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How data teams can accelerate business value through collaboration

Implementing data collaboration between data creators and data users can be challenging.

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Data lies at the center of our reasoning and decision-making. The world is built upon data, and business leaders need good, accurate data to make informed decisions and find solutions to problems. To increase data accuracy and consistency, organizations need to focus on nurturing teamwork and increasing data collaboration between data teams and data users.

This relationship between people who analyze the data and those who use it plays a crucial role in an organization’s decision-making and strategy. According to one study, more than 65% of data science projects are unable to yield the desired results because of failure to facilitate data collaboration between data science teams and data users.

Data collaboration involves creating communication channels between different members within an organization to ensure that accurate, updated, and consistent data is available to decision-makers.

In this post, we’ll talk about some of the ways businesses can improve data collaboration between data teams and data users. But first, let’s take a look at why companies should consider generating high-quality data and ensure free flow of data within the organization.

Generating quality data

Achieving data consistency is vital if you want to build a successful business. Clean and consistent data about your customers or your business landscape can help you make informed business decisions, whereas inconsistent data can lead to business decisions that could jeopardize the company.

Data consistency in the workplace requires a collaborative working environment that nurtures teamwork among all members of the organization. Good teamwork enables data teams to collaborate on data collection and analysis tasks, while at the same time establishing a single source of truth for the entire organization.

An effective way to ensure data accuracy is by facilitating data teams to work together with the employees who use the data. By eliminating the communication challenges between those analyzing the data and those actually using it to do their jobs better, you’ll be able to promote free flow of data in your organization. 

As a result, consistent and accurate data will be available to decision makers in a timely manner, helping you generate additional value for your business. 

Organizing data flow

If you want to promote teamwork in your data team, start by assigning roles to each team member. These data-specific roles might include:

  • Data scientists
  • Data analysts
  • BI experts
  • Data engineers

After you’ve organized your data team into different roles, you’ll have to define specific responsibilities for each of these roles. This involves informing each member about their individual tasks and roles, as well as those of all other members in the team. This way, everyone on the team knows who’s responsible for what.

When everyone understands what they are expected to do and how each person’s contribution affects the final objective, they’ll be able to form informal networks and alliances. This will help them overcome communication challenges and promote effective collaborative working.

You can encourage the free data flow in your organization by identifying the team member who:

  • Is best suited to source the data.
  • Is responsible for manipulating and organizing the data.
  • Can prepare the data for analysis by removing incorrect or inaccurate parts of the data.
  • Has to present the data.
  • Can manage the entire project.

This way, you can create a collaborative environment in the organization where everyone is able to make use of advanced analytics.

Improving data collaboration

To facilitate teamwork and improve data collaboration, you need to make sure that everyone is able to share their feedback and opinion.

1. Understand the bigger picture

When you receive a data request, you should start by taking into account all the necessary elements of the final objective. Don’t create dashboards right away and instead focus on maximizing data potential.

By taking time to understand the request, you’ll be able to clearly take note of and communicate the specific roles and responsibilities to each team member. 

This will also help each member better understand their own role in the big picture. As a result, your data team will be better equipped to deliver reliable and consistent data.

2. Avoid making assumptions

Relying on assumptions instead of striving for accurate, complete, and consistent data can lead people to make uninformed business decisions. You can ensure data accuracy by being open-minded when people share ideas during a team meeting and asking questions. 

Taking a fresh look at old ideas is equally important. Even though each situation is different, there’s always an opportunity to learn from past mistakes or reusing tested strategies. If your marketing team is, for example, using data to make content strategy decisions, then it’s important to revisit all assumptions with fresh analyses periodically.

In addition to this, it also helps to stay informed about the requirements and workflows of the data users. In other words, you need to allow the data to inform your direction. This is a great way to generate new ideas and promote additional value for your business.

3. Break down objectives

After you’ve identified the desired objective of the data request, break down the main goal into smaller goals. You should be able to visualize these smaller goals within the overall picture. This will help the team focus on the finer details of the project.

So, let’s say your overall goal is to increase sales. In this case, your smaller goals can be generating more leads for your business and optimizing your email marketing campaigns. Next, you can set measurable factors to each of these smaller goals. 

In this scenario, you can measure the increase in the number of leads you’re generating and the number of people acting on your conversion goal.

4. Understand how data is used

A great way to facilitate or improve the communication between the data team and data users is by asking the data users to provide details about their normal workday. This information can help the data team better understand how to use the data in an effective way.

This kind of collaborative relationship can be particularly beneficial for improving person-to-person and professional communication between the data team and data users. 

Data analysts and business intelligence professionals can work with employees on the field to design and develop apps.

5. Embrace data democracy

Another way you can improve data collaboration among data team members is by giving them access to the data. This will encourage them to figure out how they can use the data to improve their own performance and create more value for the business. 

As the data and insights are shared with different people with varying knowledge backgrounds and skills, including data users, the data scientists should ensure that the findings are explained to each person in a clear way.

If you want to ensure that everyone is on the same page, you should first collect some insights from your audience. Here are some questions worth asking:

  • What information and insights do they need to know?
  • What is the simplest way to convey the message?
  • What is the medium of communication that suits them best?

Business intelligence and data analytics tools help boost the interest and confidence of data users and enable them to use the data in ways that work best for them. As a result, people are able to generate new ideas and improve performance quickly.


Data collaboration between data teams and data users can help businesses drive success.

Implementing data collaboration between data creators and data users can be challenging. By generating quality data, organizing data flow, and improving data collaboration across the organization, companies can facilitate teamwork in the workplace, enhance decision-making processes, and increase business value.

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