How to Structure a Data Organization
According to BARC, over 40% of companies worldwide use big data analytics, and many are now enjoying various benefits. Data is one of the most valuable assets in an organization and has become the core of enterprise strategy, focus, and investment. As more and more organizations embrace data-driven decision-making, the need to structure these organizations becomes paramount.
From my experience working in the field of data, in this blog, I'm going to share the three popular models that companies use to structure their data teams and partner them with the business and get the most value out of them.
The three popular models to structure data teams are:
- Centralized Model
- Deployed Model
- Defused Model
Let's explore each for their use, pros, and cons.
In it, a centralized group handles all the requests from all the different teams. These requests are arranged into a single queue and managed by the data team using different methodologies based on priorities.
Pros: This model is a traditional way of organizing teams and requires smaller data teams, thus fewer resources.
Cons: In this model, the data team does not engage directly in business processes and is left disconnected where the folks do what has been asked. Another problem in this model is “Project Selection.” Prioritizing one task over another upsets some teams as they feel their work never bubbles to the top, and this discrepancy cascades into operational inefficiencies.
In this model, each team has its dedicated analyst or data science group for any specific request.
Pros: It is a quicker and more efficient way of structuring data organizations as the data team sits with the core business teams like sales, marketing, finance, etc., to understand a business problem. It makes the data team more accountable and helps develop domain expertise.
Cons: Unlike a centralized model requiring smaller teams, many people are needed for the deployed model as separate units serve various departments. Thus, the deployed model is resource-intensive, and not many companies can staff large data teams.
There is no central data team in this model, and the business groups have fully embedded independent analysts. Thus, there is essentially no centralized model and no representation at the C level.
Pros: This model gives business units full autonomy without waiting for instructions and permissions from anybody.
Cons: Analysts are bound to the business unit and often cannot switch paths or upskill in their data careers. Another challenge in this model is the "Lack of Structure" as there is no central data team. Instead, each business unit will find their own desired tool or way of developing their analysis and figuring out the results leading to conflicting results, the overlap of tools, inconsistent data interpretation, and whatnot.
The three models mentioned above are non-exhaustive. There are numerous ways how data organizations evolve.
Often, the change starts from the grassroots, where a business unit identifies the need for a data role and hires somebody or trains one of the internal members for the data job who become de-facto analysts. Gradually, as managers and directors realize the value data delivers, an entire team of Data Engineers, Data Scientists, Data Analysts enters the realm. This eventually requires a senior member who'd organize the data team and give it a formal structure.
Data organizations empower businesses by processing raw information into meaningful and valuable insights.
Now that you know how the team may be structured that you're working on, let's go into a deeper look at working with customers. Continue this journey of learning all things data. Head over to freethedataacademy.com/yt to see our entire catalog and sign up for a seven-day free trial. So you can start learning today to elevate your career tomorrow.