Capitalising on data and analytics depends on building the right, cross-functional team of business and technical experts in your organisation. At BoomData we believe to deliver a successful data and analytics project team there are specific roles that need to be filled, each with their own unique responsibilities. With an ongoing data and analytics skills shortage and the increased complexities of data and analytics, getting this right can be the difference in creating a well-functioning team and successful project. Here we’ll share 6 tips in creating an effective data and analytics project team and assigning roles and responsibilities based on our learnings, that may make it easier.
1. Consider Experience
Data and analytics is about every business area working with IT, requiring a cross-functional business and technical team to be successful. A great place to start is consider the experience and skills of each team member and rate their capability to fulfill each role (high, medium & low). This will provide insight into the data and analytics maturity of your organisation, any skills gaps that need to be filled and level of external assistance required. We have identified eleven key roles for an effective data and analytics team that need to be filled as per the diagram below. Larger businesses may have an individual (or more than one person) fulfilling a role, while others may have one person fulfilling multiple roles. The key is to ensure everyone is clear on who is responsible and performing the duties of each role. If you’d like to understand more about the specific skills and competencies of each role then download our ebook.
2. Engagement Is Important
There are often cases where IT leaders can see opportunities in improving the data and analytics capability across their organisation without a clear driver from within the business. In this case, we would recommend IT try and identify a business sponsor and project lead first, educate them on why this is important and let them own the project as per the appropriate roles. This may involve case studies, proof of concepts or other education. If a Business Sponsor can’t be identified, it’s best to hold off any material project until it’s in place. Enterprises succeed with analytics when their executives support and evangelize it across the company. However, The Data Warehouse Institute in its 2023 State of Analytics Report says that a clear majority (61%) of organisations still lack appropriate strategic leadership for their analytics initiatives, so ensuring executive support is critical. As far as engagement with the business and data ownership, the business should own responsibility for the quality & completeness of the data, validating and approving its accuracy.
3. Measures Matter
Data and analytics requires a clear definition of the KPIs/Measures that drive measurement of performance of the business. The process to define what KPIs matter and what should be measured is an exercise by itself. This requires various aspects of the business to determine what’s important and they all need to be linked. Usually the CEO and/or Senior Management team will decide on key measures of performance for the organisation, which cascades down through the business. For example, a metric on customer retention at an organisational level, will then lead to related metrics for the Sales, Marketing & Customer Service teams that build up to this higher-level metric.
4. Set Up A Data & Analytics Steering Committee
Alongside the roles and responsibilities of the data and analytics project team should sit a Steering Committee for the project. Typically, this is a cross-functional business and technical team that includes some or all of the above roles. At a minimum, any data and analytics program should have a Business Sponsor, an IT Sponsor and a Project Leader in-house, so all these roles should be involved. There should be particular focus on alignment with business priorities and examples where value is being delivered. The Steering Committee plays an important function in helping to keep senior management engaged in the program.
5. Consider Data & Analytics BAU Support
Once a project has been delivered and is operational, an appropriate support model will need to be in place. Regular support requirements include data missing, data not presenting as the user expects, issues with data refreshes and jobs failing, connectivity and performance issues and change requests. We’ve found that having a business lead, a data lead and a reporting lead trained in the specific environment works best. It may take some investment up-front, but support people that know your specific environment will be much quicker at solving issues as every data and analytics environment is unique.
6. Data & Analytics Is A Journey
Remember, data and analytics is a journey, not a one-off project. Ensure the team is structured in a way that it can easily adapt to changing business needs. While some of the KPIs relevant to a business may be consistent over time, the breadth, granularity or detail of information required can vary based on leadership and their approach to data. In turn, this will drive change in the data and analytics environment. Every time a system changes, this will require some level of change as well. The data and analytics Steering Committee should meet monthly, reporting on progress and agreeing on short and long-term plans, this will help to ensure the data and analytics capability stays current.
To better understand the skills and competencies required for the eleven key cross-functional roles we believe are required to deliver a successful data and analytics program, you can download our how-to-guide ebook on Creating An Effective Data & Analytics Project Team.
