contemporary applica(on

•  Mathema(cs •  Informa(on structures •  Blue sky thinking (CAVU) •  Efficiency perspec(ve



Informa(on Sharing •  Knowledge processes (Tryon, 2012) – Discovery –  Capture – Organiza(on – Use –  Transfer –  Reten(on

•  Communica(on capabili(es – Data visualiza(on (Few, 2012) – Media richness (DaU & Lengel, 1986)

DaU, R.L. & Lengel, R.H. (1986). Organiza(onal informa(on 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: Analy(cs Press. Tryon, C. A. (2012). Managing organiza(onal knowledge: 3rd genera(on knowledge management and beyond. Boca Raton, FL: CRC Press.



Tools / Applica(ons •  Data mining / analysis –  Custom – Java, Python, .NET, etc. – Off the shelf -‐ SAS, SPSS, R, Oracle, MicrosoU, etc.

•  Data visualiza(on –  Tableau, Crystal Reports, etc.

•  Data extrac(on / prepara(on – Generalist tools

•  Spreadsheet, personal database, etc. – Data interac(on standards

•  SQL, JSON, XML, etc.



Infrastructure •  Contemporary informa(on structures require significant, some(mes novel investments in – SoUware & hardware •  Compute •  Storage •  Communica(ons

– Human capital •  Those producing analy(cs and those suppor(ng infrastructure ac(vi(es are likely not the same •  Acquisi(on, reten(on and development

– Sourcing arrangements



Project Management •  Analy(cs work (typically) has – Defined objec(ves – Dura(on (deadlines) – Stakeholders that need “managing” – Financial implica(ons – Sourcing arrangements

•  PM methodologies can help keep work on track – Can also cause a boMleneck…



Analy(cs Competencies

Business knowledge

Analy(c knowledge

Informa(on Sharing

Tools / Applica(ons

Infrastructure

Project management

Where do you fit?



Where is your organiza,on?

NOTE: The distance between areas is shrinking

Discussion



•  Where is your par(cular area of opportunity? – Look at yourself, your organiza(on, the environment for clues • What can you do well? • What won’t you do well? • Where is your passion? • Where are the people? (note… you don’t want to be where everyone else is)

* The term “understanding” encompasses both knowing and do-‐ing.

(A simple) Competency / Skills Assessment Worksheet IdenKfying OpportuniKes for Personal Growth

LEARNING FROM THE PAST Another opportunity by



Common failures within IT 1.  Assuming the value will be obvious 2.  Pushing the ar(fact over the ra(onale (i)T 3.  Crea(ng 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 Analy(cs. Harvard Business Review. 91, 1, 104-‐112.



1. Ac(vely communicate value •  Value is a percep(on 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 w Information Security w IT Management w Software Engineering


2. De-‐emphasize the tools focus •  Train for problem solving first –  Systema(c thinking –  Blue sky thinking –  Collabora(ve thinking

•  Unleash tools only aUer necessary skills have been developed –  “More (me on the I, less on the T” (Shah, Horne and Capella, 2012)

–  Allegiance to a solu(on, not a vendor •  The IT “agnos(c”

•  Invest in implemen(ng 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 analy(cs •  Refine the silo approach to analy(cs –  Centralized exper(se

•  Applica(on of specialized analy(cs knowledge with generalized context

–  Localized exper(se •  Applica(on of generalized analy(cs knowledge with specialized context

–  External exper(se •  Analy(cs as a source of compe((ve advantage (Dewhurst, Hancock and Ellsworth, 2013)

•  Analy(cs as a commodity (Carr, 2003)

Carr, N. G. (2003). IT Doesn't MaMer. 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 experimenta(on –  An agile perspec(ve on failure

•  Fail fast –  Sandboxes for “playing”

•  Train “informed skep(cs” (Shah, Horne and Capella, 2012) –  Ques(on common assump(ons, challenge authority

•  Enforce the scien(fic 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 ques(on to answer –  Does the ques(on have weight? – Would the answer clearly contribute to the organiza(on’s boMom line?

–  How important is the ques(on among the universe of other ques(ons you might address?

•  Adding value through exploita,on ac(vi(es –  Allow progressive elabora(on of the problem

•  AMack the problem in short itera(ve cycles (e.g., agile) •  Adding value through explora,on ac(vi(es –  Uncovering new and important ques(ons through experimenta(on

March, J. G. (1991). Explora(on and exploita(on in organiza(onal learning. Organiza(on Science, 2(1), 71-‐87.



6. Prac(ce inten(onal implementa(on •  Theory of Reasoned Ac(on (Fishbein & Ajzen, 1977) –  Behavior driven by inten(ons –  Inten(ons fed by

•  Aztudes •  Subjec(ve norms •  Perceived behavior control

–  An extension -‐ Technology Acceptance Model (Davis, 1989) •  Aztudes as “ease of use”, “usefulness”

•  Rogers’ Diffusion of Innova(ons (2003) –  Rate of adop(on (ed to understanding of adopter categories (innovators to laggards)



CONCLUSION Drawing it all together…



Date post:	20-Oct-2014
Category:	Data & Analytics
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Finding your place in the analytics space (presentation at the Nashville Data Science meetup on...

Data & Analytics