I’ve recently been reading a great book called “The Unseen Hand” by Mark Twain. The book is about the ways in which our thoughts and actions affect our environment. The author, David McClelland, makes the point that the most “visible hand” is our thoughts, which make up 95% of our actions. I love that because it helps me understand how I can get the most out of my data.
I like the idea of “shadowing” your data. In order to understand it, I would need to understand it. To do that, I would need to be privy to the thoughts of the people who work on my data. In order to get the best use out of that data, I would need to be aware of how my actions affect others, and how I am influenced by what I am doing. I really enjoy the way that data analytics is changing the way we work.
If we were to consider how we work and what we do, we would probably come up with a different definition of data. We would probably say that we do things for a reason. That reason may be to make our company more successful. It might be to make sure that we are never late for work, or to keep our children safe. It could be to keep our own identity intact. We’d probably even say that it is about the people who work on our data.
You might think that I’m advocating data analysis as being about data, but you would be wrong. Data analysis, as we know it, is about data. While we’re in the business of analyzing data, we have to make sure that the things that we analyze are accurate. It is important to know what happened and what we should do about it.
The only thing we do is to validate, not analyze. We are not looking for a conspiracy or a conspiracy theory. We are looking for a rational explanation for data that we found. We are looking for a way to make sense of the data. We are looking to make sense of the data to give it meaning, and to make better use of it.
Just think about this for a moment. If you go to a data source that you don’t really trust, will you trust the information you get from it? The answer might not be obvious, but it’s important to have a good reason for giving it less weight. Your data warehouse may include data from legacy systems (such as your old accounting system). Because of this, you may not be able to trust the data we get from those legacy systems.
In the past, in the information age, the most important thing for your data warehouse is to make it as easy as possible for people to get to see and use it.
The problem is that the data warehouse is built on legacy systems, and these systems are sometimes hard to get to. But we don’t want to build something that is too easy to get to, because we don’t want to make it less accessible. That is, we don’t want to make it harder for people to make sense of it, and we don’t want to make it easier for people to understand how hard it is to get to.
The reason we dont want to make it harder is because this is the essence of a knowledge system. If we wanted to make our data warehouse easier to see and use, we would build a better knowledge system. But that is not the point. The point is that these legacy systems may be “hard to get to” and we dont want that. We dont want to make the knowledge system “too easy to get to” either.
We may have data from legacy systems that we might not have access to, not because we are unwilling to share a certain piece of information, but because we dont want to. Because of this we can’t share them because it makes it harder for us to understand what we have if we had access to it. We want to make the data warehouse easier to work and understand. That’s what a knowledge system is.