/ Drafting a data-driven dream team.

It’s football season and that time of year when there is a steady buzz about fantasy football at the water cooler, with everyone hoping they have drafted a winning team.

Everyone knows it’s critical to research player rankings, projections, and sleepers. Today’s top executives face a similar problem when it comes to drafting a data-driven dream team. How do you pick the right people, platforms, and technology to create a winning culture? According to McKinsey, data-driven companies are 23 times more likely to win new customers. While the platform and technology are important, so are the people. With competition intensifying year after year, you’ll need these 4 key roles for an impressive starting lineup. It’s important to note that each of these roles may vary across companies by size, organization, and available skill sets.

 

1. Champion

Helps users navigate and adopt the tool and supports evolving business and data needs.

Getting users to adopt a new technology or tool can be challenging. Companies should designate a project champion early on in the engagement to leverage business perspective to drive results with the initiative on an ongoing basis.

This person’s role isn’t strictly technical, and in fact they are more of a jack-of-all-trades. The project champion plays a vital role in managing a healthy and sustainable data playground by instilling best practices across business data and governance, and ultimately helps increase the value of the platform to the organization. Day-to-day responsibilities may include guiding new users to training and resources, keeping initiatives on schedule, and ensuring user access and security.

In today’s modern analytics world, the project champion need not be a data scientist. But what should you look for in an analytics champion? Stick to strong business acumen, communication skills, the ability to collaborate, and a reputation for solving problems. 

 

2. Executive Sponsor

Secures the budget, sets the vision and key business requirements (KBRs), and advocates adoption.

Business analytics projects must be properly scoped and funded up front, or they risk getting off track and failing quickly. According to The Project Management Institute, having executive sponsors who are actively engaged is the leading factor in project success, leading through the four major stages of initiating, planning, executing, and closing of any project.

The executive sponsor is instrumental to an enterprise analytics project, as he or she defines the critical business questions that the organization is trying to answer. Because multi-disciplinary teams (finance, sales, marketing, IT, and more) are often required for big data projects, it is wise to have the most senior sponsor possible (typically a Director, VP, or C-suite executive) as this allows for a wide view across the entire organization. The sponsor is well-respected, can deploy a team, and creates a sense of urgency for the initiative among other priorities.

 

3. Project Manager

Coordinates initial deployment and resources.

The project lead’s role is to bring the team together, drive the project forward, and continually ensure alignment to the executive sponsor’s KBRs, such as acquiring and converting more customers, increasing marketing ROI, or increasing profitability. The project lead should also determine when additional players are needed from other departments to help define metrics that support the KBRs and secure those resources.

The project lead’s ability to cultivate teamwork among stakeholders and other resources is key. Each person in the organization can contribute to the success of the solution, and collaborating over insights and asking the right questions will unlock the most value from the tool. In fact, according to a recent report by Forbes Insights, “Improving how a company fosters a culture and mindset that rewards the use of data experimentation can help a data and analytics initiative gain momentum and impact.”

 

4. Data Specialist

Ensures data availability and preparation.

To run the right plays, you need to use all the data and information you have at your disposal. While most business users can usually articulate what they want to measure, they generally need help with the step-by-step process of extracting data from a familiar format and visualizing it in the selected platform. The data specialist solves this by bringing to bear technical skills and a working knowledge of the data sources that your organization is going to connect to, whether they are on-premise systems or applications in the cloud.

A “data dream team” takes time to assemble, but it’s worth the effort. In short, it gives you a competitive edge, and a winning formula.

Check back soon for part II of this series, as we explore the long-term value of a champion—or MajorDomo, as they’re called on our team.

To learn more about how Domo caters to the business user, primarily by lowering the complexity barrier to analytics (and data) and helping users collaborate and apply analytics to their business, see Domo.com.