What can I do about poor data quality?
Data quality can sometimes be a challenge for real estate companies when it comes to a data-driven approach. Here's how you can improve your data quality using Homepal.
Many are unsure about their data quality. For example, some data may be missing, things are referred to differently, or not all cases are reported in the “right” way, which makes it difficult to get an accurate and reliable picture of the activity.
Summary:
Many think their data is worse than it is - the key is to start using it even if it is not perfect.
Incomplete data can still be valuable and used to get an overall picture.
It is important to start analyzing and following up, even if the data is not completely accurate - don't wait for perfection.
Homepal helps to detect and address gaps in real estate data through modeling and quality review.
Data visualization is crucial to highlight issues and improve data quality over time, which Homepal supports with specialized dashboards.
First of all, let's say something important:
Many people think their data is worse than it actually is - the key is to start somewhere.
There is a lot of value to be gained, even from incomplete data. Data quality does not have to be perfect for you to benefit from it. Depending on how you go about your monitoring, it doesn't matter if things are named differently, or if some elements are missing. In these cases, working with lump sums is often a good start to get an overall picture, and as your organization gets better at working with and collecting data, the exact figures will come into place.
If you want to wait until all the data is perfect to start analyzing and monitoring, you will probably never get started - the most important thing is to start somewhere, and sooner rather than later.
How to improve your data quality with Homepal
At Homepal, we are experts in real estate data. Once we get access to your data from your suppliers, we model and quality check it. At this stage, we discover what kind of flaws exist. For example, a certain type of data may be missing or incomplete.
At this stage, your Customer Success manager (i.e. your contact person at Homepal) will help you work out how to solve these problems. With experience from how other companies have gone about improving their data quality, we will guide you in the right direction.
For example, the number of open work orders may not match between reality and your case management system.
We will find out why this is the case. Are completed cases not being closed in the system? Are cases coming in that are not registered in the system? A combination perhaps?
The next step is to make the problem visible, that is, visualize it. This will help you both correct the error backwards, but also show the rest of the organization the consequences of not reporting correctly forwards.
Now we can start cleaning up the data quality. While we're doing that, we'll make sure it's right going forward.
How do I clean up my data?
Glad you asked! Homepal's answer is always to visualize, it makes it becomes tangible. We've created a dashboard whose sole purpose is to help you with your data quality - and put it all together in one guide: download it for free here: https://homepal.se/guides/5-dashboards-foer-oekad-datakvalitet
