Building a Competent Analytics Team: Core Skills and Expertise

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Written By Patrick Williams

An ardent advocate for the power of data in crafting business strategy, Patrick has designed the Digital Analytics Maturity Model, a framework that has been widely adopted by organizations seeking to leverage data for competitive advantage.

Building a competent analytics team is crucial for organizations looking to leverage advanced analytics and data-driven decision-making. To build a successful team, there are essential core skills and expertise that should be considered.

According to various sources, including Gartner and McKinsey, the process of building an analytics team can be broken down into several key steps, including defining the data vision and strategy, structuring the analytics organization, defining the roles and skills, recruiting and assessing skills, developing and democratizing analytics skills, and retaining analytics talent. These steps provide a framework for organizations to follow in order to build a competent analytics team.

It is important to have a clear data vision and strategy aligned with the organization’s mission and goals. The analytics organization should be structured in a way that maximizes the team’s potential and integrates seamlessly with the rest of the company. Defining the roles and skills required for the team is essential for recruiting the right talent.

The team should have a diverse set of technical skills, including proficiency in data analysis tools, business intelligence tools, process modeling and analysis, requirements management, agile methodologies, system analysis, prototyping and wireframing, and knowledge of ERP and CRM systems. Additionally, non-technical skills such as problem-solving, critical thinking, communication and interpersonal skills, negotiation and cost-benefit analysis, and decision-making skills are also important for a successful analytics team.

By following these steps and focusing on the core skills and expertise required, organizations can build a competent analytics team that can effectively drive business growth and optimize decision-making.

Defining the Data Vision and Strategy

The first step in building an analytics team is defining the data vision and strategy. This crucial step involves aligning data and analytics with the organization’s mission and goals, and deciding how data will be used as a competitive advantage. By gathering C-suite executives to discuss high-level strategic questions, such as the corporate mission, vision, competitive strategy, and the role of advanced analytics, organizations can establish a strong foundation for their data vision and strategy.

A key component of this step is creating a data roadmap. This roadmap allows organizations to document their current state, desired future state, and the steps needed to bridge the gap. It serves as a guide for aligning the data strategy with the broader business strategy and performance. Additionally, a well-defined data vision and strategy can attract top-notch analytics professionals, as it demonstrates the organization’s commitment to building robust data capabilities.

With a clear data vision and strategy in place, organizations can lay the groundwork for their analytics team and ensure that all efforts are directed towards common goals. By defining the data vision and strategy, organizations can set themselves up for success in leveraging data as a strategic asset and driving data-driven decision-making throughout the organization.

Structuring the Analytics Organization

When building an analytics team, structuring the analytics organization is a critical step. There are different organizational structures to consider, such as centralized, decentralized, or a hybrid model. According to experts like McKinsey, a centralized team structure has proven to be the most successful.

In a centralized structure, the analytics team reports directly to a C-suite executive, such as the Chief Data Officer or Chief Analytics Officer. This setup provides the team with autonomy and the ability to apply analytics and data science tools across the entire organization. It also sends a clear message that the company recognizes data as a highly strategic activity.

Structuring the analytics organization effectively involves more than just reporting lines. It also requires creating processes and workflows that enable seamless collaboration between team members and other departments. By integrating the analytics team with other key departments, organizations can ensure that insights and recommendations are effectively communicated and utilized throughout the company.

By adopting a centralized structure and establishing efficient processes, organizations can maximize the potential of their analytics investments and create a scalable structure that aligns with their data roadmap and overall business strategy. A well-structured analytics organization sets the stage for success and empowers the team to deliver valuable insights that drive informed decision-making and business growth.

Patrick Williams