How to Build a Powerful Data Analytics Department Structure in 3 Steps

Data analytics is one of the decisive business aspects that can assist your business to scale and reach new heights.

McKinsey states that analytics-driven organizations are favorable to acquiring new customers 23 times more than non-analytics-driven businesses.

Not only that.

Data analytics-driven businesses are likely to surpass their competitors nine times in customer loyalty to increase their customer base and retain them.

95% of businesses consider the need to manage semi-structured and unstructured data as a problem.

You need a quality data analytics team that knows how to learn data analytics trends and requirements to deliver faster results, quality, and sustainable business results to escape such problems.

How can you do that?

Let’s dive straight into the learning end of things.

Key Players of Your DA Team

Data has become the new oil. Your business needs to manage and organize your business and customers’ data to enhance your business operation and scale it to new heights.

If your business has a small group of employees, everyone should be a little versed in interpreting and gathering data.

But once your organization grows or manages a large organization, it’s important to onboard data specialists to handle specific data analytics tasks.

We have filtered a few important job titles for your analytics team. You can add a few once you increase the scale of your business. Plus, check this guide to find the core roles and responsibilities of the data analytics team.

  1. Data Scientist

    Professional data scientists are the core element of your data analytics team. Data science experts have the knowledge and the skills to leverage advanced programming, mathematics, and other tools like machine learning, artificial intelligence, and statistical modeling to deliver quality results.

    They use these data science assets to collect, analyze, and interpret large data sets and drive valuable information, patterns, and trends for the business.

    A data scientist develops hypotheses, analyzes market trends and customers, and makes inferences to get an overview of the existing business ecosystem.

    Data scientists work in a data analytics team of a business to mine big data that can help businesses identify new opportunities and improve their existing operations. They are responsible for setting up best practices for interpreting data, science tools, and collecting data.

    The role and responsibility of the data scientist vary from business to business, but they typically perform work designed to shape and inform data projects. They should have experience in using statistical tools to deliver better data outcomes.

    As the concept of a data scientist is extracted for some major technological modern fields, including math, statistics, science, computer science, and chemometrics, they need to have a mix of skills, experience, and personality traits to handle all the areas.

    They are responsible for analyzing huge data sets of qualitative and quantitative data. Data scientists also have the role of developing statistical learning models that can perform quality data analysis.

    For example, a data scientist in your data analytics team may identify challenges that can address a data source or data project to collect for future use. They may spend most of their time designing algorithms to mine and organize data.

    You need to understand the roles of the data scientists and test the applicants based on managing these roles to create well-qualified data science professionals for your business.

  2. Data Engineer

    Parallel to the data scientists, data engineers are also important for building your strong data analytics team. They are responsible for building, designing, and maintaining datasets used in data projects.

    Data engineers prepare the data for operational or analytical uses and build data pipelines to combine the information from multiple sources.

    They coordinate with the data scientists and the data analysts in the team to handle the data analytics projects, improve data transparency, and enable the organization to make well-informed decisions. They consolidate, integrate, structure, and clean data in analytics applications.

    For example, a data engineer integrates and collects data from multiple sources and builds a data platform that other team members can use to maintain and optimize the data warehouse.

    The majority of the work done by the data engineers is related to the preparation of the ecosystem and infrastructure that can be used by different data teams and other departments of the organization.

    The amount of data collected and handled by data engineers depends on the business size and generated amount. If you have a bigger organization, the complexity of the analytics architecture increases, and it requires a larger team of data engineers to handle the responsibilities.

    Different industries like retail, finance, and healthcare are more data-intensive and require a qualified data engineer workforce to prepare the data for analytical use.

    The role of a data engineer can be divided into three components.

    2.1 Generalists:

    The general focus of the data engineers is to work in a small team for intake, processing, and end-to-end data collection. It requires less knowledge of systems architecture and good data engineer skills to handle the assigned tasks. A data scientist looking to switch his profile into a data engineer can fit smoothly in the generalist role.

    For example, if a generalist data engineer is assigned to create a dashboard for a metro-area food delivery service, they would display the number of deliveries for the past month and predict the delivery volume for the following month.

    2.2 Pipeline-centric engineers:

    The pipeline-centric engineers work in a midsize team where the projects’ complexities are higher than the general scenarios. Most midsize and large companies hire pipeline-centric data engineers to handle the required tasks.

    For example, a food delivery company can handle a pipeline-centric project by creating a tool for data analysts and scientists to search for information related to deliveries. The data team might look at the drive time and the distance required for deliveries in the previous month and then predict the future growth numbers.

    2.3 Database-centric engineers:

    These data engineers are assigned tasks with maintaining, implementing, and population analytics databases. The large companies assign the role where the database numbers are high and data is distributed. The data engineers work to tune the database and the pipeline for efficient analysis and focus on creating table schemas using ELT (Extract Transform Load) methods. The data is copied from multiple sources in a single destination or warehouse system.

    For example, a database-centric project operated in a large national food delivery service would be to manage and design an analytics database. In addition to handling the database, the data engineer would script the code to extract the data in the main application database and then into the analytics database.

    The responsibility of the data engineer is to provide data in suitable formats to the data scientists for predictive analytics, data mining applications, and machine learning. They also need to deliver aggregated data to analysts and business executives to assist them in analyzing it and applying the extracted results to enhance business operations.

    Data engineers have to deal with unstructured and structured data with different approaches to handle both types. The toolkit of data engineers includes various big data tools and technologies like data ingestion and processing frameworks, among others.

    Figure out your business scale and requirements to onboard the pitch-perfect data engineers to handle your data analysis tasks.

  3. Data Analyst

    Data Analyst professionals use the extracted data to perform direct analysis and reporting. The data analyst gets a refined and transformed data set because of prior processing done by the data scientist and engineers.

    They don’t interact with the unstructured and raw data sets because it’s hard for them to perform different data analytics strategies to extract the right trends and patterns. They solve or address different challenges based on the business requirement and project. The analysis done by a professional data analyst may be diagnostic, descriptive, perspective, and predictive.

    Data Analysts are responsible for maintaining dashboards, preparing data visualization, generating reports, and using the data to forecast business operations.

    Data analysts are equipped with technical skills to handle data processing using programming languages like R, SQL, or Python. They have the experience of working with different data visualization tools like Qlik or Tableau to present the insights to the non-technical leaders for making well-informed business decisions.

    They use statistical and mathematical skills to gather, measure, analyze, and organize data to answer predetermined questions or problems.

    The roles and responsibilities of a data analyst revolve around gathering and interpreting data to solve specific problems.

    Here’s what they do daily:

    Gather data: They collect the data using different extraction techniques, surveys, and tools. They can track the visitor characteristics or buy datasets from different data collection specialists. Your team’s data engineers and data scientists can assist them with the collection process.

    Clean data: The data is cleaned, outliers, errors, and duplicates are removed from the data, and the structuring is done. The quality of the spreadsheet is maintained using programming language and basic skill sets to ensure that the interpretations are not affected.

    Model data: The designing and cheating of the database structure are handled in the modeling process by the data analyst. The data analyst needs to choose the data they need to collect and store. They need to categorize the data and work on the visual aspect of the data.

    Interpret data: A data analyst needs to interpret the data by finding trends and patterns in the structured data set to solve the business problems or forecast the right business path. Different interpretation techniques can extract useful insights from the data and assist the business team in making informed decisions.

    Presenting data: Once the interpretation is made, the data analyst creates well-vetted reports that can be analyzed by the business’s upper management and use the valuable points for the business growth. The reports can be preferred by using advanced business tools that enable the data analyst to add graphs, charts, and other visually attractive data forms to make the findings easy to understand, even for a non-technical professional.

  4. Other High-Level Positions in the Data Analytics Team

    Besides being a data scientist, data engineer, and data analyst, you need to include a leadership or management role in your data analysis team to channel the workforce in the right direction.

    It becomes important for a large organization to have job titles like data management, chief data officer, or data director to ensure that the data analysis team operates under the right leader who can guide them to achieve better results.

    Leadership positions handle the front end of the data analyst projects, and the leader focuses on the idea creation and influence for the team. They ensure that the project aligns with the goals and that the results are delivered within the limited time.

    Leaders motivate the professional team of data experts and ensure that they collaborate to solve different problems. A successful data analytics leader has many soft skills like teamwork, communication, analytical, motivation, conflict resolution, and problem-solving.

    Leadership can deliver directly to the team and solve the friction in managing data analytics projects.

    Let’s understand their roles and responsibilities compared to other departments in a data analytics team to understand them better.

Different Analytics Team High-Level Roles

An analytic team can engage in multiple types of work like Training, ad-hoc query handling, production support, and new project development.

You need to consider different work types while selecting the role and size of your analytics team.

If we talk about high-level positions, three groups have different roles.

Leadership Roles

Leaders and sponsors Analytics director or Program manager Project manager
  • Influence the team but are not involved in the team’s daily activities.
  • They work to get the budget for the project by highlighting position projections.
  • Helps to determine the scope of individual programs or projects and priorities in the project.
  • Checks project orchestration.
  • Ensures that the team operations are highly coordinated and systematic.
  • Helps in defining internal processes, staffing, delivery methodologies, and architecture of the overall project.
  • Manages different project issues that may arise.
  • Overchecks the charts’ progress and assigns different tasks to the team members.
  • Manages the scope, budget, and upwards communication.
  • Can perform part-time roles depending upon the project size to help the team align with the time restrictions.

Business Roles

Subject Matter Experts Business analyst
  • Understand the business terminology, process, and stakeholders.
  • Can be considered as a guide or data navigator.
  • Many SMEs have done reporting using different tools, including Excel, to help data experts solve dynamic issues.
  • Can also help in finding the data in the system and interpreting it for making future decisions.
  • Collects and manages the requirements for helping the data expert team perform to the best of their abilities.
  • Ensures that the information is testable and easily understandable.
  • Don’t have the technical knowledge but can solve problems with their critical thinking and calculative approach.
  • They bridge the business and the technical team to enhance productivity and achieve the desired outcomes with clarity.
  • Helps the technical team to understand business needs.
  • Their tasks/job may overlap with the product manager or subject matter experts.

Technical Roles

Architect Data engineering Data modelers Visualization specialist
  • Helps in setting up a consistent approach in the delivery and design.
  • Avoids the program from having unsupportable products and disjoints.
  • Reviews individual processes for better results.
  • Selects the technology to ensure smooth functioning and keeps everything knitted together.
  • Handles system integration points.
  • Has an understanding of ELT and ETL principles and modern EDW design.
  • Can handle data movement, cleansing, transformation, and storage for better data analysis results.
  • Has a better understanding of programming languages like python and SQL.
  • Can design targets like data lake etc.
  • They help to model optimal storage structures for other data teams.
  • Can sometimes play an architect role to help the team.
  • Can handle modern data warehouse structures.
  • Aware of the Star Schema Methodology
  • Provides details about the user insights with detailed visualization.
  • Uses the right visuals for highlighting the right information to ensure precise information delivery.
  • Add visual context for comprehension of the information.
  • Quality UX/UI skills.
  • Arranges visuals to plot a story for smooth execution.

These brief roles need to be considered while setting up a frictionless analytics team.

But there are a few points that you need to consider while hiring individuals who can perform these specific roles in your analytics team.

Have a look.

3 Things To Consider Before Building a Data Analytics Team

  1. Understand your requirement

    You need to answer the question about how many data analytics team members you require to handle the business or project requirement.

    The size and the role of your data analytics team will be clear if you know how many projects will they handle within a specific time? How much data will be assigned to the team to handle? Who will manage the data team operations? And how will the reporting be managed?

    The answer to these questions is not the same for every business. You need to better understand the project, the requirements, and the goals you want to achieve with your data analytic team.

    Having clarity can help you specify the number of data analytics team members you require and the roles they’ll perform to help the business get a better understanding of the data.

    The general rule says that the larger the size of your organization, the more data-driven it gets. So, you require a larger data analytics team to handle the data’s complexities.

  2. Set the team’s approach

    Setting up the data analytics team approach can help you optimize the results and achieve your desired goals faster.

    Depending upon your organization and the relationship to data, you need to decide whether you want data analytics to be centralized, decentralized, or hybrid.

    The centralization approach gives more control over the data analytics operations but the workload increases because they need to handle requirements for different departments in the organization.

    A single team serves the entire organization, requiring a large team to handle work in larger organizations.

    The decentralized approach includes creating data analytics units for different departments by providing them resources, employees, and processes to handle. It minimizes the workload of the data analytics team, and different departments can get their desired data results from their respective teams.

    Or you can flow a hybrid model that has a mix of both worlds to deliver tailored solutions based on your organization’s requirements. There are pros and cons related to these team structures, but you’ll hit the bullseye if you make calculative decisions.

    Understand your requirements and structure your analytics team optimized to enhance productivity and extract the results you want.

  3. Hire collaborative and talented individuals

    Once you have set a solid foundation for building your analytics team; you need to create a robust hiring process that analyzes the applicant based on the roles they applied for.

    You need to focus on hiring talented yet collaborative individuals who can work in teams and deliver quality results. Creating a motivated and collaborative team can help you achieve your goals quicker with precision and accuracy.

    It can create a balanced work environment in the organization and boost other departments to improve their work efficiency.

    If you check these important points, you can create the best analytics team to help you get the best outcomes and predictions from the available data.

Frequently Asked Questions

How to structure a data analytics team?

Structuring a data analytics team is important to ensure that the organization achieves the desired business results and makes well-informed decisions. You can structure your data analytics team by assigning the roles and responsibilities to the professionals based on their talent and expertise. You need to understand your business goals and how data analytics can assist you in achieving those goals.

Is data analytics profitable?

Data analytics can’t help you directly drive money to your organization unless you provide a full-fledged data analytics service to other businesses. But it can help you predict and analyze patterns and trends for the given data to help your organization or department make well-informed decisions. It can help you save or make money by enhancing your existing workflow based on the data analytics results and standing out from the rest with data-driven decision-making. If we boil it down, data analytics is the core aspect of making your organization profitable in the future.

How to get the best data analytics results?

Creating a collaborative work environment and ensuring that individuals in your data analytics team clearly understand their roles and responsibilities can help you extract the best data advanced analytics results. Regarding the technical aspects, you need to provide your data scientists, data engineers, and data analysts with the best tools and technologies to perform their assigned tasks with minimal effort and maximum efficiency.

Why do I need a data science team?

You need a data science team to leverage the power of data to tailor your business operations and optimize your workflows. It can help tailor your decision-making and focus on the path to business success. Different data science techniques can structure the raw data and find the patterns and trends to deliver optimized business results and insights.

How to hire a great data analytics team player?

Hiring a great data analytics team focuses on technical and collaborative aspects. You need to check the hard or technical skills of the data analytics team members based on the business requirement and then check the applicant’s soft skills to ensure that they are the right fit for the job. You need to ensure that the applicant can help other team members to improve and achieve their assigned tasks and work in a collaborative environment or not.

Structure A Quality Data Analytics Team

Being a modern-day business, you need to make data-driven decisions to stand out from the rest and achieve your desired business goals with finesse.

Hiring a data analytics team is one of the important aspects of achieving your goals, and you need to build the team professionally.

Now that you understand the nitty-gritty elements of structuring your data analytics team don’t go gung-ho to hire your team right away.

You need to understand the different professional responsibilities and roles based on the data and your business goals.

Get a better understanding of the data analytics requirement and then create a tailored hiring process to onboard talented data scientists, data engineers, data analysts, and leaders in data analytics.

Gaurang Bhatt

Written by

Gaurang Bhatt

Gaurang has 15+ years of experience solving complex business problems and enabling businesses with data-driven decisions using data analysis and predictive modeling tools like Tableau, Power BI, Looker, and Google Data Studio. His expertise lies in data visualization, reporting, and creating ETL pipelines. In addition, he is passionate about exploring different technologies like machine learning and AI. He shares his knowledge and learnings on the LabsMedia platform.