When a company is small, having a fully centralized data team may not be an issue. As you grow, however, problems can start to arise. You have one structure that’s supporting all of your business units, and they may not be able to dedicate sufficient time and resources to individual business units. This can lead to delays in surfacing important insights and decisions made on old or inaccurate data. Projects that have time-sensitive demands may not be prioritized properly, depending on the demand on the centralized data team. Furthermore, because the data specialists are not working directly within a particular business unit, they may not fully understand its unique needs and requirements.

If these problems sound familiar, it may be time to consider decentralizing your data team and introducing a low-code data solution for easier management.

Table of Contents

  1. Data Team Structures
  2. Benefits of Decentralization
  3. How Low Code Benefits Decentralized Data Teams
  4. The Role of Extract, Transform, Load Tools for Data Teams
  5. The Advantages of Using Integrate.io for Low-code Data Pipelines

Data Team Structures

Data team structures can be categorized based on their degree of centralization, with three main approaches:

  • Completely Decentralized: Decentralized data teams are contained within the business units that they serve. They lack a Chief Data Officer unit, so the business units are fully independent. 
  • Hybrid: These teams mix centralized and decentralized, and are typically set up as federated with the Chief Data Officer unit in a facilitator role. Most of the data team resources are embedded in the business units and hold responsibility for those functions.
  • Fully Centralized: The CDO unit is mostly or fully self-contained, and provides an overarching strategy and guidance for business units. You generally have a full-stack of data roles in a single unit with centralization.

The right data team structure for your organization depends on many factors, from your available technical resources to the scale of your analytics initiatives. Your approach will often change over time based on the current needs of your organization, so flexible frameworks for future growth and unexpected situations can prove useful.

Benefits of Decentralization

Hybrid and decentralized data teams address organizational concerns by moving data teams within the business units. Eliminating administrative overhead and the back-and-forth between the business unit and the data team can significantly improve the way your organization uses data. These dedicated teams also gain valuable domain knowledge that allows them to find new ways for the business unit to use data and to fully understand what’s most important for that part of the organization.

Since each business unit has its own data team, they don’t have to worry about their projects taking a lower priority than another unit. They have full visibility into the demands of their data team and can plan accordingly.


Benefits of Hybrid Data Teams

Sometimes a fully decentralized data structure may not serve the needs of the business. You could end up with data silos and increased infrastructure complexity, along with a lack of overarching data culture and strategy.

The hybrid approach seeks to leverage the benefits of both decentralization and centralization while minimizing the drawbacks. The Chief Data Officer can handle matters such as the data strategy, standardization, and governance while allowing business units to have a level of independent decision-making.

The data teams gain the domain-specific knowledge that helps them better meet the needs of each business unit while having centralized resources in place for the economy of scale.

If you choose a structure that is on the decentralized side of the scale, one way that you can make the most of your data team resources is by leveraging low code solutions.

How Low Code Benefits Decentralized Data Teams

Since decentralized data teams act independently while embedded in business units, they may not want to wait for IT developers to build data pipelines and other essential tools for data analytics. Since development resources are limited, it makes sense to offload some of the work to the business analysis and data scientists.

Low code solutions make it possible for these users to create data pipelines without having a full development background. These tools are user-friendly and provide many ways to empower data teams through greater productivity. They typically have easy learning curves and provide a streamlined development experience.

When data teams need new data pipelines, they can quickly set everything up to work with new data sources and fulfill ad hoc analysis requirements. The business unit’s time to insight becomes much faster as a result.

Another advantage of low code for decentralized data teams is an increase in overall agility. You won’t be paying an opportunity cost by waiting around for the IT development team to fulfill your request. Instead, you’re able to quickly act on new projects, technology, and shifts in the market.

Your data team does need some level of coding background to get the most out of low-code tools, although no-code tools also exist (and some products offer both types).


The Role of Extract, Transform, Load Tools for Data Teams

Extract, Transform, Load (ETL) tools make it much easier and more efficient to work with the variety of data sources and volumes that data teams handle. They automate pulling data from the source, taking it through necessary transformations, and then loading it into a data store. Here’s how this technology works:

  • Extract: You can set up a schedule for extracting data from its original sources and pulling it through the ETL tool. Eliminating the need to manually extract this data means that your data team is not spending time on time-consuming, monotonous tasks. Instead, they can focus on how they’re going to use that data to drive insights for the business unit they’re embedded in.
  • Transform: The extracted data then goes through a transformation step. You can use this part of the tool to cleanse data before it moves to your data warehouse, standardize it so that it’s ready to work with as soon as it reaches the data store, limit it so you can focus on the data that is most important for a project, join tables together, sort columns in the way that you need, and removes or masks sensitive data so that you stay in compliance with all regulations.
  • Load: The transformed data is then automatically loaded into your data warehouse or data lake.

The Advantages of Using Integrate.io for Low-code Data Pipelines


Integrate.io is a data integration platform that provides low code and no-code ETL solutions for your decentralized data teams. Using a platform like Integrate.io helps to keep the CIO happy, as it provides excellent data security, a streamlined process, and standardization while allowing the decentralized team to have control and be responsive to the business unit’s data requirements.

Here are some ways that Integrate.io’s low code data pipelines empower your decentralized data teams:

  • Out-of-the-box data transformations
  • Support for many data integration use cases
  • Visual interface for building data pipelines
  • API access, rich expression language, and webhooks for more complex requirements
  • Monitor your data pipelines
  • Integrations with over 100 popular data stores and SaaS applications
  • Scalability

Ready to get more out of a decentralized data team structure? Schedule a call with our support staff to learn more about Integrate.io and how it can help your business.