Tableau
Let’s face it, if you are dealing with data and analytics than data quality matters. Many people use Tableau for business dashboards, but before the data can be trusted for that purpose, it is important to make sure the data is high quality first. As the old saying goes, garbage in, garbage out. This is why almost 100% of the companies we have spoken with about RelayiQ’s data-driven notifications have asked us about the data quality use case.
We typically see one of two scenarios. In the first scenario, a company has no data quality pipeline set up and they (or others) are discovering quality issues on the fly in live business dashboards. Or worse, their data quality issues remain undiscovered and they are just consuming bad intelligence. In the second scenario, they use Tableau or something similar to create a dashboard specifically designed to help improve data quality but they have no alerts in place to let them know when issues with data quality actually occur. Instead, they only discover these issues when they periodically look at the dashboard or report.
We typically see the first scenario with analysts and the second scenario with IT, but it is clear that everyone is invested in data quality regardless of title because it can completely undermine your entire analytic operation.
Luckily RelayiQ can help in both scenarios. First by providing templates and best practices for people at the absolute beginning of their data quality journey. And second, by helping those with dedicated data quality dashboards in Tableau to discover quality issue in near real time. Before we dive deeper into these solutions, let’s look at the top three data quality issues we typically see folks running into:
Null Values – The absence of data in a field is often interpreted as a zero in Tableau and other visualization and reporting programs. Most folks using Tableau to identify data quality issues strive to identify these Null values as calculating in a zero is much different than simply not having a value.
Wrong Data Type – Sometimes dates are read as strings or numbers. Other data type issues can wreck havoc as well if they go undetected. This is a common check within data quality pipelines.
Outlier Detection – Many data sources are still the product of manual human entry. All it takes is one person accidentally fat fingering a huge data value to throw off your whole analysis. Typically filters are set up to identify and rule out such entries to smooth out the data and assure high quality.
RelayiQ’s templates are designed to be dropped in to existing Tableau environments and identify all three of the nagging data quality issues above with the added benefit of intelligent alerts that let you know when the issue actually occurs instead of waiting. We also offer professional services to help you get your data quality pipeline up and running fast if help is needed. Instead of waiting for your dashboards to lose credibility before addressing these issues, why not proactively address them with RelayiQ?
Many other companies we speak with are already using Tableau or a similar tool to detect data quality issues before the data reaches their analytic dashboards. However, most people do not actually find out about all these data quality issues until they go and check their data quality dashboards and reports. This is problematic because If you are checking your data quality dashboard once a day, errors that occur right after you check, can stay in place for the next 24 hours or until you go back in and check again. Worse, if you are only checking weekly, data quality issues can destroy the credibility of your analytic dashboards and reporting in the long term.
With RelayiQ you can set up smart notifications that catch data quality issues in your pipeline in real time and alert all the people who need to know about them with a customized message letting them know what actions to take. The best part is, RelayiQ installs in your existing Tableau environment and works with your existing data quality work flows, so you can have your alerts set up in minutes and not days or weeks.
If you agree that data quality matters for your business why not automate alerts to assure you are not only catching data quality issues, but you are catching them real-time? If this sounds appealing to you (and it should – who likes being surprised by poor quality data) then why not try RelayiQ for free for two weeks?