How to Assemble a Highly Effective Data Analytics Team

All too often these days, business success hinges on data analytics, and data analytics depends on a well-rounded, cross-functional team of data analysts and data scientists. How can you ensure that these teams are fine-tuned to deliver effective, actionable data insights? Fortunately, there are some keys to success. 

Without a top-notch team of analytics experts, even the most expensive and powerful data tools won’t help your organization mine valuable insights from mountains of data. As John Kutay, director of growth at Striim, put it, your analytics are only as good as your analytics team. 

“There are plenty of examples of organizations using the information served by data analytics teams to build their revenue, reduce expenses, and have a single source of truth when optimizing your business applications,” Kutay said. “This applies to every vertical in today’s digital world, from media and entertainment to consumer packaged goods (CPGs) and retail to financial services.”

But keep in mind that your analytics team is only as good as the quality and comprehensiveness of your data; the more data sources aggregated by your analytics team, the more intelligent the resulting business decisions. 

Right-Sizing Your Data Team 

Chris Morales, chief information security officer at Netenrich, a digital IT and security operations company, points out the size and scope of the data analytics team is based on organization data maturity, scale of the data used, the problem(s) to be solved, and strategic objectives. “In simpler terms, if you have a small dataset driving a business unit-level tactical outcome, a small team of 1-2 people would probably do,” he explained. “It is when you start to scale and build a larger disparate dataset specialization and specific roles become defined.”

Among the key players for any such team: a data product lead to own the business intelligence requirements for their business domain; a data engineer to implement data pipelines to support reporting and analytics goals; a BI analyst to generate reports and KPIs; and optionally, a data scientist if the team is operationalizing machine learning in their analytical workflows.

Fundamentally, data science and analytics needs three functions: Someone to ensure data is collected, stored and usable (engineer); someone to analyze and interpret the data to extract meaningful value (scientist); and someone to consume the data and bridge the gap to the business to prescribe outcomes (analyst).

Kutay said data product managers should be excellent at understanding business requirements but also technical enough to translate requirements into technical projects for data engineering, data science and business intelligence teams. “However, with that said, everyone on the team should have experience with data pipelines and SQL,” he added. “That includes ETL/ELT, data streaming, data warehousing, and reporting tools. Team leaders should have a good sense of data governance, ownership, security, and access control.”

If the team is analyzing sensitive customer data, they should also be in touch with their legal and security teams over compliance. Ensuring compliance with laws such as CCPA, GDPR, and HIPAA (for healthcare) is critical. 

Finding the Talent

When it comes to sourcing talent, some team leads start by heading to forums where data practitioners gather to share information, including Stack Overflow and GitHub. Community thought leaders and other experts can quickly identify specialized data experts. 

Whether a team lead depends on HR and recruiters, or prefers looking outside the box to secure top talent, management needs to have a clear understanding and commitment to how data analytics fits within the organization. “Defining roles should start with a data roadmap and clear picture of what value needs to be extracted and how,” Morales said. “The organizational structure changes depending on company culture.”

Once all that is in place, organizations must map the requisite data, technology and skills needed to execute. “The last step is to then take those defined skills and look inside to see what resources are already available internally and then going external to fill gaps,” Morales said. 

Tips for Data Job Hunting

For those with the right skill sets, Kutay added, there are a couple tips to keep in mind while looking for the ideal job now that market forces are in their favor. “Data analysts and data engineers should go to companies that value data-driven operations,” he said. “Thus, their purpose at the company is always justified and their involvement will be considered critical.”

While a large company with a big brand name may seem attractive, you need to ensure that the company has fully adopted data analytics as a core component of their operations. Someone looking to have a long-term career in data analytics and data science should think through the aspects they most enjoy and excel at. “Being a generalist is useful but as the field scales, specialization will be a necessity to scale,” Morales pointed out. 

From the perspective of those hiring, Kutay said companies should evangelize how they are innovating with data-driven operations and customer experiences. “There’s no better way to attract top talent than sharing how your team is unique and changing the game with analytics,” he said. 

From Morales’ viewpoint, organizations need to be clear on what functions the job will require, and not ask for generic or hard-to-fill skills. “What kind of analytics are being performed? What is the structure to support it? All employees are looking to succeed,” he said. “Not having the support and clear roadmap to allow that is a message the organization does not take analytics seriously.”