I should start this post by stating that I'm commingling BI and analytics together for the purpose of this post and using a generalized "Analytics" to represent the roles that support data and the use of data. A future post will be dedicated to all of the ways an organization can set up BI teams, analytics teams, and data engineering teams.
When we talk about analytics at an organization, two operating models are usually at the forefront of preferences or strategic options. A centralized analytics model and a self-service analytics model. The nature of the approach is pretty self-explanatory in their naming convention. Centralized analytics is defined by having 1 team or groups of teams all reporting to a centralized function while dedicated to servicing the business. Self-service or decentralized analytics is where each business team runs their own analytics teams dedicated to supporting that business department exclusively.
Before we dive further, let's take a step back and understand that this operating model is tied to or limited by the business unit that it supports. Going beyond the different department or business teams of that business unit, it's important to understand that an analytics operating model can be replicated across different business units but its implementation and subsequent operation should not span different business units. For example, a large multinational organization operates various business units from selling cars, houses, and consumer goods. Under each of those business units, an analytics operating model should be established and supported by appropriate technological and people resources.
Each of these business units should the have tools, individuals, and technologies to fully support analytics for that business unit. One single analytics stack in support of multiple business units would quickly cripple the analytics function as those teams would either be too small to support demand and deliver insights on time. Or they would be too big and be weighed down by the sheer bureaucracy naturally adopted from multiple large teams.
An analytics stack I define as the tools, teams, and technologies that support analytics functions and are composed of a data platform (data warehouse), a team of data/analytics engineers, BI engineers and a BI platform, and data analysts.
In a centralized function, the entire analytics stack sits under one individual - usually a Vice-President of Data & Analytics. This individual works with business teams to understand their needs and works to deliver data products or data solutions that provide said business teams with the data or reporting they need. As an example under this model, a request will be made to the front-end data team (BI engineers or data analysts), the data and analytics engineers will develop a new table and data model, and the BI engineers and data analysts will further refine the model while working with business users to ensure their needs and requirements are met. Granted, this is a gross oversimplification but you understand the point. All of the above happens "in-house". The business's only ability is to use the data or reporting solution that will be provided to them. Even for ad-hoc data requests, those go to the analysts to write a query and provide the data (usually in excel).
Under the centralized model, the business will face a bottleneck in the access of data and information. Both will be slow as requests are prioritized and acted upon by the corresponding data team. Not necessarily a bad thing if you ask me, especially if the business is too reactionary versus proactive. In the centralized model, the data teams focus on building the right thing for the right insights. The right solution is always more effective than a dozen misguided attempts at the solution that ultimately get tossed away. Faced with finite resources, the business is forced to make decisions on what it truly values and needs to grow.
On the other hand, in a decentralized function, the flow of information is correlated to the amount of analysts on the business teams. In the decentralized or democratic model, the top (or front) end of the analytics stack sits with the business and reports directly to the business. Analysts are empowered to act as data experts, BI engineers, report developers, and anything in between that involves the retrieval and use of data. Business analysts work with their heads of business to identify data and reporting needs building data models, and reports that fit the requirements. In this scenario, speed-to-value is prioritized as requests can be turned around quickly as there is no reliance on an engineering team to build something for them. And if there is a need for more data, what's wrong with sticking a few excel files into a BI tool and maintaining that on a daily and weekly basis?
The decentralized or democratic analytics operating model gets data into the hands of the business faster but at the cost of maintaining manually-operated reports and data solutions. Business teams are not set up to maintain technical engineering solutions. Lack of knowledge into best practices and standards leads to things that "work" in the sense that the output is trusted but don't work as soon as the individual who's built it is gone. Business created and owned solutions last as long as the person who put it together. There are countless examples where the business maintains something but has no idea what it actually is and therefore can't make changes to it.
I'm quite obviously showing my bias at this point in time. But I would make the argument that is it worth discussing the benefits of an operating model that only has... one benefit? Organizations that establish a decentralized function in their analytics operating model will get fast results that fizzle quickly. One way to address this is to establish a hybrid model. Decentralized centralized analytics.
Decentralized centralized analytics has the greatest chance of finding the balance to deliver value over time at fastest speed. In a decentralized centralized analytics operating model, analytics functions still sit under a central authority but front-end members of the data team (BI engineers and/or data analysts) sit with the business and work exclusively with that business team or function. This is arguably more commonly known as the hub-and-spoke approach or the business partners approach where members of the analytics function serve as direct partners in support of business needs.
Under this operating model, the business knows the individual they need to work with for reporting and data needs. Sitting within the team, the data analyst or business engineer becomes knowledgable in data that business team generates (and it's preferences for reporting). Being part of the centralized analytics function, the data analyst or business engineer is empowered to build data and reporting solutions that are efficient, scalable, and follow standards and best practices. The centralized function teaches them those standards and best practices as they report to their analytics leaders and that knowledge is leveraged in their output for the business team they are attached to. This way, the business receives a fast response to its reporting and data needs while governance and scalability are addressed.
In the prior paragraph, I say "sitting with the team" which I mean quite literally. In the decentralized centralized analytics operating model, the BI engineers and data analysts desk's should be located with the business team in the office - in fact, there should be nothing that should prevent them from not being considered a member of the team. They should act as a team. Talk to each other in person to solve data and reporting issues. Understand what each is working on. The only thing that would identify the engineers and analysts as being different are their weekly meetings with their managers and governance-focused ceremonies (this is important and a future blog post!).
Let's quickly summarize. Self-service/decentralized/democratized analytics operating models prioritize speed-to-value which fizzles over time. Centralized analytics operating models prioritize value which takes a while to actualize. Decentralized centralized analytics aims to prioritize both through analytics partners assigned to specific business functions.