In the last 3 years, spend on data analytics services has increased 51.36%, with an expected growth of 13.2 billion dollars in 2019. Now more than ever, it’s crucial to ensure you have the necessary experts on your analytics team.

Let’s face it: we’ve seen a myriad of analytics job titles popping up in the last few years, including:

  • Data Scientist
  • Business Intelligence Engineer
  • Data Visualization Developer
  • Digital Analyst

With so many new and specialized roles in the analytics landscape, organizations are left confused about whether they have the right resources to be successful. However, when you focus on core responsibilities, there are three defined roles you must fill to build a strong digital analytics team.

Architect

The architect role is filled by an expert technology user. She is responsible for data mining, data cleansing, and executing technical implementation projects. Additionally, this team member will often handle all data warehouse solutions, from design to maintenance.

When hiring an architect, you should look for an individual who has experience with relational databases and querying data with languages such as R, Python, or SQL. She should also be comfortable troubleshooting tracking issues and identifying inconsistencies in data collection and storing. As far as education requirements, an architect role is often filled by an individual with a bachelor’s degree or higher in computer science, information science or a similar field.

Analyst

The data analyst role on your team is reserved for an individual with the statistical knowledge and capabilities to use tools and technology for deciphering data. He is responsible for identifying trends and assessing performance. He is also the team member who most often produces regular and ad-hoc reporting.

An analyst often has experience with web analytics tools, such as Google Analytics, as well as statistical packages including SPSS or SAS. He should also have experience working with dashboards in programs such as Tableau or Sisense. The preferred education for a data analyst includes the fields of business, applied mathematics or computer science.

Interpreter

An interpreter brings business understanding to the data provided by the architect and analyst. By taking into consideration her business acumen, she can generate deep insights and tell the story behind the data. An interpreter is also responsible for asking the right questions to make recommendations for immediate next steps and long-term business planning.

Whereas the architect and analyst roles rely on a strong technical or mathematical background, the interpreter often is more strategic and has a deep understanding of the business. Educational background in this position is often someone with a degree in the field of business or marketing.

Unicorns Don’t Exist

Whether you are building your analytics team from the ground up or expanding an existing team, it’s important to remember one thing: hiring an applicant who is an architect, analyst and interpreter is about as likely as spotting a unicorn on your commute to work.

Due to the deep knowledge of specific concepts and skills, it’s particularly challenging for any individual to master more than one of these roles. Instead of attempting to hire a 3-in-1 employee, focus on strong and passionate team members who will follow and grow along with changes in the industry.

Just remember, assembling your digital analytics team should be a strategic, long term investment for your organization to grow a sustainable analytics presence.

Is an in-house data analytics team oversized for your organization? Let Liquid’s team function as yours – let’s talk!

Courtney Fenstermaker

About Courtney Fenstermaker

Courtney Fenstermaker is a Data Analyst at Liquid, specializing in architecting custom data analysis strategies and implementation plans – transforming data into actionable insights for Liquid’s clients. She graduated from Moravian College in 2016, and made her mark at Liquid through the internship program before joining the team full-time.