← Back to Blog
Thursday, December 8 2022

Data Overload: How to Manage Too Much Data

Yes, there can be too much of a good thing—here’s how to manage data when you have lots of it.

Watch the webinar on-demand. 

Data has become increasingly important in making decisions and informing action. But is it possible that there is such a thing as too much data?

The answer depends on how we utilize the data at our disposal. Too much data can lead to decision paralysis. When faced with too many options or variables, it can be difficult to select the most relevant information and make an informed choice. Having an excessive amount of data can overwhelm and create confusion with its sheer volume, distracting us from vital issues that data is intended to solve.

With companies creating more and more data every day, it’s crucial to find ways to manage the data overload. In a recent webinar, our three panelists share insights on how to do exactly this. They are:

  • Blake Burch, Co-founder & CEO at Shipyard
  • Will Yang, Head of Growth at Instrumentl
  • John Morrell, Sr. Director of Product Marketing at Acceldata

Here are their top tips on how to manage too much data.

Avoid Data Paralysis by Finding Your North Star Metrics

Companies that have invested in collecting and using data must learn to be strategic with the data at their disposal. Otherwise, they can’t be sure where to look and how to understand what matters—and what to do with all that information.

Yang suggests finding your North Star—the thing you’re always working towards—to guide your decisions on data collection, analysis, and usage. Oftentimes with data overload, you feel overwhelmed because you don’t know where to focus.

“You can start looking at one thing you think is interesting, but it might not drive that North Star metric. Organizations first need to get clear on what their North Star metrics are. Ask yourself, are we even measuring the right things before we start collecting data? This is a great first step in feeling a bit more order in the midst of the chaos you might be seeing.”

Yang shares that his company operates on bi-weekly sprints. Each sub team has clear key metrics that everybody is reporting on that drives the overall business growth. This helps them pinpoint the handful of ingredients that have gone into their recipe for success rather than getting lost in a sea of metrics and data points.

Use Data Delivery to Positively Influence the Way You Manage and Leverage Data

In order to be strategic in the way you use data, you need to have the right infrastructure in place where people can access data in ways that are meaningful to their use cases. The way data is processed and transformed contributes to the data overload problem, either by making it worse or drastically improving it.

According to Morrell, to make the data useful, organizations need to examine how they’re delivering data to the end user.

“It’s really the reliability of these data pipelines that can prevent companies from being more strategic. If the data doesn’t flow effectively and the proper tools are not in place to ensure the proper flow of data, the end users won’t receive the data in a timely manner. And then they lack trust in it, so they just won’t use it. To be more strategic, you have to make sure the data gets delivered reliably and effectively.”

While many companies believe that solving this challenge means hiring more qualified data engineers, Morrell shares a better (and more cost-effective) approach—equipping your existing data team with the right tools so they can properly structure the data pipelines.

“If you can give them the right tools to ensure high-quality data, this will free up their time from data pipeline troubleshooting so they can deal with more data requests. They can be more effective and efficient in ensuring there’s high-quality data and high data reliability.”

Teach Others How to Make the Best Use of Data

The data overload problem comes from two different buckets: one is the inundation of requests from the people who need to use the data, and one is the data quantity issue. While delivery pipelines and organizational tools can help to solve the latter, data teams can also address the former by prioritizing data literacy, self-service options, and a better understanding of how others are using the data.

Burch believes that digging deeper into data requests can help data teams to more effectively deliver on the organization’s needs and minimize the feeling of data overload.

“Any requests for data should be assessed in a bit more depth rather than taking everything that comes your way. You could ask, ‘What is the data going to be used for?’ or ‘How long is it going to be used for?’ or ‘How are we going to validate this and how will it be useful?’ You can weed out a lot of overload this way. Another option is to set up internal systems to allow data access to be more self-service. Teams can grab data from visualization tools or orchestration tools, any basic requests that will allow people to serve themselves.”

Data literacy plays a big role in this. Educating people outside of the data team on what data exists, how to access it, and how to make sense of it will help them narrow their focus to what they really need and learn to tune out the rest.

Final Thoughts

Overcoming data overload is truly a team effort. Understanding how other departments want to use data and the results they expect can go a long way in ensuring companies capture the right data in a reliable, consistent manner. By prioritizing ongoing communication and collaboration across all functions, data teams will be more empowered to create the right data sets and build quality metrics around them—and that’s something all organizations can do today, regardless of size, resources, or experience.

For more insights, check out our other webinars in our Driving the Data-Driven Enterprise series!

Launch Blog CTA