Who are the key stakeholders in a data analysis project for BI?

In a Business Intelligence (BI) data analysis project, key stakeholders typically include business executives who make strategic decisions based on the insights, IT professionals responsible for data governance and system integration, data analysts and scientists who extract and analyze data, end-users who require actionable intelligence for daily operations, and project managers who oversee the project timeline and deliverables. Each has a vested interest in the project’s success—executives in the return on investment, IT in the technical implementation, analysts in the accuracy of insights, users in the utility of the information, and managers in the overall project performance.

Stakeholders play a vital role in leveraging data analysis outcomes to drive their organizational objectives, strategies, and decision-making. They are responsible for identifying the business challenges and opportunities that necessitate data analysis initiatives. Hence, comprehending their requirements, anticipations, and inclinations becomes crucial, alongside delivering pertinent, precise, and executable data analysis deliverables.

Executives, business analysts, IT workers, and end users are some of the most important people in a BI data analysis project. Executives set the strategic direction, analysts describe the needs, IT makes sure that the data is integrated and of good quality and end users get insights they can use.

In a data analysis project, it is crucial to group the stakeholders into a service identifiable group like Developers, Executives, PMs, and end users. This is important in initiation to deployment and maintenance phase. Each users assigned to these roles should have a clear understanding of their role and how it is vital for the success of the project. These users include data engineers, data analyst, data visualization designer, end users and executive sponsor. All these users need to be aligned with the project goal, validate data facts and processes to minimize waste.

In my experience working on various BI projects, it really boils down to what the Business User wants to see on their dashboard. The Data Analyst and Engineers may offer their professional advise, while the DBA or even the Data Steward may come with their suggestions. But in the end, it boils down to how the solution addresses the metrics needed by the users and helps to facilitate decisions. By Business User, this could be the departmental Heads, departmental members, the The CEO and other Board Members. I have learnt that you can suggest and advise, but never assume for the Business User.

While data understanding is important for data analyst, equally important is the communication between the data analyst and stakeholders. Understanding the requirements is the first step in data analytics project - mismatch between the stakeholder expectation and data analyst's understanding will lead to more reworks and less efficiency.

Data analysts are the driving force behind data analysis and our role encompasses a wide range of tasks, including data gathering, cleansing, manipulation, exploration, modeling, and visualization. We are entrusted with the responsibility of employing suitable methodologies, tools, and techniques to extract insights and address pertinent business queries.

In my experience data engineers provide technical support and guidance to data analysts and business users. It is imperative to possess the necessary expertise, tools, and resources to effectively manage the data infrastructure and pipelines. When collaborating with data engineers, clear and consistent specifications, requirements, and expectations should be utilized, and any encountered issues or challenges should be promptly reported for resolution.

One thing I found helpful is the Project managers are integral part to the smooth execution of data analysis projects. They play the role of planning, organizing, and overseeing the project's various aspects from defining project objectives, scope, timeline, budget, and deliverables. Additionally, they manage project teams, stakeholders, risks, and issues.

For the success of any data analytics projects, all these stakeholders needs to work in coordination, everyone should have at-least basic understanding of data and technical jargons. Projects delivered in silos will lead to nowhere and just adds up to cost and organizational inefficiencies.

The importance of constant communication among stakeholders cannot be overemphasized. Communication helps ensure that stakeholders agree and are up to date on any project decisions, which in the long run will help prevent the loss of resources.