Recall that I said there are many steps in the process of turning data into information. This is just a partial list of the tasks involved in a best practices data warehouse operation. These tasks don’t go away in a model-driven approach. But, being well-governed requires repeatable processes that deliver consistent results. There is no way to achieve that if these remain manual tasks or are driven from different perspectives. Manual processes are often prone to human error, are inconsistent, and take too much time – especially when requirements change frequently. In a model-driven approach, information policies that support business requirements emerge directly from the business information model. These information policies form a reference against which all of these tasks can be executed. Because of this, you can automate a very large number of these tasks and achieve consistent results. Anybody who’s built data warehouses using Kalido would never go back to what one of our customers call “stick build”. Because you’d never want to have to design and write code that does all this from scratch again. We handle ragged and variable depth hierarchies. We handle data exceptions. We have a built-in match engine for identity resolution. We build slowly changing dimensions without you having to do a single thing. We generate data marts. And all of this is driven by the business information model.