3 min readApr 3, 2024


The Top 5 Pains of a BI/Analytics Manager

Data Analytics Series

1) Business Strategy

A business strategy is supposed to define an organization’s mission, vision, values, direction, purpose, goals, objectives, respectively the roadmap, alternatives, capabilities considered to achieve them. All this information is needed by the BI manager to sketch the BI strategy needed to support the business strategy.

Without them, the BI manager must extrapolate, and one thing is to base one’s decisions on a clearly stated and communicated business strategy, and another thing to work with vague declarations full of uncertainty. In the latter sense, it’s like attempting to build castles into thin air and expecting to have a solid foundation. It may work as many BI requirements are common across organizations, but it can also become a disaster.

2) BI/Data Strategy

Organizations usually differentiate between the BI and the data Strategy because different driving forces and needs are involved, even if there are common goals, needs and opportunities that must be considered from both perspectives. When there’s no data strategy available, the BI manager is either forced to address thus many data-related topics (e.g. data culture, data quality, metadata management, data governance), or ignore them with all consequences deriving from this.

A BI strategy is an extension of the business, data and IT strategies into the BI knowledge areas. Unfortunately, few organizations give it the required attention. Besides the fact that the BI strategy breaks down the business strategy from its perspective, it also adds its own goals and objectives which are ideally aligned with the ones from the other strategies.

3) Data Culture

Data culture is “the collective beliefs, values, behaviors, and practices of an organization’s employees in harnessing the value of data for decision-making, operations, or insight”. Therefore, data culture is an enabler which, when the many aspects are addressed adequately, can have a multiplier effect for the BI strategy and its execution. Conversely, when basic data culture assumptions and requirements aren’t addressed, the interrelated issues resulting from this can prove to be a barrier for the BI projects, operations and strategy.

As mentioned before, an organization’s (data) culture is created, managed, nourished, and destroyed through leadership. If the other leaders aren’t playing along, each challenge related to data culture and BI will become a concern for the BI manager.

4) Managing Expectations

A business has great expectations from the investments in its BI infrastructure, especially when the vendors promise competitive advantage, real-time access to data and insights, self-service capabilities, etc. Even if these promises are achievable, they represent a potential that needs to be harnessed and there are several premises that need to be addressed continuously.

Some BI strategies and/or projects address these expectations from the beginning, though there are many organizations that ignore or don’t give them the required importance. Unfortunately, these expectations (re)surface when people start using the infrastructure and this can easily become an acceptance issue. It’s the BI manager’s responsibility to ensure expectations are managed accordingly.

5) Building the Right BI Architecture

For the BI architecture the main driving forces are the shifts in technologies from single servers to distributed environments, from relational tables and data warehouses to delta tables and delta lakes built with the data mesh’s principles and product-orientation in mind, which increase the overall complexity considerably. Vendors and data professionals’ vision of how the architectures of the future will look like still has major milestones and challenges to surpass.

Therefore, organizations are forced to explore the new architectures and the opportunities they bring, however this involves a considerable effort, skilled resources, and more iterations. Conversely, ignoring these trends might prove to be an opportunity lost and eventually duplicated effort on the long term.

Originally published at Written: Apr-2024.




IT professional/blogger with more than 24 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management