How to Scale Your Impact When You're a Data Team of One

There are some days when Kyle Ogilvie wakes up and everything is quiet. He gets to enjoy a slow start to his day with a calm, peaceful morning. But instinctively, that calm can feel a little unnerving because most days he’s dealing with quite the opposite.  

As the founder of &KO, a data, strategy, and operations consulting firm, Ogilvie is used to waking up to an inbox of client requests for enhancements to their data products or training for their teams. Working with businesses and professionals who may not have a data team of their own to consult with, he’s constantly balancing competing priorities from clients eager to access his data expertise and explore new data capabilities. Their enthusiasm can sometimes lead to an overwhelming flood of asks.  

“Your requests start to explode, and that's a good thing,” Ogilvie says. “It means people are using the things you're building.”

But with that enthusiasm comes pressure. If you’re a one-person data team fielding an avalanche of requests, Ogilview says it can feel like you’re getting “pressed to death.”

You may be one person, but you can conquer the mountain. By thinking strategically, any data professional can not only survive the demands of working solo but also grow their influence to every part of the organization. Below, Ogilvie shares some thoughts on how to do just that.  

Homing in on the right mix of projects

It might be tempting to take on every project that comes your way to prove your value to the company, but part of being an effective employee means understanding your limitations. As a solo data professional, learning how to prioritize is the key to making the biggest impact with limited resources.  

Ogilvie thinks of the work as falling into three distinct buckets:

• New products
  This bucket includes independent analyses or original deliverables that allow you to explore new frontiers in your data and answer exciting questions you haven’t tackled before. They're the projects where you get to “use your whole brain.” ‍
• Enhancements
  Once you’ve built out products like data sets, visualizations, and databases, you'll likely need to upgrade them at some point to make sure they stay useful or to include new, relevant information. You can generally see these upgrades coming.  ‍
• Maintenance
  On the other hand, you don’t always know when a maintenance project is going to rear its ugly head. These can be tech debt maintenance issues that arise when pipelines fail or data looks off and you have to identify the problem and fix any bugs.  

New products provide the most value for the organization, while enhancements provide a bit less, and maintenance keeps things steady. However, ignoring maintenance issues can detract from the value of your existing data products. So, even if you want to minimize maintenance issues to focus on high-value projects, you still have to prioritize them.  

“With these projects, it can be quite unclear what the problem is and how to fix it and that’s frustrating to your stakeholders,” says Ogilvie. “If your stakeholders start to distrust your products, people will stop relying on you, and you'll just be spinning your wheels, trying to fix the same things over and over and over again.”

Practical solutions for expanding your impact

New products in your project pipeline can show your leadership that you have a forward-thinking vision for how to implement data strategy across your company, says Ogilvie. How can you minimize the grunt work so that you can invest in these high value projects that will make a difference?

Prioritize user experience

In order to create data products that will stand the test of time, you have to build them with intention. And to remember they're going to be used by other people.  

“Every time I approach a new project, I think about taking myself out of it,” Ogilvie says. “If I wasn’t a data person or if I didn’t build this, what would I need to use it? Where could I go to answer questions? And what do I want to know from it?”  

Getting ahead of these basic questions will ensure that you're designing data products that function for professionals with limited technical expertise and make insights easy to access for everyone. When you kickstart the project with the user at the forefront of your mind, you can deliver long-term solutions that don’t require constant maintenance or updates. And, you can introduce users to self-service BI for small teams.  

Take advantage of automation

As you’re being assigned tasks, take stock of all the requests that you're required to manually carry out. Find trustworthy tools that can help you automate those tasks and processes, which will allow you to multiply your capacity.  

When deciding on potential tasks to automate within a project, Ogilvie also recommends identifying some of the more difficult or complicated parts and focusing on how automation can make those more burdensome issues easier and less time consuming, which will likely give you back the largest chunks of time.

However, he cautions, “You don't want everything to be out of sight, out of mind.”  

You need proof that any automation tools you're using to make tasks more efficient actually work correctly and that bugs don’t pop up that can compromise your data and create annoying tech debt issues down the line.  

Employ AI  

This isn’t some novel insight, Ogilvie says, but it's practical. Using AI has given him the most capacity back, especially when working as a solo data professional.  

He notes that it can be particularly helpful when dealing with a cold-start problem, like generating that first bit of code you need to fix a bug. In some cases, AI can even do the majority of behind-the-scenes tasks you need for data analysis or creating workflows, leaving you with the simpler task of validating the work.  

It’s not a catch all, but when used appropriately, it can deliver significant value by freeing up your time.  

Build for inevitable change

“Every single thing that you take on as a task, you should think about it changing,” Ogilvie says.  

You may create a product within the parameters you're operating in at the time of development, but those inputs are likely to change over time. For example, if a team starts using a different email client, you may not receive data tabulated in the same way. Do the tools you use give you the space to make changes easily or will these types of changes force you to rebuild data products from scratch?

Likewise, consider whether your requestors are appropriately planning ahead. When they ask for a data set for April, are they going to return in May asking you for a revised version? If you can prepare for these simple requests by making your products and processes repeatable, then you can not only save time for yourself but also build goodwill with your internal customers by delivering what they need quickly.  

Proactively communicate your priorities

You may be a numbers person, but sometimes words will be your saving grace. As a solo data professional, you have the benefit of knowing exactly what projects are in your pipeline. Share that information with all your stakeholders so everyone across the company is on the same page about priorities.  

For example, Ogilvie created a newsletter for his colleagues that provided enough details to keep them in the know about how projects were advancing, when updates or changes were being made, and when products would be released.  

“Most people didn’t read it, but it was there if they needed to refer to it, and if there was ever a question about prioritization,” he says. “And that’s the conversation you actually want to encourage.”

Keeping your colleagues aware of everything on your plate could even score you some much needed investments or assistance.  

Provide documentation and training

You also want people to be able to use your products without them having to nag you for every little question.  

Training is one way to help people become more familiar with your data products. But Ogilvie notes that potential trainees need to have a vested interest in actually learning what you’re presenting; otherwise, they’ll tune out.  

As the one-person data team in your organization, you can lean into your time limitations and inability to answer every small service request to encourage them to take training seriously. And don’t forget to record all the training you conduct so you don’t have to repeat them.  

You should also build out documentation.  

“Documentation needs to be clear, easy to navigate, and you should be able to find the information that you're looking for within 30 seconds,” Ogilvie recommends. “Prioritize structure. Creating an outline really helps because the outline should correspond to the table of contents, and every single person should know exactly what is in every part of the outline from reading the headers.”  

Start making an impact today

Remember, enhancements and maintenance projects could help you curry favor with people across your organization, but often, these tasks are going to be expected.  

If you want to communicate your value and the value of good data to encourage your company to eventually invest in building out a team, then you have to step up, stand out, and make a visible impact. Clear space in your schedule so you can engage in those more in-depth conversations where you can better understand your colleagues’ goals and design new solutions that will make them achievable.  

Domo can help solo data professionals automate repetitive data tasks, build reusable dashboards, create governed self-service tools so others can find answers independently, and monitor data assets in one place. Ready to learn more about how Domo can help you increase your impact? Join the Domo community where you can connect with other professionals navigating the same challenges.