Data teams are starting to look and work like product teams. The role of a data product manager is becoming more common and data teams are starting to specialize in specific parts of the data infrastructure. The data product now supports many types of stakeholders in the organization. Some of these stakeholders work closely with the data team on a weekly or even daily basis and some stakeholders rarely chat with the data team but are reliant on the data product to achieve their goals. Data teams have faced growing demands, making it harder to understand the different ways stakeholders interact with data.
By the end of this post, we should’ve shed some light on the following questions:
How to measure success of a data team?
What are the metrics that matter to data teams?
How to prove that your data team is making an impact on the business?
One-on-one conversations can be a great way for getting very granular, deep feedback from stakeholders about their experience using company data. However, it’s hard to scale ad-hoc feedback as the team scales. Furthermore, ad hoc feedback from stakeholders tends to focus only on those who are already using the data regularly, not those who are not able to use the product. Additionally, product teams can have one-on-one conversations with customers about the pain points they are facing, but these interactions are not scalable as the company scales.
The product teams rely instead on survey tools such as Net Promoter Score to obtain an overall picture of customer sentiment. As a refresher, the net promoted score is a single question, which asks customers “How likely is it that you would recommend [product name] to a friend or colleague?”. Net Promoter score measures customer experience. The metric provides the core measurement for customer experience management programs the world round.
Customers can answer the NPS survey by picking a number between 1 to 10, with 1 being the lowest and 10 being the highest value. The total net promoter score is calculated by subtracting the percentage of detractors from the percentage of promoters.
For example, if 10% of respondents are detractors, 20% are passives and 70% are promoters, your NPS score would be 70-10 = 60.
Pairing this simple survey with an open-ended question at the end, which asks: “what can we do better to improve the product”, can help product teams drill down into the health of their product. Given the shift towards data product management, we strongly believe that data teams can use a similar survey to measure their understanding of the data product.
Introducing: The Data NPS
The data NPS can help data teams understand the impact of their work to enable self-service and have a good pulse on what they can do to improve the data product. Of course, doing an NPS survey will capture a very small part of the story - the data team should integrate this survey with one on one feedback to drill down on specific challenges that employees are facing.
Just like any other form of input from external customers or stakeholders, just because there’s a lot of people asking for something doesn’t mean you should prioritize it. The nice thing about this type of survey is that it gives teams a pulse check and a metric to improve over time.
This survey can help teams measure the impact of new tools or processes around data. It can be easy to complicate the survey and go into extensive detail about how employees are using data. Using an NPS format that asks only one question will increase response rates and assist the team in maintaining a single metric over time that can improve as more tools are added and processes are improved. To measure the impact of initiatives and the data confidence, self-service and literacy, we recommend sending the following question to employees once a month:
How easily, independently and confidently can you answer your questions using company data?
Employees can answer this question on a scale of 1-10. We recommend surveying all employees to get a benchmark of the entire organization.
- Promoters respond with a score of 9 or 10 and are very confident and literate data users.
- Passives respond with a score of 7 or 8. They are satisfied with your data product but not confident enough to be considered promoters.
- Detractors respond with a score of 0 to 6. These are unconfident users who are unlikely to use data to make decisions and may even discourage others from doing so.
Whether your team is in the early stages of building your data product and infrastructure or is well-established, the feedback from regular user surveys can help you prioritize maintenance and infrastructure work based on real user impact. If you want to run a more comprehensive survey that drills into the details of the data product, we recommend doing this on an annual basis as a year-end check-in for your team.
Understanding the results
The data team can use this simple survey once a month to understand how they feel about working with data. The proposed data NPS measures the confidence of employees use company data. To interpret the scores, a number is given from -100 to +100, with a higher score being more desirable.
The results may not surprise you at all. An anonymous survey can, however, uncover surprising attitudes. Having an unvarnished perspective is much more helpful in getting started. Take negative responses on board, but do not take them personally; use them to identify problems and make improvements over time.
Shaping the roadmap
Once you have the results, do some quick analysis and present the results to influence your priorities. Look closely at the results if they differ significantly from what you expected to see if you've found a real problem, or if the survey itself was the cause. But at this point, listening to your stakeholders and improving is more important than aiming for perfection.
Without a survey like this, it's difficult to quantify the impact of the data team or the satisfaction of the employees with the data product. The added benefit about measuring this metric monthly is the ability to benchmark new initiatives and tools that your team is interested in on-boarding.
After the analysis has been complete, we recommend sharing the analysis with the team and deciding what new initiative your team can focus on to improve this metric, or whether the already planned work will address the issues. Make sure you continuously report the improvement from this metric and make it clear to stakeholders that this metric is something that your data team is measure and trying to improve.
We hope that this metric can help data teams measure the impact of their work, quantify their value to the business, and make better decisions about where to spend a majority of their time. Data teams know first hand that if you can't measure it, you can't improve it. With the data NPS, we hope data teams can focus on the things that matter and improve their work to make every employee more confident when using company data!