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Guide To Building Data Science Teams


Data Scientists are no magicians, but they are in high demand. Researchers and analysts in this space recognize the diversity and explosion of Big Data, but the only way enterprises are going to be able to prepare for the future of Big Data is with a Data Science team capable of working with dirty data, complex problems, and open-source languages, experts in the field say. Putting together an entire team has the potential to be more difficult. The following information should help to make the process easier.

What roles need to be filled for a Data Science team? You will need to have Data Scientists who can work on large datasets and who understand the theory behind the science. They should also be capable of developing predictive models. Data engineers and data software developers are important, too. They need to understand architecture, infrastructure, and distributed programming. But when it comes to the processes, the key thing to remember with Data Science is agility. The team needs the ability to access and watch data in real time. It is important to do more than just measure the data. The team needs to take the data and understand how it can affect different areas of the company and help those areas implement positive changes. They should not be handcuffed to a slow and tedious process, as this will limit effectiveness. Ideally, the team will have a good working relationship with heads of other departments, so they work together in agile multi-disciplinary teams to make the best use of the data gathered.


Data science team structures
Embarking on Data Science and Predictive Analytics requires a clear understanding of how the initiative is going to be introduced, maintained, and further scaled in terms of team structure.

Team leader
The team leader must have chops when it comes to data science. Leadership and business skills alone are not enough.

Data strategist
We have discussed this role already as the bridging point between the Data Science team and the business. This role may work with campaign experts from the marketing team. Data strategists may be similar to product managers, and may need to work with front-end developers and UX professionals as part of a wider data product team. There may also be data analysts involved, much like on a more descriptive analytics team, who do data processing and may also visualise data.

Data Scientist
Data Scientist should have expertise in both statistics and software development. They will likely be able to use Hadoop or Spark to analyse large datasets and they will be familiar with R or Python.

Data engineers and architects
These roles are about understanding how data is structured in the organisation. That means databases, cloud computing, distributed frameworks like Hadoop and some programming languages expertise. Data architects capture, organise and centralise data. Engineers then test, maintain and get the data ready for analysis.


Build an engaging environment.

Much of what motivates high-performing Data Scientists and quants, and what shapes a work environment for them, comes down to the outlook of their colleagues and the dynamics of their daily interactions. High-performing team members need to be comfortable giving colleagues their best ideas and collaborating to shape the most promising of these inchoate thoughts into real-world solutions, while willingly abandoning those that turn out to be less well-founded. The culture of world-class analytics teams is one in which team members (and their managers) are excited by what their teammates can do.


Make sure the team has access to the resources it needs

All business units need resources, and generally there are not enough to go around. For analytics teams, resources include the obvious hardware, software, and support staff. But analytics teams also need access to things like time with senior people or academics in the larger organization and beyond or to high-performance computing platforms or specialized data sets for modelling projects.

Written by

Amir Arres has been the Editor in Chief of Dataism since November 2015. He directs its strategy and development. He has a background in Data Analysis and a BA in Business Decision Making. Amir is interested in how new thinking from Big Data challenges conventional ways of understanding knowledge and culture. His vision for Dataism is to create a sanctuary online for bold and nuanced ideas.