How to Build the Perfect Data Science Team

Although the fields of statistics, data analysis, and computer programming have been around for decades, the use of the term “data science” to describe the intersection of these disciplines has only become popular within the last few years.

The rise of this new specialty — which the Data Science Association defines as “the scientific study of the creation, validation and transformation of data to create meaning” — has been accompanied by a number of heated debates, including discussions about its role in business, the validity of specific tools and techniques, and whether or not it should even be considered a science. For those convinced of its significance, however, the most important deliberations revolve around finding people with the right skills to do the job.

On one side of this debate there are those purists who insist that data scientists are nothing more than statisticians with fancy new job titles. These folks are concerned that people without proper statistics training are trying to horn in on a rather lucrative gig without getting the necessary training. Their solution is to simply ignore the data science buzzword and hire a proper statistician.

At the other end of the spectrum are people who are convinced that making sense out of large data sets requires more than just number-crunching skills, it also requires the ability to manipulate the data and communicate insights to others. This view is perhaps best represented by Drew Conway’s data science venn diagram and Mike Driscoll’s blog post on the three “sexy skills” of the data scientist. In Conway’s case, the components are computer programming (hacking), math and statistics, and specific domain expertise. With Driscoll, the key areas are statistics, data transformation — what he calls “data munging” — and data visualization.

The main problem with this multi-pronged approach is that finding a single individual with all of the right skills is nearly impossible. One solution to this dilemma is to create teams of two or three people that can collectively cover all of the necessary areas of expertise. However, this only leads to the next question, which is: What roles provide the best coverage?

In order to address this question, I decided to start with a more detailed definition of the process of finding meaning in data. In his PhD dissertation and later publication, Visualizing Data, Ben Fry broke down the process of understanding data into seven basic steps:

  1. Acquire – Find or obtain the data.
  2. Parse – Provide some structure or meaning to the data (e.g. ordering it into categories).
  3. Filter – Remove extraneous data and focus on key data elements.
  4. Mine – Use statistical methods or data mining techniques to find patterns or place the data in a mathematical context.
  5. Represent – Decide how to display the data effectively.
  6. Refine – Make the basic data representations clearer and more visually engaging.
  7. Interact – Add methods for manipulating the data so users can explore the results.

These steps can be roughly grouped into four broad areas: computer science (acquire and parse data); mathematics, statistics, and data mining (filter and mine); graphic design (represent and refine); and information visualization and human-computer interaction (interaction).

In order to translate these skills into jobs, I started by selecting a set of occupations from the Occupational Information Network (O*NET) that I thought were strong in at least one or two of the areas in Ben Fry’s outline. I then evaluated a subset of skills and abilities for each of these occupations using the O*NET Content Model, which allows you to compare different jobs based on their key attributes and characteristics. I mapped several O*NET skills to each of Fry’s seven steps (details below).

ONET Skills, Knowledge, and Abilities Associated with Ben Fry’s 7 Areas of Focus

Acquire (Computer Science)

  • Learning Strategies – Selecting and using training/instructional methods and procedures appropriate for the situation when learning or teaching new things.
  • Active Listening – Giving full attention to what other people are saying, taking time to understand the points being made, asking questions as appropriate, and not interrupting at inappropriate times.
  • Written Comprehension – The ability to read and understand information and ideas presented in writing.
  • Systems Evaluation – Identifying measures or indicators of system performance and the actions needed to improve or correct performance, relative to the goals of the system.
  • Selective Attention – The ability to concentrate on a task over a period of time without being distracted.
  • Memorization – The ability to remember information such as words, numbers, pictures, and procedures.
  • Oral Comprehension – The ability to listen to and understand information and ideas presented through spoken words and sentences.
  • Technology Design – Generating or adapting equipment and technology to serve user needs.

Parse (Computer Science)

  • Reading Comprehension – Understanding written sentences and paragraphs in work related documents.
  • Category Flexibility – The ability to generate or use different sets of rules for combining or grouping things in different ways.
  • Troubleshooting – Determining causes of operating errors and deciding what to do about it.
  • English Language – Knowledge of the structure and content of the English language including the meaning and spelling of words, rules of composition, and grammar.
  • Programming – Writing computer programs for various purposes.

Filter (Mathematics, Statistics, and Data Mining)

  • Flexibility of Closure – The ability to identify or detect a known pattern (a figure, object, word, or sound) that is hidden in other distracting material.
  • Judgment and Decision Making – Considering the relative costs and benefits of potential actions to choose the most appropriate one.
  • Critical Thinking – Using logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions or approaches to problems.
  • Active Learning – Understanding the implications of new information for both current and future problem-solving and decision-making.
  • Problem Sensitivity – The ability to tell when something is wrong or is likely to go wrong. It does not involve solving the problem, only recognizing there is a problem.
  • Deductive Reasoning – The ability to apply general rules to specific problems to produce answers that make sense.
  • Perceptual Speed – The ability to quickly and accurately compare similarities and differences among sets of letters, numbers, objects, pictures, or patterns. The things to be compared may be presented at the same time or one after the other. This ability also includes comparing a presented object with a remembered object.

Mine (Mathematics, Statistics, and Data Mining)

  • Mathematical Reasoning – The ability to choose the right mathematical methods or formulas to solve a problem.
  • Complex Problem Solving – Identifying complex problems and reviewing related information to develop and evaluate options and implement solutions.
  • Mathematics – Using mathematics to solve problems.
  • Inductive Reasoning – The ability to combine pieces of information to form general rules or conclusions (includes finding a relationship among seemingly unrelated events).
  • Science – Using scientific rules and methods to solve problems.
  • Mathematics – Knowledge of arithmetic, algebra, geometry, calculus, statistics, and their applications.

Represent (Graphic Design)

  • Design – Knowledge of design techniques, tools, and principles involved in production of precision technical plans, blueprints, drawings, and models.
  • Visualization – The ability to imagine how something will look after it is moved around or when its parts are moved or rearranged.
  • Visual Color Discrimination – The ability to match or detect differences between colors, including shades of color and brightness.
  • Speed of Closure – The ability to quickly make sense of, combine, and organize information into meaningful patterns.

Refine (Graphic Design)

  • Fluency of Ideas – The ability to come up with a number of ideas about a topic (the number of ideas is important, not their quality, correctness, or creativity).
  • Information Ordering – The ability to arrange things or actions in a certain order or pattern according to a specific rule or set of rules (e.g., patterns of numbers, letters, words, pictures, mathematical operations).
  • Communications and Media – Knowledge of media production, communication, and dissemination techniques and methods. This includes alternative ways to inform and entertain via written, oral, and visual media.
  • Originality – The ability to come up with unusual or clever ideas about a given topic or situation, or to develop creative ways to solve a problem.

Interact (Information Visualization and Human-Computer Interaction)

  • Engineering and Technology – Knowledge of the practical application of engineering science and technology. This includes applying principles, techniques, procedures, and equipment to the design and production of various goods and services.
  • Education and Training – Knowledge of principles and methods for curriculum and training design, teaching and instruction for individuals and groups, and the measurement of training effects.
  • Operations Analysis – Analyzing needs and product requirements to create a design.
  • Psychology – Knowledge of human behavior and performance; individual differences in ability, personality, and interests; learning and motivation; psychological research methods; and the assessment and treatment of behavioral and affective disorders.

Using occupational scores for these individual ONET skills and abilities, I was able to assign a weighted value to each of Ben Fry’s categories for several sample occupations. Visualizing these skills in a radar graph shows how different jobs (identified using standard SOC or ONET codes) place different emphasis on the various skills. The three jobs below have strengths that could be cultivated and combined to meet the needs of a data science team.

Another example includes occupations that fall outside of the usual sources of data science talent. You can see how — taken together — these non-traditional jobs can combine to address each of Fry’s steps.

According to a recent study by McKinsey, the U.S. “faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions” based on data. Instead of fighting over these scarce resources, companies would do well to think outside of the box and build their data science teams from unique individuals in other fields. While such teams may require additional training, they bring a set of skills to the table that can boost creativity and spark innovative thinking — just the sort of edge companies need when trying to pull meaning from their data.

Updates:

May 2, 2014 – The folks over at DarkHorse Analytics put together a list of the “five faces” of analytics. Great article.

  1. Data Steward – Manages the data and uses tools like SQL Server, MySQL, Oracle, and maybe some more rarified tools.
  2. Analytic Explorer – Explores the data using math, statistics, and modeling.
  3. Information Artist – Organizes and presents data in order to sell the results of data exploration to decision-makers.
  4. Automator – Puts the work of the Explorer and Visualizer into production.
  5. The Champion – Helps put all of the pieces in place to support an analytics environment.

D3 Notes:

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