We are excited to introduce Data Health — a feature designed to help you clean your data and save time.
While speaking with our customers about the quality of their data, we heard the following pain points:
- “People ops team is stretched too thin and I just can't dedicate anyone to clean our data”
- “I don’t know where to start with data cleaning, I don’t have visibility what data is missing or needs updating”
- “We have so much data we are not using…”
- “There's no point in starting any work in people analytics when our data is not in good shape”
- “I am worried that at the management meeting, I will present HR updates with data that is not correct”
- “I am afraid that if I start doing analytics most of my work will revolve around data cleaning”
- “We spent last weekend going through all of our tools and cleaning everything up”
The foundation of any analytics endeavor is data quality and integrity. Data cleanliness has been one of the persistent blockers for successfully implementing people analytics practices across companies of different sizes and shapes. As the old saying goes: “Garbage in, garbage out”. It’s not about the number of data points you are collecting, but their quality and richness.
Instead of going through the mindless chores of data maintenance that can take hours away from you and your team, focus on ensuring the metrics you are about to present at your next management meeting are 100% accurate.
This is why we are introducing Data Health — a feature designed to help you improve the quality of your data and save time. Richer data means better metrics. Better metrics will give you insights for making better decisions about your people.
Your data will never be perfect, and it doesn’t need to be. However, with Data Health, you will always know what areas you can improve, what’s important, when it’s important.
Let’s see how this works.
Under the hood
Our Story metrics rely on your connected apps. Data from these external sources gets pulled, broken down into its most basic quality attributes, and then, with some mix’n’match magic, assembled again in a form of metrics.
Data-quality attributes are standard bits of information you are already tracking in your ATS and HRIS. Those are the fields like employment status, employee’s age, employee’s ethnicity, etc.
To assemble data correctly and present it as a metric, we map all the data attributes and define their functionalities. In that way, we can know which elements of data are contributing to which precise metrics. Moreover, we can understand if specific metrics do not show proper values due to inadequate or missing input data.
On the surface
You can jump into Data Health from the navigation bar.
We had much fun coming up with a narrative for this feature. Turtles are known for their longevity, and albeit being slow, they win races against those fluffy-tailed bunnies. Embodying mindfulness as one of our design ethos, we want you to slowly but surely improve your data while having a sense of progress along the way. Data integrity is not an overnight patch.
Next, you’ll see three different groups of data attributes: Needs Improving, Missing, and Healthy. For each, we show the score and steps on how to improve or acquire.
“Needs Improving” are attributes you already have enabled, but still require some improvement. Metrics that use these attributes are working and available to you, but are not 100% right. You should start cleaning these first.
“Missing” is the unfulfilled potential of your connected apps. These are the attributes that you can and should start tracking in order to enable new metrics.
“Healthy” group contains, well, all the good stuff. This is the list of your clean data-quality attributes you don’t need to worry about. Hopefully, this will be your longest list.
Where are we heading?
Our roadmap for Data Health includes
- Connecting Data Health with Story metrics
- Enabling data enrichment
Soon you’ll be able to see the health of each metric, right on the metric. Evenmore importantly, you’ll be able to see all metrics your data currently can’t support, with better-explained steps on how to acquire them. For some of the metrics, we are tinkering about ways of supplementing them with your company-specific flavor. More on that later.
If you have any questions or feedback, we’d love to hear from you. Reach us at email@example.com
Happy data cleaning!
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