High-Performing Analytics Team

In this article we'll be covering :

  • Understanding Business Analytics
  • Factors Driving Team Structure
  • Key Players within the Team
  • Additional Considerations
Updated on
Updated on

Creating a High-Performing Analytics Team


Due to the rapid pace of adoption, the global analytics market is set to rise from USD 28.12 billion in 2021 to USD 118.42 billion by 2028.  Many enterprises are building meaningful partnerships with analytics outsourcing providers, while some are updating their existing analytics infrastructure, with a host of organizations just getting started on their analytics journey.

Building an analytics team that can address and manage diverse business expectations is not an easy task.

Some questions we often get from business stakeholders are:

  • How do I get the momentum going for analytics in my organization?
  • What kind of team do I need? How do I build this team?
  • What about apps? Should I build one big app or several small ones?
  • When should we progress from operational to descriptive to perspective to advance data science teams?

Essentially, entrepreneurs want to know how to build a high-performance analytics team at every step of the organization’s data maturity journey. The answer to this question would vary depending on the type of organization involved. You may be small to a medium enterprise looking to invest in a dedicated analytics team, or a multinational corporation seeking to enhance your analytics capabilities. However, the essential recipe for success remains the same for all.

This article is aimed at understanding how you can set up your analytics team structure for seamless deployment and efficient operation. But first, let’s take a quick look at the fundamentals of business analytics.

Basics of Business Analytics

Business analytics is a multi-faceted process, demanding an amalgamation of expertise from mathematics and software engineering, to data discovery and data visualization. It involves the application of business intelligence tools for analyzing various aspects of a company, ranging from the performance of specific business functions, products, and services, to customer experience and brand equity.

Your business analytics framework is heavily dependent on the industry you are involved in, your business structure, and what you expect from your analytics investments. For example, if you are in consumer goods manufacturing, analytics can help you in a wide variety of ways from optimizing your manufacturing process, to improving product development and innovation. If you are directly involved in the retail industry, employing analytics can help you track customer behavior across channels, predict market trends, and so on. Insights from these analytics will in turn enable you to manage inventory, customize marketing strategies, influence sales, and boost ROI.

Building an analytics team, comprising state-of-the-art BI tools being operated by skilled personnel, can allow you to transform your business from the ground up. The following are some considerations to be made while constructing your analytics team structure.

Aspects Affecting Analytics Team Structure

There are a few fundamental ideas that should guide you toward successfully setting up your data team.

  • You need to understand that business intelligence begins with the word business, and you need substantial business knowledge before you start thinking about how to build your team.
  • You should also appreciate the fact that you want an ‘analytics team’. You cannot experience analytics success with just a talented analyst.
  • You need a team where the players support each other and deliver business intelligence that offers your company a competitive edge.

The following are three factors you need to consider when building your analytics team – the size of the team you wish to set up, how centralized you want the team to be, and how it integrates with your company’s overall data strategy.

  • Team Size
    The one-size-fits-all approach doesn’t work in this instance. Typically, the larger the organization, the more data-driven it will be. Thus, calling for a larger analytics team.
    You will need to consider the amount of business data that the team will be handling, and the number of projects they will have to work across within a specific time period. Last but not the least, who is the team catering to? Are they operating within a single business function or are they aimed at assisting stakeholders and decision-makers across the enterprise?
  • Analytics Model
    It is your discretion whether you want to employ a centralized analytics team, catering to the entire organization’s BI requirements. You can also have a decentralized setup, where each business function has its own resources for analyzing data specific to its department. Even a hybrid model can be employed where analysts enjoy a little more flexibility but have to oscillate between business units and disparate datasets.
    You have to evaluate your business process and your organizational data, to make this decision. The model you choose will influence your analytics team structure, the analytics process, the BI tools, and how the insights impact business decisions.
  • Data Strategy
    Your organization’s data strategy will determine your analytics roadmap, and consequently, be a deciding factor behind how you define your analytics team roles. If you feel that every business decision taken has to be data-backed; then it makes sense to have a team capable of accessing all the organizational data, analyzing it efficiently, and supporting stakeholders across the organization.
    But if you determine that you need analytics for improving a specific business function such as sales, you can employ a smaller team to focus on just sales data and coordinate with the sales team.

Key Roles within the Analytics Team

Building an analytics team will not involve the same components at every organization, but you can’t expect high performance with just a group of Business Analysts. The different roles comprising the team can be broadly categorized into leadership, business, and technical roles. Each of these roles has individual functions within them, where personnel are proficient in their respective set of capabilities.

A well-constructed analytics team should ideally include the following personas:

The “Analytics Champion”

First, you’ll need someone to head your analytics initiatives. It is the Chief Data Officer’s or Chief Analytics Officer’s job to set an analytics vision for the business. This person will need to ensure that analytics is a well-respected function with a strategic voice and ongoing participation in execution in the context of organizational objectives. So, make sure this person is ready to take on internal business leaders and external leaders if required before setting up the rest of the team.

Personnel in leadership roles should also have strong skills when it comes to communication, motivation, delegation, conflict resolution, and problem-solving, apart from the requisite analytical capabilities.

The “Data Guys”

These are Systems Architects, Data Engineers, Data Scientists, Data Architects, and related personnel, who are the masters of data discovery, extraction, and governance. They aren’t the developers of the BI tools themselves, but the human intelligence required to deal with the logistics surrounding the automated analytics process, including source data governance, acquisition, management, aggregation, security, and scalability.

You need a strong technical team to adequately support your BI team and its analytics requirements.

They will be the first layer of validation experts, ensuring data gets populated in the right cadence, frequency, and quality. This team is also responsible for streamlining server architecture for on-premise solutions or managing administrative capabilities for cloud-based BI solutions. They know how to establish a flawless data foundation, and have a keen eye for creating the best possible development environment.

The BI Team

These team members can help with requirements gathering and project management for analytics projects, produce static and dynamic BI, third-party in-tool reporting, and base-level analysis. They are your foundation for establishing subject matter expertise on the team, and your first tier of support for analytics requests. Value and nurture them as much as possible.

Members of this team also need to possess communication and collaboration skills, apart from their capabilities for analytics and data interpretation.

The APP Builders

Builders of reporting apps and BI solutions create easy-to-use applications for the BI and business teams. As the skillsets of data providers and the BI teams vary, app builders help bridge this gap.

The Storytellers

We miss out on this aspect more often than not. The storytellers are the people who identify the narratives behind the data, helping you visualize the right insights through your dashboard. They add context to the data, ensure accurate delivery, and leverage their UI-UX skills to help you comprehend the business intelligence from the raw data.

Additional Considerations for Effective Team Building

Effective Onboarding

While developing your analytics ecosystem you need to identify which of the above-mentioned analytics team roles your data team really requires. This will also help you map out if any of the personnel will be capable of contributing across functions and projects. Data analysts and architects can leverage their capabilities in varied projects, making them flexible assets within the team.

Onboard the right talent, so that your data team is not just able to handle the projects assigned to them, but can operate smoothly. Every individual working in coordination with the rest of the team will generate positively influence productivity, and foster a great work environment.

Plan for Investments Upfront

Building an analytics team requires payroll setup, technology and hardware investments, and initial seed money. All this requires investment and the two following approaches could work here.

  • Building a business recovery model
    As seen in our experience working with multiple customers, building on seed money by pitching to internal stakeholders (business stakeholders) for additional funds always helps. This model creates flexibility in scaling up programs and supporting team members when the time arises, provides enough cash to manage business downturns, and creates a buffer for rework on any analytics deliverable.
  • Having a central committee for financial approvals
    This would be another way of looking at financing your team’s investments but this approach normally takes time and throws up bureaucratic hurdles from time to time.

Intra-organizational Collaboration

The data team needs to work closely and collaborate with engineers, product managers, product designers, marketing teams, and sales personnel. Analytics impacts every business function, and real-time insights can revolutionize the way that each function operates, and the decisions that they make. Effective collaboration across functions can help you enhance the entire organization’s operations from the ground up.


Organizations today have data amalgamation and reporting processes that typically run in silos. The larger the organization, the bigger the silos. And that is why the role of the internal analytics team becomes so important—after a few pilots run, their role is to champion the cause of analytics across teams from time to time. Eventually, once the team matures, they can define future analytics roadmaps in collaboration with the business teams.

Managed services are heavily process-dependent and come into play when there is an unexpected gap in the team, maybe due to a member leaving the organization. It ensures that there is business continuity and provisions in place for dealing with such situations. Internal or external disruptions should not impede analytics initiatives, and considerations have to be made for accommodating contingencies.

Skill Set Upgrade

With the introduction of new technology, there has been an increased ability to compute terabytes of data at a rapid pace, along with beautiful visuals created by reporting and analytics tools such as Tableau and PowerBI, helping with data visualization and understanding business insights.

It is imperative to train your analysts and other functional folks in new technology and methodologies. This is even more essential in the case of advanced data science personnel, who work in a field where there is a constant evolution.

Setting Expectations

As business owners, we want data and analysis to be available yesterday. However, a lot of data processing needs to happen before we can see the dashboard. Hence it is imperative to set expectations for the delivery of the insights and data among the business stakeholders. The timeline for data being converted to BI should be clearly defined, or else it does not take long for small issues to snowball into larger problems.

Managing Culture

Building an analytics team within the organizational context is a cultural change, and needs to be handled appropriately. As you onboard and start building internal teams, there will be reluctance from old-timers and existing practitioners resisting change, so it is important to communicate the value that will be added by the new teams, and ensure ongoing communication across teams is not affected.

Here are a few guidelines that can help ease the transition:

  • Provide a challenging and exciting environment
    Data analysts by nature seek out challenging and high-performance environments, and colleagues with whom they can bounce off and build on new ideas. The analytics team lead should be able to assemble a dynamic group of people who are not afraid to disagree with or entertain the most inventive of solutions.
  • Invest in retention but do not splurge
    The continuity of the data analytics team is very important. It takes time to build up intellectual trust in this field and to understand the unique ways people approach problems. If a team member leaves, sure you can hire a new analyst with skills, but it will be hard to quickly re-create the team knowledge base and trust that had been built up. Serious investment in team retention is far less costly than turnover.
  • Provide freedom
    Data analysts and data scientists like to get their hands dirty with data and usually work hard. Make sure they have some built-in downtime and flexibility on the job, to pursue new ideas and research.  Analysts crave time to invest in their own processes and productivity.  Allowing this could pay off later in the form of on-the-job satisfaction, retention, and innovative breakthroughs in projects.
    Building the right team culture and getting upfront investments (seed money) is key and should be planned early in the journey. While team culture prepares the ground for success, you can make incremental improvements to the team. With time, effort, and patience, you can develop a high-performance analytics team, maintain it in-house, and improve your business from within.

Once the team is operational you can also accentuate your analytics initiatives, by accessing expertise from an external source. Analytics Outsourcing can enable you to support your in-house team with the exact industry expertise that might be needed for a project. Your analytics team structure can also gain some flexibility, with an additional helping hand working in cooperation, and delivering insights. Assess your situation, make the right decision, and optimize your analytics journey for maximum potential.

Note: This article was recently updated to offer deeper insights into business analytics and building an analytics team.


Devendra Desai
Devendra Desai
Devendra has over 18 years of industry experience in Data Analytics, AI and Digital Transformation, with business and technical specialties. He has worked with global...
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