The interesting thing about data quality is it's a mess. It was a mess in the analog times before, but you couldn't see it. And now in the digital world we have much more data and now we can see how messy the data is. Right. And when you look at census data before on paper or CRM databases, right now, it's a lot of old data in there. And with analytics, data, behavioral data, we see how messy the data is and that won't go away. And especially if we're talking omnichannel, if we're talking marketing, that's data from different sources captured in different ways on different websites. So that's tricky and we can't solve it. We can only manage it. And that's interesting part because, I mean, it's a three sided answer, I would say. First, I mean, we can, of course, make sure that the that the tracking that the collection is okay. We can test we can use tools like Observepoint, etcetera to make sure the data quality there is great. But second thing is we always have to check the data for anomalies because we will track data that's not okay and then we need to spot it. So pattern recognition. And the third thing is we have to live with the uncertainty. We need to know that the data is not okay and we need to know in which ways it's not okay, and then we can try to correct for that in our analytics part.