Obviously, the issue of data and needing the right data is important. I guess what I want to do is bring this back up to.... We used education as an example, but what we are talking about here is data to be able to analyze the socio-economic gap between indigenous people and non-indigenous people in Canada.
I don't want to turn this into an exercise of collecting data for the sake of collecting data. However, the issue about data that is important is to identify what data matters, make sure you collect it and make sure you collect it well so that it doesn't have errors in it. You need the right data, and it needs to be of the right quality, but that data needs to be leading to something. In this case, it needs to be about whether or not the socio-economic gap between indigenous people and non-indigenous people is closing.
This one is more complicated because we talk about a nation-to-nation relationship, but quite frankly, I see it as a many-nations-to nation relationship. There are many nations when you are dealing with the first nations. They don't necessarily all have the same priorities, so that complicates the department's job of identifying what types of data matter to individual first nations or groups.
However, what we see too often is data that's collected and isn't used, data that is collected poorly so that it can't be used, or data that isn't collected when it should be collected. They need to have a very good framework. What is it that we are trying to do? We're trying to close the socio-economic gap. How do we measure the socio-economic gap? What data do we need to do that? Is that data of sufficient quality that we can rely on it, and how is it being reported? It's a simple model, but it is complex in the implementation.