How To Manage Data Requests

Data requests are questions that employees have about data that exists in the organization or about new data isn't being collected yet. Traditionally, data teams will take data requests through an intake form or a Slack channel where employees can ask for the request.
Last updated
April 11, 2024

The past year has changed the way we live and work dramatically. Remote work became the default and teams started to work asynchronously. During this shift, many departments realized that their processes were built for in-person communication and quickly adapted to the new normal. Zoom meetings replaced meeting rooms, Slack replaces ad hoc hallway conversations, and tools like Miro emerged as a way to collaborate while everyone is in a different place. On a team level, specific workflow tools like Figma, Notion and Google Sheets helped teams maintain some transparency into how different team members were working. In this new remote world, teams that lean into collaboration and knowledge sharing move faster than those that store important information in undocumented silos.

This is true of all teams. Without processes for remote work, knowledge is lost, forgotten and is difficult to find. This is especially true of data teams, whose role is to communicate important information with the rest of the organization. Without the right processes and tools for answering questions, servicing tickets and creating a culture that is built around remote work, data teams could struggle to maintain efficiency as they support the organization. Traditionally, data teams manage their communication with external stakeholders through a data requests process, but this process has yet to be optimized for remote work.

What are data requests?

Data requests are questions that employees have about data that exists in the organization or about new data isn't being collected yet. Traditionally, data teams will take data requests through an intake form or a Slack channel where employees can ask for the request. Some of the requests are unique and difficult questions, which require the full attention of the data team. On the other hand, some data requests are repetitive and low priority, which doesn’t require much effort from the data team.

Data requests help employees answer questions that they may have about the effectiveness of a particular feature or pricing plan. Without a transparent, efficient process for answering these questions, data teams risk employees trying to answer certain questions without data or trying to make the decision with the wrong data. Most data teams use an intake form and a Jira board to prioritize data requests, which allows them to clarify the intent behind the data request before prioritizing its importance on the roadmap.

Why teams should take data requests?

Not all teams take data requests, but we strongly believe that in a remote-first work environment, data requests are a great way to get employees on the same page. Many teams who don’t take data requests do so because they are worried about getting overwhelmed with the amount of data questions that come up and don’t want to deal with the questions that may be a low priority. Data and analytics teams already manage a lot of different tasks, and data requests can feel like a repetitive process for most teams. The three primary reasons why teams should consider taking data requests are to encourage a culture of curiosity, to enhance the perceived value of the data team and to improve decision making across the company.

Curiosity can be helpful for multiple types of decisions making but particularly when operations, marketing or product teams are looking to come up with a new solution to an old problem. Organizations that encourage curiosity in their decisions can create more innovative solutions, which differentiate them from the competition. Secondly, data teams should consider taking data requests to increase the perceived value of the data team across the organization. By interacting with stakeholders regularly, data teams can start to position themselves as a key resource for the organization. Data teams that choose to avoid data requests can sometimes feel disconnected from the projects and goals of the rest of the company. By taking requests, data teams signal that they are ready to drive decision-making wherever needed. Lastly, data teams should consider taking data requests to increase data literacy across the company. By learning how and which questions to ask, employees can start to learn how to make smarter decisions. Data teams can easily teach employees the important ways of working with data and its nuances by taking data requests and interacting with the rest of the team.

Treating data like a product

The way we see it, data requests are the equivalent of tickets for a customer support team. By treating data requests as part of their operations, data teams can easily answer questions from business users. Customer support teams use a mix of tools to support their tickets and make sure that all customers are given quick answers and abilities to self-service. For a long time, data teams have adapted some of the self-service practices that support teams have used, but haven’t had developed a universal way to deal with data requests. If data teams want to treat the data like a product, servicing the requests is an important part of the process.

How to manage these requests  

Similar to customer support tickets, it’s important to make sure that customers who are requesting data have the right expectations around results. This may mean that employees know that there is going to be some time between asking a question and getting an answer as well as a variance of preciseness based on the urgency and quality of the data requested. Explaining the nuance in the data might be off-putting to business users, but is an important thing to manage as the data request is coming in.

In addition, team members should be encouraged to look at completed tickets, BI tools and data knowledge base tools to try to figure out if the answer to their question has been answered in the past. We think about this the same way as intercom works with the self-service knowledge base. Customers are encouraged to try to find the answer to their question and when they can’t, they turn to the customer support team. Similarly, the data team should create a way for employees to search through the data knowledge across the company and try to come to their solution to the problem. If they can’t, they can submit a data request to the data team. Most teams today use a mix of Jira, Google Forms, Confluence and Slack to answer these questions.

In Conclusion

Teams should think about the data requests as their customer pulse. Customer support teams look at customer support requests and try to not only solve that problem for one customer but make sure that other customers don’t have that problem in the future. Data teams should aim to make old answers to common questions searchable and accessible so that they aren’t answering the same question multiple times. Additionally, if a certain dashboard or metric is difficult to find, they should think about ways to make it more legible for their customers. When customer support teams see that certain requests are coming up often, they work with the rest of the team to fix the problem at the root source. Similarly, data teams should look for patterns in their data requests and try to help employees by solving the problem at the root.

By managing a transparent and inclusive data requests process, data teams can achieve a similar outcome to support teams. They can start to data requests with good documentation and common FAQ’s and they can start sending more time servicing the difficult question or the tasks in their work that require more effort. Today, data teams say that about 50% of the questions they receive are questions that they have answered in the past and that over 30% of their time is spent trying to find and understand the data. Both of these trends are only more prominent now that teams are remote. Data teams that think ahead and want to spend less time getting pinged on slack in the middle of the day should think about adopting a data knowledge management tool to solve their problems.

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