As companies become more data-driven, a data analytics team becomes crucial for scaling business intelligence.
Data teams are no longer limited to large enterprises — they are also quite effective for smaller firms. Regardless of size, every business must build a solid data analytics team to reap the benefits of modern data science.
So, how should you structure your data analytics program? We'll guide you through analytics functions, team org structure, duties, and compensation.
A data analytics team can handle various data management operations, from data ingestion to data warehousing and analytics. But for maximum effectiveness, the organization must define its data analytics team's objectives, responsibilities, and roles.
|Analytics||Leadership needs data to inform decision making||Build dashboards and narratives that can inform strategic decision making||Deliver insights|
|Automation||Too many tasks are being accomplished manually||Save money and time by replacing manual tasks with automated processes||Lower costs|
|Product Development||There is an opportunity to generate revenue by building a data product||Turn raw data into products from which revenue can be generated||Grow revenue|
Here are some common objectives a typical data analytics team would have:
Establishing proper data collection and analytical facilities to derive business insights
Deriving strategic solutions from periodic data analysis
Support for non-data teams with easy-to-use visualization tools
Developing analytical models for developing predictive analytics capabilities via internal and external applications.
You need specialized skills, roles, and responsibilities within your data science team to achieve these broader objectives. These roles include data engineers, data analysts, and data scientists. And if required, some advanced positions, such as data manager or chief data officer.
The first and foremost benefit of employing a data analytics team is to improve business intelligence and outcomes. You need to be able to build the basic data foundations to get relevant data-driven insights and business intelligence in the long term.
Define your data architecture, data governance, standards, and policies, build your R&D library and model repositories, and set up a robust data onboarding process.
Involve all the relevant stakeholders, make sure you collaborate, and form a proper framework that enables data-driven decision-making.
Build scalable big data models and architecture and uncover ways to optimize your data.
While most smaller teams might have data science engineers who can do a bit of everything, your team must have at least one specialist data analyst who can build the data models and architecture required.
As your data needs grow, you might be tempted to increase your team size proportionally. Now, if you want a data task completed within a fixed timeline, the best method would be not to increase your team size arbitrarily.
Instead, we suggest leveraging data automation as much as possible.
Many data-related tasks like data cleaning, data collection, and consolidation can be partially or even fully automated with the help of AI-based automation tools, machine learning, and intuitive data management tools.
You can also automate your data pipelines and the data model-building tasks. And this automation also falls under the responsibilities of your data analytics team.
The ratio of analytical engineers to data team size varies across organizations, and in most cases, it sits somewhere around 1:4 to 1:10.
Pioneering scalable uses of data isn't just for back-office discussions. Product leaders often need a data scientist to help them solve customer pain points.
SaaS applications routinely ingest data from multiple sources to deliver easy way for users to view and make sense of all the data gathered.
Accelerate customer onboarding through easy data imports, surfacing insights from Machine Learning with embedded ETL solutions.
This is an example of using data to enhance an existing software solution without burdening a software engineering team.
In most companies following a hybrid approach or a decentralized embedded model of data team structure, there is a need for a central entity that can act as a Center of Excellence (CoE) across the business units.
A CoE can help you improve team collaboration, communication, and information sharing and accelerate data-driven decisions. It is required to set up data governance, best practices, and quality control and find solutions to any challenges your data teams might face.
When you start to build your data analytics team, there are several things you need to consider.
The size of your team, the roles you need, and the objectives and responsibilities the team must carry out.
Best ways for team members to collaborate to support business priorities.
Executive representation to achieve a data analytics roadmap and discuss projects with other business units. For instance, a Chief Data Officer sets initiatives in motion and advocates for the organization's overall data strategy.
A centralized model follows a data team structure where a single centralized group handles all data requests. And this kind of data analytics team typically consists of a head of data, a few data engineers, data scientists, and one or more data analytics or modelers.
Some advantages of using a centralized team are:
Greater collaboration as the entire team is involved in the data meetings, and they can easily communicate with the stakeholders
Simplified knowledge sharing on dependencies
Faster data analysis sprints in a focused manner
Centralized priorities deliver high-value outcomes
A decentralized model follows a team structure where each business unit might form its own data team to satisfy its particular data requirements.
This type of team structuring could be necessary for larger enterprises where it would be too cumbersome for a central team to care for every need of individual business units.
It is more suitable for achieving narrower use cases where business priorities are handled well.
Allows for federated initiatives that can function as independent analytics projects for their business unit
If each business unit has a data analyst or engineer, the entire org can apply data-driven insights in parallel
The hybrid model aims to combine the best of both worlds. In this model, a centralized data team provides the best practices to be followed across the organization and handles the core data tables.
This model allows data scientists, data engineers, and data analytics to work on advanced analytics projects while adopting a broader governance framework
Business groups can start small with their data-led initiatives and scale as needed
Maintain shared interest in fueling data governance and business intelligence
The optimal team structure for an organization usually depends on its size and data requirements.
For an organization of about 100 people, a modest team size of seven people with a couple of data engineers, data scientists, and an analyst headed by a chief data officer can be considered adequate.
With optimal use of automation, a team size of 15 might be appropriate.
As the size increases, some companies also tend to follow a split team approach where a data science team with sub-teams takes care of day-to-day data operations, and a centralized data team is used to build the overall data architecture for the organization.
For larger organizations, the most preferred structure is a hybrid model with a Center of Excellence that provides a data framework followed by individual business units and their working groups hosting their data resources.
Data team leaders carry significant responsibilities that go beyond just team management. They must continue optimizing their data strategies and find innovative ways to use their data operations best.
Team leaders must also ask strategic questions reflecting an understanding of a business problem and craft how data models can help accordingly.
The top responsibilities of an analytics team leader include the following.
Data team leaders should be able to convey their findings and insights via the various KPIs and metrics to the C-level executives. And their responsibility does not just end with sharing information. It also includes seeking input on business needs and devising solutions to meet them effectively.
Understanding business needs starts with a gap analysis to see where the business is lacking and can improve. Team leaders can use their data capabilities, tools, and functions to analyze and arrive at optimal business strategies backed by verifiable data insights.
With proper metrics, you can easily measure the impact and progress of any business function. Of course, data team leaders should be forerunners in using these techniques to measure the impact of their business operations. For instance, pricing optimization can have a massive benefit in terms of revenue growth.
Once the possible growth opportunities are identified, data team leaders could prioritize them in terms of their business value, cost, and universal factors that derive business benefit. They should be able to use the information collected to take data initiatives and start relevant analytics projects.
Data team leaders have access to the necessary data sources to identify business problems, collaborate with the technical teams, and identify the data sources they can work with.
They can also collaborate with multiple departments across the organization to collect crucial information regarding their primary users, target audience, competitor analysis, etc. Centralized teams can quickly survey and aggregate the required data sources.
One easy way to achieve a good result within a data team is to identify the easy wins early on. These include simple yet valuable functions like data visualizations and data dashboards.
Once you solve simpler tasks that add value to your business, focus on other core business problems. Easy wins also boost motivation and build a stronger team.
For example, when your team is just getting used to a data-driven business model, demonstrating a smaller analytical project with immediate results would help you get better buy-in from stakeholders before proposing a monumental change.
You can implement a simple dashboard to show employee data and their attrition rates and collect information on employee surveys to present a correlation between these and suggest what can be done to improve employee attrition rates. This project might be much easier to implement while you can later focus on making these data to redesign your hiring strategies further.
Data team leaders make the business decisions on which data tools and technology. The choice of the data warehouse system, cloud or on-premises installation, big data configurations, datasets to use, and more should all be taken properly.
A good recommendation would be to form a robust data strategy before building complex data infrastructures.
Solving business problems is the primary purpose of setting up data teams. So focus your data analytics team on the most valuable business ailment while you work on getting infrastructure and tools. Apply an agile startup methodology to scale your data management functions. Make sure you don't buy things you don't need.
Data analysts are the architects of a data team working closely with data engineers. They create real-time data visualizations and dashboards and use statistical techniques to understand data. They are also responsible for developing and implementing data analyses and collection systems.
Data analysts should be well-versed in database tools and data warehouse solutions. They should also be able to work with statistical tools and dashboards and have expertise in the business domain.
The business analyst role is similar to a data analyst. Still, they work more closely with how a business operates rather than the day-to-day data operations within a company. They should know business problems, values, and processes well. They often use a variety of software providers to collect and build business reports.
Business analysts should have good problem-solving skills and knowledge of analytical tools and data models. Building out data dashboards for non-technical stakeholders should be easy.
Data engineers are responsible for the day-to-day operations of the data team. Their tasks include designing data sets, forecasting, collaborating with data analysts and scientists, maintaining databases, etc. They should be familiar with achieving optimal data quality from structured and unstructured data.
Skills in SQL, data warehousing, data architecture, coding, machine learning, and ETL/ELT processes knowledge are expected from a data engineer. Past experience on a software engineering team should help place top-tier candidates. Experience with Python is a major plus.
Data scientists are responsible for creating algorithms, optimizing data models, and creating new data visualization modules. They're innately driven by researching tools and programmatic ways to solve data challenges.
A data scientist should have skill sets related to statistical analysis and computing, machine learning, deep learning, processing large data sets, data visualization, data wrangling, mathematics, and programming.
A Head of Data or Chief Data Officer is a leader who interacts with the C-suite executives and the data team managers. They are responsible for setting a data team charter, prioritizing data initiatives, and spearheading the data strategy across business units, including the analytics roadmap.
Chief data officers should have strong communication, managerial, analytical thinking, and data-driven decision-making skills. They should be familiar with data warehouses such as BigQuery, Redshift, or Snowflake. Hiring data analysts and building up a data science team is a typical task needed by such leaders. Speaking about the business outcomes led by data is also a must.
$200,000 — $1,000,000
(A higher salary depends on the size of the organization.)
It's easy to fall victim to being a data report order taker. That's not why a company hires a data science leader. Instead, apply these best practices to bring your data strategy to life.
What are your must-have priorities vs. curiosities?
Determine if data analytics is desired instead of automated data products.
Is your team and mandate big enough to expand scope?
What are the highest-value data projects?
What data do you need to solve the problems?
Does the data exist in your organization to solve these problems?
Which is the highest ROI problem to solve with data?
What is the minimum viable tech stack to solve your identified problems?
How do you solve these problems? (What's holding your data scientists back?)
With these data science priorities, you'll position yourself to fulfill the company's mission with data.
I recommend these in-depth articles that will help you build out your data analytics team:
Enterprise Analytics Explained: Use Cases, Benefits, & Tools
Ben Rogojan (The Seattle Data Guy) and I collaborated to pull together a step-by-step guide. You can watch his YouTube summary here.