How to Structure Your Data Analytics Team
Data Roles

How to Structure Your Data Analytics Team

Cole Lehman
Cole Lehman

Your data analytics team helps you understand your customers, products, and services better — if you structure your team effectively. There’s no single formula to build a data analytics team. Your company will have to hire a unique mix of data experts with a variety of skills. But there are common data team roles and responsibilities you can choose from that lead to more effective data-driven business decisions.

But before we move on we want to make you an offer you can't refuse. 🙂 If you need an extra brain to bounce ideas off of or talk strategy, we want to help. Our team at Shipyard has seen (almost) every data problem under the sun. We've helped build plenty of solutions from scratch for Fortune 1000 brands.

Now, we want to give back and help out the data community a bit more. No catch, no charge, if you need some help or just want an outside party to weigh in click this link to schedule time with one of our data experts.

Now here’s an overview of what a data analytics team looks like, who’s on it, what they’re responsible for, and what to consider when building one to fit your company.

What is a Data Analytics Team?

A data analytics team performs specialized tasks to make your business data usable for digital products, business units, departments, and decision makers. It can also be called a dataops team, analytics team, data science team, or a business intelligence team. There are several common roles on a data analytics team — ranging from data scientist to chief data officer.

Your team size and selected positions depend on your company. Some data analytics teams are centralized in their own department and some are decentralized, with data team members assigned to different business units or departments.

Analytics team roles generally fall into two categories. People who make the data usable, like data engineers, and the people who apply the data to solve business problems and make decisions, like data analysts or data scientists.

It’s important to build a team structure with the right balance of roles and skills. You don’t want to hire five data engineers when you really need two data engineers, one data scientist, one machine learning specialist, and one business analyst.

Without any data engineers, all you would have is lakes of raw data spread across unusable sources. With engineers and no data scientists you could have automated data workflows, data pipelines, and clean datasets but no one who knows how to apply the data to the business needs. Here’s an overview of the foundational data analytics team roles you’ll need no matter what size team you want to build.

Key Roles on an Analytics Team

A successful data analytics team is built with a foundation of data engineers, data scientists, data analysts, machine learning specialists, and may even include a chief data officer. The right combination of team members can combine to build a solid data infrastructure that connects your data stack end-to-end — improving your business decisions through operational analytics.

Once you understand these core roles you can start to choose the right analytics team members to get the best results for your business initiatives.

Data Engineer

Data engineers are responsible for moving data from source to source, building data workflows, data governance, and maintaining solutions like cloud data warehouses and data lakes. Data engineers also help build and maintain exact, transform, and load (ETL) processes. They’re the ones that automate data workflows to move datasets into a central source that data scientists and data analysts can use.

A data engineer might build a solution to combine your product information management solution with your digital experience tool data, marketing campaign results, and sales data. Data engineers need a strong background in mathematics and computer science with lots of practical experience in building data ecosystems.

They don’t need to know the same advanced statistical methods as your analysts or data scientists, but they need to be able to have conversations with them and build data management solutions that meet their needs.

They’ll work on data pipelines when analysts run into performance issues so that analysts and data scientists can focus on the data. Sometimes a data engineer can also be a data scientist (a full-stack engineer with strong analytical skills could perform both roles), but it’s usually a smart move to separate the roles and have a devoted data engineer.

Data Analyst

Data analysts are responsible for analyzing large volumes of data. They interpret data, study reports, and help identify trends that lead to strategic decision-making. This is a crucial function that provides value throughout almost every stage of an organization’s life cycle.

The primary goal of a data analyst is to make sense of data, which they do through conducting both structured and unstructured analysis. They may do some predictive modeling — making predictions about what is likely to happen in future scenarios based on historical data.

Data analysts also perform exploratory analysis and qualitative analysis. Qualitative analysis involves looking at data with an eye toward insights that might not be readily quantifiable or measurable. For example, a data analyst might examine event data and look for trends in specific purchase locations, transaction times, or individual behaviors.

Data analysts are the ones who usually discover new opportunities for marketing, product development, and customer experience.

How to Become a BI (Business Intelligence) Analyst, From a BI Analyst

Data Scientist

In a nutshell, a data scientist is a cross between a data engineer and a data analyst — they aren’t just someone who knows how to build, run, and monitor an algorithm (though that's certainly important).

They are experts in data science who understand both business and data technology. Data scientists advise their business stakeholders on how data can bring strategic value, and build out automation and models to help achieve that value.

Your data scientists are the ones who know which business questions the data needs to answer, and they work with data engineers to roadmap and build the right solutions. Data scientists can also spend a significant amount of time collecting, cleaning, and merging data. They analyze, process, and model data — from product information to IoT data — so it can be used company-wide.

Machine Learning (ML) Engineer

Machine learning engineers are in charge of developing algorithms, improving existing algorithms, and modifying those algorithms to perform better. They’re also experts at recognizing what information is important and how best to utilize it, which makes them valuable for decision-making processes.

ML specialists also build models that help make complex analysis feasible for a business. These models use machine learning and AI technologies to take over mundane tasks and provide businesses with faster insights. This role is extremely valuable because it allows businesses to process massive amounts of data quickly.

For example, if you're running a FinTech company whose main goal is to track every transaction made within your network in real-time, then you need people working in machine learning roles. The same goes for companies in IoT, augmented reality (AR), or other emergent technologies. Even if you’re in something as seemingly simple as e-commerce or enterprise software, ML specialists can help you understand your data faster and more clearly than other data roles.

Chief Data Officer (CDO)

A chief data officer is a C-level executive responsible for leading data strategy, implementation, and optimization. Their job involves using data insights to shape corporate strategies and drive business performance. Because they understand the overview of dataops from the raw data to machine learning, your CDO can help other executives learn the best ways to leverage the data analytics team.

A CDO must understand what makes data valuable, how it can help improve business processes, and how businesses can learn from it. Chief data officers come from many different backgrounds but often have experience in business intelligence (BI).

Most CDOs are familiar with predictive modeling, big data technologies, advanced analytics methods like neural networks, and machine learning. If you’re going to invest in a CDO, make sure they’re a great leader and know how to nurture a strong team.

Key Responsibilities of a Data Analytics Team

Data analytics teams have a number of key responsibilities — including research, data collection, building data pipelines, data analysis, data visualization, building dashboards, and reporting. Depending on your business org, your data team may also need to own the identification and presentation of strategic opportunities. Or, that could be left to the business stakeholders, product managers, and decision makers.

Data team responsibilities revolve around doing everything they can to improve business performance based on data they collect. This includes taking advantage of predictive capabilities, analyzing historical trends, and making future projections with regards to how markets will change (or how customers will behave).

Here’s a short list of key responsibilities for your data analytics team:

  • Build and automate data workflows
  • Eliminate data silos
  • Deliver accurate data, on time
  • Building reports, dashboards for metrics, and advanced analytics capabilities
  • Manage data solutions and technology decisions

Things To Consider When Building a Data Analytics Team

Every member of your data analytics team is going to have a mix of technical and business skills that you’re going to have to balance. Everyone can’t be focused on math or machine learning, and everyone can’t be an expert in omnichannel customer experiences. With a blend of these skill sets, your team will help your company turn big data into a strategic asset.

  • Mathematics - This covers everything from statistics to building algorithms. You’ll need people who can perform advanced data analysis and create accurate metrics.
  • Data visualization - Make sure you have at least one person on your team who can turn data into useful visuals. Data visualization is a study in and of itself, and the better you can present data across your business, the more effectively it can be used.
  • Software engineering - You’re going to need the equivalent of a full-stack engineer (or a few of them) to maintain software and build data workflow automations, likely in Python. Some front-end applications might hold the data that completes a puzzle in your backend data pipelines, and you’re going to need someone who can make those kinds of connections.
  • Data cleaning/management - All of your data sources will come in unique formats — cloud data warehouses, social media feeds, e-commerce platforms, ETL platforms — and you need people with skills that can take this data and make it useful.
  • Business strategy - Business savvy is crucial. You need at least one person who speaks the same language as marketing, merchandising or sales, and product development. This makes it easier for everyone to turn your company data into business decisions and new opportunities.
  • Human communication skills - Since data analytics is highly technical and math based, you have to put some extra focus on finding team members who are also social and fun to work with. It’s ok to have a team with a data engineer that is more comfortable with numbers than humans, just make sure to balance that with team members who can communicate with their peers.

Questions to Ask When Building a Data Analytics Team

Do you really need a chief data officer or just a data scientist with a great business mind and interpersonal skills? This is the kind of vital question you’ll have to ask as you build a data analytics team for your organization. Here’s a list of other important questions to ask yourself as you build your data team:

  • How big does my data analytics team really need to be?
  • Do I have the right mix of team roles?
  • Should my data analytics team be centralized or decentralized?
  • What’s my budget for this year? For next year?
  • Am I planning the right roles for the future?
  • Are my team roles clearly defined?
  • How does my team support company data strategy?

If you want an expert opinion on any of these questions, we’re happy to help. Shipyard builds and provides tools for data analytics teams to create solid data infrastructures that connect data stacks from end-to-end. Our team is happy to help you wrap your head around anything in this article, just schedule a 30-minute consultation with one of our data experts.

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