 So, with that, I just want to conclude my thought process here in terms of this thing. So the top most item which you think about it, which needs consideration from the NPD angle is the right pricing and some blueprint of framework of how the right pricing should be and it has to have considerations across the verticals and across the data domain as well. There are two aspects. Data domain is a horizontal aspect and the vertical is like your finance data or people centric data or say weather centric data or car centric data. So that is the vertical centric item. So how you're going to achieve it? So this might be a, I'll say it is a hard problem to solve. So this is something that needs to be thought about. Then the second is, as I told you, what is the protection for the data provider himself in terms of, of course, there are myriad of laws across, say, finance data. Now, suppose a company is having user centric data as well as the finance data. Now, what is the protection when they give out these data, when they do some aggregation using both these data sets because they have user dimensions as well as the finance dimensions and the NPD is forcing them to give a dimension which is blending these two. So how does, what is the liability protection available for that person? So for that data provider of the source. And the thing is, if you think about PDP, there is a usage. You need to get explicit consent for usage of a data from a customer. Now that usage, would you be able to enforce across your ecosystem, right? We talked about the tree, the federated tree, how it is going out. So what if some algorithm runs and tweaks the usage in terms of this thing? So what happens there, right? So the who has the honors of audit, whether it's going to be a government authority or who has the honors of audit or should I spend my own rupees on going and auditing each time the whole ecosystem is using it the right way or not. So that is another, another, another concern which has to be addressed there. And if you think about it, anonymization itself is a technical challenge. If you look at latest technical literature and all these things, truly anonymized data set is something very hard to achieve. It is achievable within a contained ecosystem, within your company. But say if a person is collecting aggregate sets from your company and 10 other companies and he's able to add some intelligence with this to the sum of the parts, you can get, you may not be able to get the exact name and the identity of the individual, but you still know or you have the digital persona of that person, right? So there is a problem there in anonymization itself. So the whole non-personal concept here, non-personal concept as well as data democratization, in the sense all the data sets available from all the companies outside, that sounds a bit oxymoronic to me in terms of how to achieve both together. And the last thing which we talked about in terms of the IP rights is if data is the USP of your organization, data or its derivative or its inference is the USP or the IP of your organization, pretty much I believe this commoditization, which the NPD is bringing about and scrutiny by public domain of all the assets which you are having, it can pretty much nuke that IP or USP of yours. So that is another thing, how it can be addressed with the data as a business, especially from a startup perspective, probably a group tech company who has trillions and petabytes of data points can still survive and go ahead and do new creation. But if you are a data oriented startup, which you're doing that, whether you have a viable business at all comes into question with this particular aspect.