Who are today’s Analytics professionals?

How do we frame the skills and context of all the Analytics professionals out there?
How do we articulate the value one brings to multi-disciplinary conference calls?
How do we orchestrate the strengths & challenges of team members in order to ultimately deliver to our clients?

In order to deliver on-target, on-time and on-budget Analytics projects (small or large), the answers to the above questions are essential. This blog identifies the 9 fields of study and the corresponding professionals that contribute significantly to Analytics projects, their key value propositions & challenges, and high-level suggestions to categorize and organize the rapidly growing Analytics resource pool.

an·a·lyt·ics: the systematic computational analysis of data or statistics
Wikipedia defines Analytics as “the discovery and communication of meaningful patterns in data”. It highlights the multi-disciplinary nature of this field, for example, the “simultaneous application of statistics, computer programming and operations research to quantify performance.”

MIT Center for Digital Business fellow, Dr. Thomas Davenport, thinks that “the field of business analytics was born in the mid-1950s” and we are currently in the era of Analytics 3.0. A brief History of Data Science and evolution of Big Data Technologies created by Mamatha Upadhyaya is a good read for this backdrop. Whether one is new to Analytics or grew up in one of the disciplines, it is important to understand all of the 9 fields of study, how they overlap, and how they are dependent on one another, in order to solve complex business problems.

(1) Business Analysis (BA)

IBM’s Jean Francois Puget (an Optimization mathematical guru) thinks that, “the value of analytics comes from the business decisions it enables,” and/or “automates”. Since we should always start with the business questions first – not the data – veteran Business Analysts and Domain Experts must be engaged from the beginning to end of a project in order to discover, develop, champion and capture the value that Analytics promises. Depending on the business problem at-hand, Business Analysts might be Domain SMEs (i.e. Marketing, Operation, Supply Chain, Workforce) or Industry SMEs (i.e. Retail, Energy, Banking) or both (i.e. Supply Chain Expert in Canadian Liquid Pipelines).
Key Value Propositions: Context, Business Value Maps, KPIs, Key Pain Points, Process & IT Re-Engineering, Model Requirements, Testing & Calibration, etc.
Challenges: The struggle in documenting tribal knowledge and translating business requirements into mathematical formulas, agreeing on trade-offs among competing business priorities, calibrating analytical models output, and sometimes maintains an all or nothing attitude, etc.

(2) Finance

When P&G decided to collect data from their manufacturing facilities in the 1990s to understand how products and machines could be optimized and to “create operational efficiencies that would contribute to healthy EBITDA margins,” business value quantification was at the top of their agenda. Strategic and financial skills (typically demonstrated by MBAs / CFAs / CAs / Financial professionals) are very important to create a Business Case at the front end, to conduct periodical Project Progress check points and to ensure Executive alignment and complete Business Value Quantification at the back end of an Analytics initiative.
Key Value Propositions: Business Case, Project Funding Approval & Compliance, Value Based Go/No-Go Decisions or Changes in Direction, Benefit Quantification, etc.
Challenges: Not familiar with Optimization, Simulation or Predictive Model Development, Testing or Implementation of complex IT projects, etc.

(3) Operations Research (OR)

Operations Research overlaps with other disciplines, notably Industrial Engineering and Operations Management, according to The Institute for Operations Research and the Management Sciences (INFORMS). The essential contribution of this profession is the construction of mathematical models that attempt to describe the system, which is often concerned with determining a maximum (such as profit, performance, or yield) or minimum (such as loss, risk, or cost) of business objectives. INFORMS is the largest society in the world for professionals in the field of operations research, management science, and analytics. INFORMS governs the Certified Analytics Professional (CAP) designations and hosts a yearly conference on Business Analytics and Operations Research.
Key Value Propositions: Problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queuing theory, Markov decision processes, neural networks, decision analysis, etc.
Challenges: Often does not get involved with Politics, Organization Change Management, Project Timelines & Budgets, and Core IT (UI, database, integration, infrastructure) related issues, etc.

(4) Statistics

The American Heritage Dictionary defines Statistics as “The mathematics of the collection, organization, and interpretation of numerical data, especially the analysis of population characteristics by inference from sampling.” Statisticians apply statistical thinking and methods to a wide variety of scientific, social, and business endeavors such as astronomy, biology, education, economics, engineering, genetics, marketing, medicine, psychology, public health, sports, among many others. The American Statistical Association (ASA) is the world’s largest community of statisticians.
Key Value Propositions: Descriptive statistics (i.e. mean, standard deviation, frequency, percentage) and Inferential statistics (i.e. answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (i.e. regression analysis). Inference can extend to forecasting, prediction, estimation of unobserved values, as well as the extrapolation and interpolation of time series or spatial data, and can also include data mining.
Challenges: Similar to the OR professionals.

(5) Data Science

The Harvard Business Review article, Data Scientist: The Sexiest Job of the 21st Century, written by Dr. Thomas Davenport is a good read. Data Scientists advise executives and product managers on the implications of the data for products, processes, and decisions by enabling the shift from “ad-hoc analysis” to “on-going conversation with data”. This is a hybrid practice of research and execution, and application of the ‘start with data first’ philosophy. “As they make discoveries, they communicate what they have learned and suggest its implications for new business directions” – whether for new revenue models, cost control or regulatory compliance.
Key Value Propositions: As the industry is still defining the profession, Data Scientists are creating new deliverables and tools every day. For example, “Yahoo, one of the firms that employed a group of data scientists early on, was instrumental in developing Hadoop. Facebook’s data team created the language Hive for programming Hadoop projects.”
Challenges: Similar to the OR professionals, but more Core IT savvy.

(6) Data Visualization

Visualizing information – facts, data, ideas, subjects, issues, statistics, questions – all while using words minimally is a rapidly growing and essential discipline in its own right. Data Journalists and Information Designers like David McCandless, author of ‘Information is Beautiful’ refers to visualization information as combining “language of the mind” with “language of the eyes”. His ‘dataviz process’ includes (i) conception and the generation of good, interesting ideas, (ii) researching and curating juicy data, (iii) executing and selecting appropriate & effective visualizations, (iv) designing and beautifying impactful charts & diagrams.
Key Value Propositions: Creativity in displaying information visually and making the patterns they find clear and compelling (i.e. Information Design, Hypothesis, Charts, Diagrams, etc.)
Challenges: Similar to the OR professionals, but more Core IT savvy.

(7) Computer Science (Information Management)

A computer scientist specializes in the theory of computation and the design of computational systems. According to Wikipedia, computer science “is the systematic study of the feasibility, structure, expression, and mechanization of the methodical procedures that underlie the acquisition, representation, processing, storage, communication of, and access to information.” Under the Information Management umbrella lies many sub-disciplines, like Data Acquisition, Streaming Computing, Data Integration, Transformation, Warehouse / Data Marts, Enterprise Content Management, Business Intelligence & Performance Management, etc.
Key Value Propositions: Architecture, Integration, Infrastructure, UIs, Database Administrations, Data Governance, Data Models, Testing, etc.
Challenges: Similar to the OR professionals and are often not familiar with Analytics model development (non-deterministic aspect) life cycle, etc.

(8) Organization Change Management (OCM)

Seth Godin in his Analytics Without Action blog mentions two key messages: “(i) Don’t measure anything unless the data helps you make a better decision or change your actions. (ii) If you’re not prepared to change your diet or your workouts, don’t get on the scale.” This is aligned with the 3,000+ global business executives study findings by MIT Sloan Management Review that “the analytics adoption barriers that organizations face most are managerial and cultural rather than related to data and technology.” Analytics savvy OCM practitioners are best equipped to address these challenges by working collaboratively with the enterprise senior change agents.
Key Value Propositions: Guiding an organization through the Analytics maturity curve. Booz Allen defines them as Awareness, Understanding, Acceptance, Adoption and Ownership in their recent point of view.
Challenges: Often not familiar with OR or Statistical methods of problem solving and Analytics model development (non-deterministic aspect) life cycle, etc.

(9) IT Project Management (PM)

IT Project Managers are the unsung heroes of any large transformation program, often taking most of the blame, and none of the glory. However, it is very important to understand the uniqueness of managing Analytics projects from traditional IT projects. Professor Stijn Viaene (from Katholieke Universiteit Leuven, Belgium) categorizes the most important qualities of Analytics PMs into five areas: (1) having a delivery orientation and a bias toward execution, (2) seeing value in use and value of learning, (3) working to gain commitment, (4) relying on intelligent experimentation and (5) promoting smart use of information technology.
Key Value Propositions: Statement of Work & Contracts, Project Schedule, Financial & Resource Plan, Progress Reports, Risks & Action Log, etc.
Challenges: Similar to the OCM professionals.

So, how should we categorize resources and structure high performing Analytics delivery teams?

This requires a lengthy discussion, and perhaps a dedicated blog. Nonetheless, here are some high level suggestions:

Based on education and relevant experiences, each professional should pick a Major and Minor from the 9 disciplines defined above. They should have significant depth in the Major, and reasonable breadth in the Minor.
It is certainly possible for one to have multiple majors & minors. For example, as an Advanced Analytics Delivery Leader, I would categorize myself as:
- Major: Business Analysis (Supply Chain & HR) and Operations Research
- Minor: Finance, Project Management and Information Management

Although we are in the era of Analytics 3.0, this is still an early stage for mass adoption. We do not need to have a “winner takes all” attitude, but rather stay true to our Majors & Minors, while continuing to learn in this very dynamic Analytics profession.

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