Tag Archives: teams

Data Science and the Perfect Team

Today, I am proud to welcome a guest post by Claire Gilbert, Data Analyst at Gongos. For more on Gongos, see the description at the end of the post.

It’s fair to say that for those who run in business intelligence circles, many admire the work of Fast Forward Labs CEO and Founder Hilary Mason. Perhaps what resonates most with her fans is the moniker she places on data scientists as being ‘awesome nerds’—those who embody the perfect skillsets of math and stats, coding, and communication. She asserts that these individuals have the technical expertise to not only conduct the really, really complex work—but also have the ability to explain the impact of that work to a non-technical audience.

As insights and analytics organizations strive to assemble their own group of ‘awesome nerds,’ there are two ways to consider Hilary’s depiction. Most organizations struggle by taking the first route—searching for those very expensive, highly rare unicorns—individuals that independently sit at this critical intersection of genius. Besides the fact that it would be even more expensive to clone these data scientists, there is simply not enough bandwidth in their day to fulfill on their awesomeness 24/7.

To quote Aristotle, one of the earliest scientists of our time, “the whole is greater than the sum of its parts,” which brings us to the notion of the team. Rather than seeking out those highly sought-after individuals with skills in all three camps, consider creating a collective of individuals with skills from each camp. After all, no one person can solve for the depth and breadth of an organization’s growing data science needs. It takes a specialist such as a mathematician to dive deep; as well as a multidisciplinary mind who can comprehend the breadth, to truly achieve the perfect team.

awesome-nerds-img
Awesome Nerds

Team Dynamics of the Data Kind

The ultimate charge for any data science team is to be a problem-solving machine—one that constantly churns in an ever-changing climate. Faced with an increasing abundance of data, which in turn gives rise to once-unanswerable business questions, has led clients to expect new levels of complexity in insights. This chain reaction brings with it a unique set of challenges not previously met by a prescribed methodology. As the sets of inputs become more diverse, so too should the skillsets to answer them. While all three characteristics of the ‘awesome nerd’ are indispensable, it’s the collective of ‘nerds’ that will become the driving force in today’s data world.
True to the construct, no two pieces should operate independent of the third. Furthermore, finding and honing balance within a data science team will result in the highest degree of accuracy and relevancy possible.
Let’s look at the makeup of a perfectly balanced team:

  • Mathematician/Statistician:
    This trained academic builds advanced models based on inputs, while understanding the theory and requirements for the results to be leveraged correctly.
  • Coder/Programmer:
    This hands-on ‘architect’ is in charge of cleaning, managing and reshaping data, as well as building simulators or other highly technical tools that result in user-friendly data.
  • Communicator/Content Expert:
    This business ‘translator’ applies an organizational lens to bring previous knowledge to the table in order to connect technical skill sets to client needs.

It’s the interdependence of these skillsets that completes the team and its ability to deliver fully on the promise of data:
A Mathematician/Statistician’s work relies heavily on the Coder/Programmer’s skills. The notion of garbage-in/garbage-out very much applies here. If the Coder hasn’t sourced and managed the data judiciously, the Mathematician cannot build usable models. Both then rely on the knowledge of the Communicator/Content Expert. Even if the data is perfect, and the results statistically correct, the output cannot be activated against unless it is directly relevant to the business challenge. Furthermore, teams out of balance will be faced with hurdles for which they are not adequately prepared, and output that is not adequately delivered.

To Buy or to Build?

In today’s world of high velocity and high volume of data, companies are faced with a choice. Traditional programmers like those who have coded surveys and collected data are currently integrated in the work streams of most insights organizations. However, many of them are not classically trained in math and/or statistics. Likewise, existing quantitative-minded, client-facing talents can be leveraged in the rebuilding of a team. Training either of these existing individuals who have a bent in math and/or stats is possible, yet is a time-intensive process that calls for patience. If organizations value and believe in their existing talent and choose to go this route, it will then point to the gaps that need to be filled—or bought—to build the ‘perfect’ team.
Organizations have long known the value of data, but no matter how large and detailed it gets, without the human dimension, it will fail to live up to its $30 billion valuation by 2019. The interpretation, distillation and curation of all kinds of data by a team in equilibrium will propel this growth and underscore the importance of data science.
Many people think Hilary’s notion of “awesome nerds” applies only to individuals. But in practice, we must realize this kind of market potential, the team must embody the constitution of awesomeness.
As organizations assemble and recruit teams, perhaps their mission statement quite simply should be…
If you can find the nerds, keep them, but in the absence of an office full of unicorns, create one.

About Gongos

Gongos, Inc. is a decision intelligence company that partners with Global 1000 corporations to help build the capability and competency in making great consumer-minded decisions. Gongos brings a consultative approach in developing growth strategies propelled by its clients’ insights, analytics, strategy and innovation groups.

Enlisting the multidisciplinary talents of researchers, data scientists and curators, the company fuels a culture of learning both internally and within its clients’ organizations. Gongos also works with clients to develop strategic frameworks to navigate the change required for executional excellence. It serves organizations in the consumer products, financial services, healthcare, lifestyle, retail, and automotive spaces.

How To Build Data Science Teams?

Companies everywhere are struggling to assemble data science teams. Here are a couple of videos to help answer the following questions and more.

  • How do you assemble a team?
  • What skills do you need?
  • Where do you look for data scientists?
    •  

      DJ Patil, one of the stars of the data science world, answers a bunch of great questions in this talk. It is a couple years old, but still relevant.

      What are the Characteristics to look for in a Data Scientist?

      • Curiosity
      • Passion for playing with data
      • History of having to manipulate data to solve problems

       

      What are the Key Data Science Skills?

      • Finding Data Sources
      • Working with large data sets despite constraints
      • Cleaning data
      • Merging data sets
      • Visualization
      • Building tools for others to use

       

      Where to look for data science team members?

      • Internal
      • Interns
      • Other fields (physics, neurology, sciences)
      • Academic counterparts

       

      Principles for Data Science Talent

      • Would we be willing to work on a startup together?
      • Can you knock the socks off in 90 days?
      • Will you be doing amazing things?

       

      David Dietrich of EMC just recently added some insight to DJ’s points about building data science teams. His philosophy is: Building data science teams is not the goal. Developing data science capabilities is the goal. The structure is not nearly as important as the work being done. Different organizations can be successful doing data science different ways. In the video he lays out the pros and cons of all the following strategies.

      Strategies to Assemble Data Science Capabilities

      1. Transforming – reposition/add/modify existing teams such as a reporting team
      2. Creating – just start from scratch
      3. As a Service – consultants or websites, new ones are appearing every day
      4. Crowdsourcing – competitions like the Netflix prize or Kaggle

       

      Now, go start developing data science capabilities!