Data is the lifeblood of every organization, and the teams that manage it are critical to success. Data teams are responsible for collecting, analyzing, and reporting on data that is used to make business decisions. But how can you tell if your data team is truly performing at its best?
The answer lies in KPIs, or key performance indicators. By tracking the right KPIs, you can get a clear picture of how your data team is performing and identify areas for improvement.
So what KPIs should you be tracking? Here are some of the most important KPIs for data teams:
This KPI shows how effective your data and analytics team is at finding and understanding data patterns. The more insights they can generate, the better they will be at making predictions and recommendations.
If decision-makers are only using analytics occasionally, then it’s likely that they aren’t fully leveraging its potential. However, if they are using analytics regularly, then it’s likely that they are getting more value out of it.
Predicting future trends and patterns is one of the main advantages of analytics. However, it’s important to track the accuracy of these predictions to make sure that the team is providing valuable insights.
In today's fast-paced world, speed is often just as important as accuracy. That’s why it’s important to track how quickly your data analytics team can generate results. The faster they can work, the more value they will be able to provide.
This KPI can indicate how reliable your data engineering team is. The more uptime you have, the less likely it is that you will experience disruptions in your data pipeline.
This shows how often your data engineering team makes mistakes. The fewer errors and incidents they have, the more efficient they will be.
By tracking this KPI, you can see how quickly your data engineering team responds to problems. The faster they are able to fix errors and incidents in a short amount of time means less downtime for yourself as an organization.
By measuring how often your automated deployment is successful, you can save time by reducing the amount of manual work needed for data changes.
This shows how active your data engineering team is. The more new features and changes they deliver, the more you will be able to improve your data pipeline.
This KPI measures the productivity of your data science team in terms of the number of models they develop each month. A high number of models developed per month indicates a team that is efficient and productive.
A high accuracy rate indicates that data science models are effective in solving business problems.
This KPI measures the effectiveness of data science in solving business problems. A high number of business problems solved by data science indicates that the data science team is providing useful insights.
A high percentage of projects on schedule indicates a team that is efficient and effective in managing projects.
By having the right KPIs in place, you can ensure that your data team is meeting your goals. As you can see, there are many KPIs that your data team can be tracking. While not an exhaustive list, these are some of the most important ones for each type of data team. No matter what KPIs you use, it’s important to have a clear understanding of your goals. And luckily, ThoughtSpot makes it easy to measure all of these KPIs in real-time so you can make decisions with confidence. If you’re looking to get started with self-service business intelligence or want to improve your current setup, start a free trial today and see how ThoughtSpot can help you achieve your goals.