Using objective and subjective data to unlock team performance

When I think about the differences between and the need for both objective and subjective data, a popular TV show comes to mind, House. If you aren’t familiar, the show centers around a brilliant yet misanthropic medical genius who leads a team of doctors that treat patients with strange illnesses no one else can seem to figure out. His uncanny ability to combine a seeming battery of diagnostic tests with subtle (and often controversial) observations gives him insights most others miss.

Although the doctors we visit when we aren’t feeling well may not be dealing with such extreme circumstances, the basic methodology is the same. By identifying symptoms through observation, physical examination, laboratory and diagnostic testing as well as subjective information from the patient telling the doctor something that they can’t see, a diagnosis is formulated. Without the information about the things we feel, the doctor would have a difficult time determining what questions to ask us and which tests to run. Without the empirical symptoms and verifiable test results, the treatment options offered by the doctor and be proven little more than witchcraft.

What is Objective data?

Objective data is fact-based, quantitative and observable (think SMART goals.) A good example is temperature; at any given point we can read a thermometer and identify how many degrees Celsius it is.

Other examples of objective data:

This is the type of data we are used to seeing in typical KPI tracking dashboards and analytics projects. Although valuable, these team metrics and SaaS metrics lack crucial context that the most BI or analytics systems typically don’t capture. Without qualitative data we often miss the reasons behind numbers rising or falling at a given time.

For example, looking at objective data alone makes it hard to tell why a product development team’s cycle time has expanded or its sprints have decreased in productivity. However, if via team polling we discovered that our development team felt product requirements were poorly communicated, we’d quickly understand the crux of the issue and be able to fix the issue and improve team performance. That, my friends, is the very definition of an actionable insight.

What is Subjective Data?

Subjective data is qualitative, based on opinions, interpretations, points of view, emotions and judgments. It is not ideal to rely solely on this type of information in scenarios like critical decision making (business, military, news reporting, etc.) Although subjective data cannot give us the objective truth, it can add invaluable context to help guide our analysis and decision making. The typical example for this type of data that comes to mind is how someone feels.

Other examples of subjective data:

  • Sentiment analysis.
  • Customer satisfaction.
  • Team morale
  • Employee engagement.

Note that even in the case of subjective data, scores can be applied. A Yelp review has a certain number of stars, a client might say they are 80% satisfied with their service (8 out of 10) or an employee might feel 95% confident they are prepared for their next development sprint. That said, any number we apply to these feelings is itself subjective.

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Metrics like NPS or Net Promoter score, which is a popular indicator of growth and long term customer loyalty, seem to get closer to an objective measure than shorter term customer satisfaction surveys. Similarly, Notion’s team polling gives managers insight into team morale, productivity and confidence — all indicative of team health and success.

Ask your team how they feel about the work ahead. Click here to get this team poll now.

However based on the criteria we’ve outlined above, I would argue this data is still subjective. And while subjective data gives valuable insight into a variety of values that can have a big impact on your team, your product and your business, its integration with objective data provides necessary context and factual evidence for extracting maximum value.

Going back to the initial example of a doctor/patient relationship, we can use a similar approach to guide our efforts to optimize team performance. Objective data tells us how we are tracking toward our goals. Combine that information with subjective data like team polling and we can spot trends such as burnout, attrition or lagging morale before they become critical issues. By joining these two distinct types of data, we can more effectively operate within a data-driven framework that allows us to make faster, more informed decisions.

How do you combine the objective and subjective to increase your team intelligence?

As Notion’s founder Dave mentioned in a previous post Running Your Team Better When You’re Running a Modern Stack, much of the inspiration behind the Notion platform came from trying to answer a seemingly simple question; “How is our team performing?”

It took too many people, too many tools, and too much wasted time to come up with a good enough answer. Plus, most of those tools don’t account for the subjective, so you’re still left with an incomplete picture.

With the data locked away in typically a dozen or so tools and one-off spreadsheets (not to mention data we might need from another department), we’re only left with a couple of options.

The way we’ve always done it

What’s the most common way we get data from a bunch of spreadsheets and tools? You guessed it, another spreadsheet. Time to go from source to source and person to person cobbling together all the information we need. Of course this makes anonymous team polling more challenging, which may lead to less than truthful responses from your team.

Unfortunately once we’ve compiled the data, asked it our most pressing questions and generated our first set of answers, this is really only the beginning. All we’ve really done is map out the process we will have to go through next time (and every time) we want to update or view our progress on certain KPIs, not to mention future efforts to track additional team metrics. Rinse and repeat.

By the time we have a snapshot of how our team is performing the information is out of date and we have the start the process all over again.

They way we are trying (or want to try)

The second option is to find a stand alone solution that you can use to gather qualitative data from your team then launch a long term departmental analytics project to bring it all together. Maybe you custom build surveys or pay a consultant to create and distribute them to your team. You might hire another consultant or build an internal team to manage implementing a complex business intelligence system too.

Of course nearly 80% of the time, resources and energy of those projects are spent on data warehousing, data clean-up and data integration. And most teams wind up collecting tons of data that they don’t actually need and don’t wind up using.

Even if you know exactly what you need, these business intelligence projects still require a significant amount of resources for requirements gathering, planning, tool evaluations, communication, execution and validation/on-going maintenance.

Plus, they’re expensive. Really expensive.

There probably isn’t a large pile of cash lying around to pay outside developers for this type of work. And we certainly don’t want to (nor should we) divert already limited development resources from buildingour product.

There has to be a better way…

We’ve already invested in the tools and the team, we’ve set the goals and we’ve got plenty of work ahead of us. How do we make sure we stay on track to meet our goals? How do we collect the data we need, easily share it with the people that need it and put it into a format that everyone can easily access?

Instead of having everyone commit their time to entering team performance metrics into a spreadsheet or sending over daily updates in Slack, we recommend using a KPI tracking dashboard that seamlessly integrates the objective and the subjective for a more holistic understanding of the team metrics that move your needle.

By having a platform that can quickly collect aggregate data in a central location, you can align your team to make faster, smarter decisions and you can focus your time on development and innovation.

This post was originally published on the Notion blog.