In the hyper-competitive business world, data isn’t just a buzzword—it’s the backbone of strategic decision-making. Statista reveals that the value of the big data analytics market is projected to exceed $655 billion by 2029, which only underscores the magnitude of data’s role in transforming businesses. However, organizations require a competent data analytics team to leverage data to its full potential.
Whether you are considering building an in-house data analytics team or outsourcing to data analytics experts, making an informed decision that aligns with your business objectives is crucial.
This article will provide you with the top five key factors to consider before building a data analytics team. We believe these insights will help you evaluate the benefits, challenges, and commitment involved, ensuring that you can maximize the potential of your data resources.
We also made a video on the topic on our YouTube Channel:
Of course, you can continue reading the entire article to gain a comprehensive understanding or use the clickable table below to jump to the section that interests you the most.
Table of Contents
Data Analytics Team Responsibilities
Data Literacy, Data Culture & Collaboration
How to Build a Data Analytics Team FAQs
In-House Data Analytics Team or Outsourcing Experts?
Bridge Skill Gaps with B EYE’s Data Analytics Experts
Building an Analytics Team from Scratch: Data Analytics Team Structure
When it comes to structuring your data analytics team, choosing the right approach can significantly impact the effectiveness and success of your data-driven initiatives. Understanding these structures will help you determine which model aligns best with your specific business requirements, enabling you to maximize the value of your data resources. In his Forbes article, Lokesh Anand, cofounder of a leading data engineering company, goes over the key data analytics team structures: centralized/top-down, decentralized/bottom-up, hybrid teams, and the option of outsourcing data analytics experts.
Centralized/Top-Down
In a centralized structure, the data team operates from a central business unit, covering a broad spectrum of projects from various business functions. This structure enables a central team to control all data sources and technology. Typically nestled within the IT department or the CIO’s office, this arrangement aids in the uniformity of tools and platforms. However, its broad scope may limit its capacity to tackle business function-specific challenges.
Decentralized/Bottom-Up
A decentralized structure places data professionals within specified business units—sales, marketing, or operations. This granular approach aligns data resources with departmental needs rather than overarching organizational ones. While it leads to quicker insights, it can create chaos if each team adopts distinct platforms and methodologies.
Hybrid Team
An alternative method is to form a hybrid data team operating across IT and outsourced data analytics professionals. This model ensures efficient decision-making in larger, data-intensive organizations while maintaining cross-team consistency.
Outsourcing Data Analytics Experts
Outsourcing data analytics to external experts effectively bridges the gap between technical expertise and business needs. We at B EYE have been aiding multinational corporations in achieving their goals by providing a broad range of high-quality data analytics services, leveraging leading BI platforms like Qlik, Tableau, and Power BI to drive business improvements worldwide.
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Data Analytics Team Responsibilities
Data analytics team members are responsible for research, data collection, building data pipelines, data analysis, visualization, and reporting. They could also present strategic opportunities depending on your organizational structure. Essentially, the data analytics team’s task is to bolster business performance based on collected data, encompassing predictive analytics, historical trend analysis, and future market projections.
Key responsibilities of your data analytics team include:
- Eliminating data silos
- Building and automating data workflows
- Ensuring timely, accurate data delivery
- Creating reports, metrics dashboards, and advanced analytics capabilities
- Managing data solutions and technology decisions
Data Analytics Team Roles
A successful data analytics team comprises diverse roles that collectively drive valuable insights and support data-driven decision-making within an organization. Distinguishing between the data analytics roles and their specific responsibilities will help you assemble a well-rounded team covering all critical data analysis aspects. That said, let’s explore the key roles that form the foundation of a robust data analytics team.
Data Analyst
Data Analysts interpret data and turn it into information that offers ways to improve a business. They gather information from various sources and interpret patterns and trends to provide actionable insights.
Business Analyst
Business Analysts focus on optimizing business processes, reducing costs, and increasing revenue by leveraging data. They bridge the gap between IT and business, interpreting data to assess processes, determine requirements, and deliver data-driven recommendations.
Data Scientist
Data Scientists utilize predictive analytics, machine learning, and statistical modeling to understand patterns and trends in complex data sets. They leverage this understanding to provide strategic direction and actionable insights.
Data Engineer
Data Engineers design, construct, install, test, and maintain data management systems. They ensure that systems meet business requirements and industry practices. Their primary responsibility includes maintaining the smooth operation of data systems.
Data Architect
Data Architects create the blueprints for data management systems to integrate, centralize, protect, and maintain data sources. They define how the data will be stored, consumed, integrated, and managed by different data entities and IT systems.
MLOps
MLOps professionals manage the lifecycle of machine learning models. They oversee the creation, testing, deployment, and monitoring of these models, ensuring they are efficient and accurate in delivering the desired outcomes.
DataOps
DataOps professionals streamline the management, integration, and use of data. Their goal is to improve data analysis’s speed, quality, and reliability, leading to more agile and efficient decision-making.
Chief Data Officer
The Chief Data Officer (CDO) oversees a company’s data initiatives. They guide the data strategy and ensure that data-driven insights align with the company’s strategic objectives.
Project Manager
Project Managers ensure that data projects are executed within the scope, time, and budget. They manage risks, maintain stakeholder communication, and ensure project goals are met.
Data Analytics Support
Data Analytics Support roles assist in maintaining and troubleshooting analytics tools and databases. They play a crucial part in data integrity and system stability. At B EYE, we provide data analytics support services, prompt problem-solving, and personalized consultations to minimize issues and optimize the impact of your data initiatives through accessible and efficient support services.
Data Analytics QA
Data Analytics QA roles test data systems for errors and inconsistencies. They guarantee information quality, integrity, and confidentiality, ensuring adherence to laws and regulations.
Data Analytics Strategy Consultants
Data Analytics Strategy Consultants advise on the implementation of data analytics. They formulate strategies and consider an organization’s goals to enhance decision-making processes.
Data Literacy, Data Culture & Collaboration
“The most successful companies are leading from the top by making a strategic, sustained investment in upskilling and then sustaining their data literacy community on an ongoing basis,” says Paul Barth, Qlik’s Global Head of Data Literacy.
Indeed, data literacy is the bedrock upon which successful data analytics teams are built. Therefore, encouraging data literacy, fostering a data culture, and promoting collaboration can drive data proficiency and enrich the organization’s decision-making capabilities.
However, achieving these goals requires investment in various resources, including time, financial resources, and organizational commitment.
Data Literacy
Establishing data literacy entails providing training programs, workshops, and resources to enhance employees’ understanding of data analysis and interpretation. This investment in upskilling can empower individuals across the organization to effectively utilize data insights in their decision-making processes.
In the video below, Bernard Marr, an internationally best-selling business author, keynote speaker, and strategic advisor to companies and governments, and Alan Schwarz, a Pulitzer Prize-nominated data-driven journalist, engage in a compelling discussion of the significance of data literacy.
Data Culture & Collaboration
Fostering a data culture involves creating an environment where data-driven decision-making is encouraged and celebrated. This initiative requires fostering open communication channels, promoting transparency, and recognizing and rewarding data-driven successes. It also involves breaking down data silos and encouraging cross-functional collaboration to leverage insights from diverse perspectives.
In terms of investment, building this foundation for an in-house data analytics team requires allocating resources for hiring, training, and retaining skilled professionals. The investment includes salary and benefits packages, professional development programs, and infrastructure support for data analytics tools and technologies. Furthermore, it entails establishing data governance frameworks and ensuring data privacy and security measures.
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How to Build a Data Analytics Team FAQs
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Should I choose a centralized or decentralized data analytics team structure?
The choice between a centralized or decentralized team depends on your organizational needs. Centralized teams provide consistency, while decentralized units offer granular insights. A hybrid model can offer a balance of both.
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How do you build a good data team?
Building an analytics team involves:
– Defining clear data analytics team roles.
– Promoting data literacy.
– Fostering a culture of collaboration.
It also requires aligning the team’s responsibilities with organizational objectives. -
What does a data analytics team do?
A data analytics team extracts insights from data to improve business performance. They build data workflows, eliminate data silos, deliver accurate data on time, and make strategic decisions based on data analysis.
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How big should my data analytics team be?
The size of your data analytics team depends on your business size, goals, and data complexity. A small, focused team can be effective for some organizations, while others may require a large team with specialized roles.
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Do I have the right combination of data analytics team roles?
The right combination of roles in a data analytics team depends on your business needs. A well-rounded team typically includes data analysts, data engineers, data scientists, and data strategy consultants.
In-House Data Analytics Team or Outsourcing Data Analytics Experts?
Whether to build an in-house data analytics team or outsource data analytics experts depends mainly on your organization’s unique needs, resources, and strategic goals. Both approaches have their benefits and drawbacks:
In-House Data Analytics Team
Advantages:
- They deeply understand your company’s culture, goals, and data.
- They can provide real-time support and solutions tailored to your business needs.
- There’s better control and security over your data.
Disadvantages:
- Recruitment, training, and retention can be costly and time-consuming.
- The rapid evolution of technology may demand continuous upskilling.
Outsourcing Data Analytics Experts
Advantages:
- It gives you access to a pool of experts with broad skills and experience.
- It’s often more cost-effective, as it eliminates the expenses related to hiring, training, and retaining an in-house team.
- It allows you to focus on core business functions.
Disadvantages:
- There may be issues with data security and privacy.
- There could be a need for more control and understanding of your business specifics.
Hybrid Data Analytics Team
Advantages:
- You can leverage internal knowledge while accessing specialized skills externally.
- Efficient decision-making and cross-team consistency.
- Optimized cost-efficiency by combining in-house expertise with outsourced resources.
Disadvantages:
- Complexity in managing internal and external resources, which can be easily solved by outsourcing an experienced project manager.
Ultimately, it would be best to base your decision on factors such as the complexity and scale of your data analytics needs, the availability of skilled personnel, budget constraints, and your company’s data policy. A hybrid approach can also be a viable option, where some data analytics functions are handled in-house while others are outsourced.
One thing is sure — building a data analytics team is an investment that involves time, resources, and expertise. While the endeavor is rewarding, it can be a daunting task.
Bridge Skill Gaps with B EYE’s Data Analytics Experts
Outsourcing data analytics to B EYE’s experts allows businesses to reap the benefits of data-driven decision-making without the costly and time-consuming set-up phase. We’ve been instrumental in helping multinational corporations achieve their goals.
Why navigate the complex world of data analytics alone? Schedule a free 30-minute consultation with our experts at B EYE today, and let us empower your business with actionable data insights.
