Analytics Job Task Analysis

The Certified Analytics Professional (CAP®) designation will be based on 1) meeting the eligibility criteria, 2) verification of soft skills/provision of business value by your employer, and 3) passing the CAP® examination. 

The CAP® examination is based on a delineation of common or typical tasks (T) performed and knowledge (K) applied by Analytics Professionals. In the course of analytics work, these tasks may be performed multiple times with modifications made based on data, findings and results as part of ongoing feedback loops. (For clarity and simplicity most of the feedback loops are not presented in this document. It is assumed and understood that they are a routine part of practice.)

The JTA outlined below was developed by the INFORMS Analytics Credentialing Job Task Analysis Working Group, comprised of 12 subject matter experts (SMEs) (see nearby list) who are: highly regarded in their field; diverse in geography, sector (public-private), organization type (e.g., large companies-smaller consulting firms, practice-academia, etc.), and application area (e.g., finance, logistics, software, consumer goods, etc.); representation of the descriptive, predictive, and prescriptive segments of analytics.

The 36 typical tasks and 16 knowledge statements (not provided here) in the analytics JTA are organized in seven domains or large areas of responsibility, as listed in the nearby table. Also shown are domain weights, which are based on the SMEs assessments of the importance of tasks and the frequency of their performance. 

(12-18%)  Domain I        Business Problem (Question) Framing

T-1       Obtain or receive problem statement and usability requirements
T-2       Identify stakeholders
T-3       Determine if the problem is amenable to an analytics solution
T-4       Refine the problem statement and delineate constraints
T-5       Define an initial set of business benefits
T-6       Obtain stakeholder agreement on the problem statement        

(14-20%)  Domain II       Analytics Problem Framing

T-1       Reformulate the problem statement as an analytics problem
T-2       Develop a proposed set of drivers and relationships to outputs
T-3       State the set of assumptions related to the problem
T-4       Define key metrics of success
T-5       Obtain stakeholder agreement

(18-26%)  Domain III     Data

T-1       Identify and prioritize data needs and sources
T-2       Acquire data
T-3       Harmonize, rescale, clean and share data
T-4       Identify relationships in the data
T-5       Document and report findings (e.g., insights, results, business performance)
T-6       Refine the business and analytics problem statements         

(12-18%)  Domain IV     Methodology (Approach) Selection

T-1       Identify available problem solving approaches (methods)
T-2       Select software tools
T-3       Test approaches (methods)1
T-4       Select approaches (methods) 1

(13-19%)  Domain V       Model Building

T-1       Identify model structures1
T-2       Run and evaluate the models
T-3       Calibrate models and data1
T-4       Integrate the models1
T-5       Document and communicate findings (including assumptions, limitations and constraints)

(7-11%)    Domain VI     Deployment

T-1       Perform business validation of the model
T-2       Deliver report with findings; or
T-3       Create model, usability and system requirements for production
T-4       Deliver production model/system1
T-5       Support deployment

(4-8%)    Domain VII    Model Lifecycle Management

T-1       Document initial structure
T-2       Track model quality
T-3       Re-calibrate and maintain the model1
T-4       Support training activities
T-5       Evaluate the business benefit of the model over time

Successful performance of the tasks listed above requires specific knowledge, which is what will be tested.  At this time, the supporting knowledge statements are being used to develop items (questions) for the first exam and, as such, are not publicly available. 

 Tasks performed by analytics professionals beyond certification level


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Domains and Weights

Domain Description Weight
I Business Problem (Question) Framing 15%
II Analytics Problem Framing 17%
III Data 22%
IV Methodology (Approach) Selection 15%
V Model Building 16%
VI Deployment 9%
VII Life Cycle Management 6%

INFORMS Analytics Certification Job Task Analysis Working Group

Jeff Camm (Univ. of Cincinnati)
Arnie Greenland (IBM Global Bus. Serv.)
Bill Klimack (Chevron) * #
Jack Levis (UPS) * #
Daymond Ling (Canadian Imperial Bank of Commerce)N
Freeman Marvin (Innovative Decisions Inc.)
Scott Nestler (U.S. Army) #
Jerry Oglesby (SAS)
Michael Rappa (NC State / Inst. Adv. Analytics) #
Tim Rey (Dow Chemical)
Rita Salam (Gartner)N
Sam Savage (Stanford / Vector Economics)

* -INFORMS Board Member
# - Certification Task Force Member
N – Non-Member of INFORMS