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March 25, 2020
Data Intelligence

Five reasons why your data science team is underperforming

Data science requires your company to adopt a policy of intentionality.   This is because it can be difficult to see the work product of data  science and to understand the value it is providing.  The intent of this  article is to challenge you regarding your knowledge of your company’s  relationship with data science through provocative statements and  discussion.

1. Data science does not fit into the narrative of your company.

In order to be most effective, your company has to be a data science native.  The narrative of what your company does, and how it solves problems must have tenets of data science at its core.  In order to achieve this, everyone in the company needs to understand and agree with the value of data science as a means of achieving the company mission and goals.  Effective utilization of data science in your company is a proactive strategy for propelling the company forward, rather than a reactive strategy for catching up with the competition without a proper strategic heading.

2. Your company lacks executive support for data science.

Your executive team needs to provide authentic support for data science in order for it to flourish.  This means that your executive team must deeply understand and embrace the value proposition of data science as relates to the narrative of your company.  However, deep understanding is not enough. Your leadership must also create space for data science within the organization. This means affecting the hearts and minds of everyone in the company to create a culture that is embracing of data science as a means to achieve the company mission and goals.  

This  also means making the right connections so that your data science team  has access to domain knowledge and the proper tools and resources needed  to accomplish well-defined objectives.  Data science is much more than  simply data scientists. It is also the local and cloud hardware, the  larger infrastructure that makes deployment possible, integration with  other software and teams, not to mention significant efforts spent  identifying, organizing and engineering existing and new data sources.  

3. Your company is not ready for a data science team.

In  order for data science to be successful, your entire company has to be  ready for your data science team.  This means that your data science  team needs to feel welcomed and valued as a meaningful part of the  larger team—all of whom are focused on a shared mission with shared  goals.

The existing teams need to understand the benefits of data  science in the context of efficiently pursuing the mission and goals,  and proactively reach out to help enculturate and integrate data science  team members.

Positive communication should be reached as soon as  possible to remove blockers from interdisciplinary work. It is of  paramount importance for a successful data science team that it is well  connected and has the authentic support it needs from every other part  of the company.

4. You don’t have the right blend of data scientists.

Data scientists are not all the same, but rather are as richly diverse as any other group.  Leveraging this diversity is important for building effective teams. For example, data scientists interests and aptitudes fall into research, engineering and reporting.  You must understand what your company needs and attempt to find data scientists who have ability and interest in the same areas. Mismatches set you up for significant challenges in getting solid results.  This can be especially problematic in companies that shift to data science by leveraging existing analysts, as the expectations of the new role are significantly different.

As with any field, data scientists are either more or less  experienced.  A team comprised entirely of inexperienced data scientists  is less capable of intuition about problems and of producing results  quickly.  On the other hand, a team comprised entirely of well-seasoned  data scientists may stagnate by not being aware of the newest practices  in the field.

Understanding the career lifecycle and how this relates  to the needs of your company is important in creating the right blend of  data scientists on your team.

5. Your data science team is not operating efficiently.

Complacency in the workplace can be devastating and results in a decrease in operational efficiency.  Data science teams can be susceptible to this for a number of reasons. In companies where the leadership and other teams are not knowledgeable about data science or are not embracing of data science, it can be easy for data scientists—lacking direction, vision and leadership—to fail to perform by hiding behind technological hype and exploitation of the larger company’s ignorance. 

Like every other team in your company, data science should be focused on executing against goals aligned with the company mission.  The work should be intentional and there should always be pragmatic candor between data science teams and other teams regarding solving company problems.  There is no place in an effective data science team for over-indexing on technological or role hype. There are no concepts in data science that are inaccessible to anyone outside of data science; your data scientists should be able to clearly explain what they are doing in plain terms. This isn’t just for everyone else’s benefit; it is important in technical fields to know the techniques well enough to describe them simply. There is also no place for discussions about one-liners as a means of solving problems. One-liners are a myth; there is always significant engineering required to take data science to production.

Your data science team needs to stay ahead of the curve on technology and technique.  This is extremely important because new ideas often pay significant dividends. However, you must be pragmatic regarding the value of educational opportunities.  For example, most conferences are about networking and recruiting; it is much simpler to read the author’s papers and directly communicate with them as a means of intentional learning.  It is important to keep your team focused on professional development, but you must be practical about where the value comes from.

To recap, the success of data science in your company depends on the proper attitude and efforts across the entire organization. Without an appropriate company narrative, the right executive and  company support, the right data science talent and the right focus on  the achievement of well-defined company goals, it is often the case that  the team will underperform.  If you want to see your data science team  flourish, you should spend some time considering these thoughts and what  changes you can affect in your company.

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Colin Shaw
Director of Machine Learning, Colin comes from an analytical background in computational math, physics, and programming. He was one of the first people to graduate Udacity's self-driving car program, and has code that runs on a Lincoln MKZ. At RevUnit, Colin helps identify solutions and implement them to help our customers work better.

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