Without knowing its strengths and weaknesses, no team could prosper, let alone a data science team. Every team requires a set of KPIs that indicate their growth and performance over a certain period – and data science teams are no different.
However, the KPIs for data science teams are not like other teams. Not only are they distinct, but they're sometimes also very specific to the nature of projects the team is doing. So, in this article, we will be looking at the top KPIs that every data should have.
Data science teams are different from the rest when discussing the tech industry. Some industries, such as mobile and web development, have a swift pace where the KPIs are closely related to the velocity of deliveries, but it's not the case for data science.
Studies suggest that many companies that introduce data science teams into their pipelines expect quick ROI, often pressuring them to deliver double-digit growth. This not only backfires and slows the teams down, but sometimes also ends up in teams being discontinued by top-tier management, as their expectations don't meet the team's performances.
In reality, good data science teams often take months or even years to deliver value. It’s not like every other IT industry where you’re just building stuff. Instead, it takes patience and innovation.
Consequently, this begs the question: "How can you track progress over such long periods of time?" Well, the simple and effective trick is to use suitable KPIs. With the right KPIs, the management can stay on the same page as the developers and ensure transparency. At the same time, the teams can go at their own pace, doing what they need to.
Now, let’s move on to the KPIs that every data team should have. While there are a lot of KPIs that your data science team could follow, I’ll be listing down the most generic ones that all data science teams must follow, no matter their domain.
Data quality is always going to be in the first place whenever we talk about data science. If the quality of data you're using is below par, you're never going to make it, no matter how advanced models you use. A simple logistic regression could perform better with high data quality than a random forest trained on data of questionable quality.
So, always make sure you keep a check over the perceived data quality in your data team and include it in your KPIs. It's always going to have a significant impact on your end results.
Increasing business value is one of the major reasons why data science exists. Today, data is such an invaluable tool that it's being compared with gold. It delivers immense business value and helps businesses expand quickly.
However, these KPIs primarily depend on the kind of business you're doing.
Data science teams deliver massive value to customers. Data helps businesses understand their customer base better, making the services more customizable and personalized. This makes the products more user-friendly and increases brand loyalty.
Also, since data provides customer patterns and trends to the businesses, they can ensure high-quality customer support for their customers and ensure their needs are being met.
The number of changes you make to your data model is crucial to the performance of your data science team. If you're making changes to your data model every day or even once in a few days, you're probably doing something wrong. On the contrary, fewer data model changes are required if you have a mature data science team.
Measuring the number of times things went south due to data quality is essential. If the data team is often experiencing setbacks and the reason is often data quality, you might be in a bit of trouble. In such scenarios, you might need to make some amends in your data ingestion or ETL or even check your data warehouse/lake.
Data gathering is the foremost process in the lifecycle of data science, and the faster it can be done, the quicker a data science team can deliver value. If data gathering often takes a long time and is not trustworthy, there might be some amends that you need to make in your process.
Always consider the ease and velocity of your data gathering processes, and since it's a continuous process that makes everything else depends upon it, you need to make sure there are no compromises made on it.
This KPI refers to the amount of time a data science team requires to deploy a model to production once everything is ready. Ideally, this shouldn't take much time since the model is already trained, and it just needs to be deployed somewhere so it can be used.
However, model deployment is a continuous process. Data science teams are deploying models to production every now and then, so if it takes longer, it might start to bottle-neck your processes.
The data science lifecycle contains a lot of phases that can be entirely or at least partially automated to minimize the human effort required every time you have to do something. While a team might be doing everything manually upon its inception, it should start moving towards automation gradually once things are settled.
Just like every other team has its own set of KPIs that reflect its growth and performance, data science teams also have some KPIs that they need to follow. Throughout this article, we have covered the top eight most important KPIs for every data team, no matter the domain. So, make sure you go through the article in detail and make sure your team starts following them.