KPIs for data science teams

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Are you wondering whether key performance indicators (KPIs) should be used to track the performance of data science teams? Or maybe you are more interested in hearing about specific examples of KPIs that can be used to track the performance of data science teams? Well either way, you are in the right place!

In this article, we tell you everything you need to know about KPIs for data science teams. First, we discuss what KPIs are and why it is important to track KPIs for data science teams. After that, we describe the characteristics of good KPIs for data science teams. Next, we describe some characteristics that demonstrate success in data science teams. Finally, we provide examples of KPIs that reflect these measures of success.

What are KPIs for data science teams?

What are KPIs for data science teams? KPIs are metrics that are used to measure the performance of data science teams. These are metrics that can be used to gauge whether data science teams are making progress towards their goals and whether they are becoming more efficient and effective. These are not metrics that are used to measure the success of specific projects, but rather high level metrics that measure the health of the broader team.

In this discussion of KPIs for data science teams, we will specifically focus on objective metrics that measure the actual performance of the team. We will not focus on subjective health indicators that measure the internal health of data science teams, such as metrics that indicate whether team members feel mission aligned or whether they feel a sense of ownership. These internal health indicators will be discussed in a separate post.

Why monitor KPIs for data science teams?

Why should you monitor KPIs for data science teams? Here are some examples of benefits that you stand to gain from tracking KPIs for data science teams.

  • Clarity around what success looks like. One advantage of tracking KPIs for data science teams is that KPIs provide team members with a clearer definition of what success looks like. This is particularly true for data minded teams. While high level strategic goals can sometimes seem vague and abstract, clear and measurable KPIs can provide the team with a more concrete example of what success looks like.
  • Understanding of whether processes are working. Another advantage of tracking KPIs for data science teams is that they can help to provide clarity around whether process changes that data science teams are implementing are helping the team succeed or holding the team back. While any good data scientist would tell you that it is tricky to determine causation based on observational data, that does not mean that this data should be ignored completely. If you implement a change to your team processes then see an immediate drop-off in your KPIs, that might be an indicator that you should investigate whether that change was for the better.
  • Understanding of where the team needs to improve. Another advantage of tracking KPIs for data science teams is that they can help you to determine where the team needs to focus their efforts in order to improve. Well thought out KPIs can help you determine where you need to focus your effort because they can help to show you where you are doing a good job and where you are not doing such a good job.

What does a good KPI look like?

What does a good KPI look like? In this section, we describe characteristics of strong KPIs that can help teams to improve. You should keep these characteristics in mind when determining what KPIs to measure for a data science team.

  • Measurable. Perhaps the most important characteristic of a good KPI is that it is measurable. This means that there is a clear and unbiased method that can be used to measure the data required to track the KPI. In addition to just being measurable now, the KPI should continue to be measurable in the future so that you can understand how your performance tracks over time. Ideally, the KPI should also have been measurable in the past so you can build a baseline and understand how the team performed in the past.
  • Aligned with strategy. Another important characteristic of a good KPI is that the KPI should be aligned with strategy. This often means that it is aligned with the strategy of the data team, but it might also mean that it is aligned with the strategy of the company or the strategy of the organization that the data team is embedded within. If a KPI is not aligned with strategy, then it will not be meaningful to track success because it is not aligned with the definition of success that has been laid out.
  • Actionable. In addition to being measurable and strategy aligned, a good KPI should also be actionable. That means that there should be actions that the data science team can take to move the KPI. If the factors that drive the value of the KPI are totally out of the hands of the data science team, then it is not a good measure of success for the team.
  • Simple and intuitive. Finally, a good KPI should be simple and intuitive. Someone should be able to understand what the value means and how it is an indicator of success without having an unreasonable amount of context on the data team and what they are working on. This will make it possible for you to share the success that the team has had with the rest of your organization because they will be able to understand what this success means.

What factors indicate success in data science teams?

What are some factors that might indicate success in a data science team? While the exact definition of success will vary depending on the strategy of the company and strategy of the data science organization, there are some common themes that come up in discussions of success in data science teams. Here are some examples of common themes that might arise.

  • Customer satisfaction. The first common theme that comes up when discussing the success of data science teams is customer satisfaction. Are your stakeholders happy with the work you have completed for them? Do your stakeholders feel like they get enough support from your team?
  • Value provided. Another common theme that comes up in discussions of data science teams and their success is the amount of value that the team provides to the company. Is the team saving the company money by automating manual labor? Is the team providing the company with incremental money by increasing the number of users that have adopted their product or the average value that one user provides? There are many different ways to measure this value, but in general it is important to understand how the team is contributing to the company.
  • Adoption. The next common theme that comes up when discussing the success of data science teams is adoption of products that are created by the data science team. How frequently are stakeholders using your products? Are your products being used by the same person over and over again or are they being used by a wide variety of people?
  • Type of work. Another common theme that comes up when discussing the success of data science teams is the type of work that is being completed. Sometimes teams find themselves spending a lot of their effort on one type of work, when in reality they would hope to spend more time on a different type of work. In these cases, it can be useful to understand how your team’s effort is distributed over different types of work.
  • Reliability. Another common theme that comes up in discussions of data science teams is reliability of the products that they build. Are your products frequently failing completely? Are they continuing to run but producing incorrect data? These are just a few examples of topics to think about when measuring the reliability of data science products.
  • Efficiency. Another common theme that comes up in discussions of data science teams is efficiency. Efficiency can mean different things to different teams, but in the end it is a measure of how the team converts inputs into outputs. Are your data products using an inordinate amount of computational resources? Are they taking an inordinate amount of time to develop? These are just a few questions to keep in mind when thinking about efficiency.
  • Integration with other teams. A final theme that comes up when discussing the success of data science teams is the level of integration with other teams. Is the data science team included in important strategy decisions that will impact their team? Is the data science team collaborating with all of the stakeholders that they should be working with?

What are some examples of KPIs for data science teams?

What are some examples of KPIs that can be used to measure the performance of data science teams? Here are some examples of KPIs that can be used to measure the performance of data science teams.

  • Net promoter score (NPS). One example of a KPI that can be used to measure data science teams is the net promoter score. This is a metric that represents the satisfaction of their customers that can be collected via surveys that are sent out to internal stakeholders. This KPI can be used to measure customer satisfaction.
  • Dollars saved (or earned) through data products. Another example of a KPI that can be used to measure the performance of data science teams is the number of dollars that are earned (or saved) through the use of data products. This KPI will be difficult to measure for some data science teams (such as teams that work to influence company strategy) and easier to measure for other data science teams (such as teams that automate manual labor). This is one example of a metric that can be used to measure the amount of value that the team adds to the company.
  • Monthly users of data products. Another KPI that can be used to measure the success of data science teams is the number of monthly users that make use of data science products. This can be used to measure the adoption rate and ensure that data science teams are building products that are useful to their stakeholders.
  • Percentage of effort spent on long term initiatives. Another KPI that can be used to measure the success of data science teams is the proportion of their time that is spent on long term projects and initiatives, as compared to ad hoc work and incident resolution. This is one way to measure whether data science teams are spending the right amount of time on the right types of work.
  • Number of incidents per product monitored. Another KPI that can be used to measure the performance of data science teams is the number of incidents that occur per product that is monitored. This provides a measurement of the reliability of data science products.
  • Time to incident resolution. Another KPI that can be used to measure the reliability of data science products is the time to incident resolution. If data science teams are taking weeks or even months to resolve incidents and get key products back up and running, this is something you want to know.
  • Cloud computing costs per team member. Another KPI that can be used to measure data teams is the amount of money that is spent on cloud computing costs per team member. This can help to measure whether data science team members are using computation resources efficiently or not.
  • Percentage of product changes that are AB tested. A final example of a KPI that can be used to measure data science team performance is the percentage of product changes that are AB tested before they are released to the full population. This can help to measure how engaged the data science team is with different stakeholders and whether a measurement-focused culture is permeating the company.

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