Building Data Teams
In the startup world, “unicorn” is a term used to describe a startup that grows quickly to achieve a one billion dollar or greater valuation. In data science, the term means something else. You’ll often hear hiring managers bemoan the fact that they cannot find a data specialist who can do everything: mine data, write complicated data processing procedures, be thoughtful about how to augment structured data with unstructured sources, be mindful of system requirements, be diligent about quality and testing, generate reports and data visualization, and of course communicate all that in business language to senior management.
At TCB Analytics, we’ve built large data teams from scratch in different sectors to take advantage of diverse business opportunities in market research, e-commerce, social media, and telecom. We also have experience teaching data science to post-graduate professionals. We know that unicorns don’t exist, and the idea that one professional possesses all those skills is impractical.
Through our hiring and team development experience we’ve realized the value in breaking down the definition of a unicorn into its composite parts to find specialists that are skilled in different layers of the data stack.
When creating a data team, you’re looking to:
- Hire data architect experts that are good at extracting data, setting up cloud-based storage, and monitoring that infrastructure for scale and cost (résumés usually taut ETL, AWS, database design, NoSQL experience.) These are the people that design the foundational layer of your data products to ensure your customers will benefit from high availability and that the solutions will scale efficiently.
- Hire data engineers who are skilled at moving data from your storage layer into aggregates and reference tables. They know enough about data architecture to communicate backend requirements, but their main job and focus is in making sure the data is prepared for analysis. They should be able to find complementary data sources, test data integrity, and organize data for your analysts (résumés for this team include some ETL, scripting languages such as Python and/or Perl, and they should be masterful at manipulating large data files from a command line.)
- Hire data analysts who are curious and inherently love to solve problems. They should be proficient in tools like Tableau and R so they can cut data, generate reports quickly, and make tools available over the web in dashboards. Admittedly, finding analysts who are curious and dig into the data to ask questions is hard. We’ve found that it’s helpful to stimulate curiosity and friendly competition within this group through peer review. Make sure to set up and monitor collaboration tools like Slack for analysts so they can share work and ideas. As a manager, you can see how they respond to each other’s work. The way your team collaborates and coaches each other will give you a strong sense of the skills on your team and help you identify the personalities who are fit to present reports to management.
- If you need them, you may have a few data scientists on staff that write predictive algorithms and advanced statistical models to extrapolate insights from data or anticipate future business trends. In our experience, these are very expensive and underutilized resources. Unless your core business is in selling predictions from data or you are consistently using analytics to make business decisions internally, it’s more cost-effective to have a team of consultants that are familiar with your business on call, than to over-staff in this area.
The final piece of advice is look for team members who are specialists for the roles above, but know enough about the other roles on the team to be mindful of the challenges and opportunities that exist across the data business. That mutual respect is vital to having a world-class data team that self-teaches, challenges one other, and scales efficiently.
We know none of this advice solves how to hire for these highly sought after roles, but we will discuss how we test, review, and hire the best data people possible in a follow-up blog post.