We looked at the distributed data warehouses in detail in the previous post. We will look at the executive information systems and the data warehouse next.  EIS was the predecessor for data warehouses. EIS was the notion that the computational information should be available to executives via appealing screens but it was more about the presentation than the computation. When the data warehouse first appeared, it was not accepted by EIS but the warehouse provided exactly what the EIS was lacking.  EIS could be argued has later morphed into business intelligence especially in terms of key-ratio indicator, trend analysis, drill-down analysis, problem monitoring, competitive analysis and key performance indicator. Trend analysis is the time sequenced measurements, drill-down analysis is the slicing and dicing on the data, key performance indicators could be such things as cash on hand, customer pipeline, length of sales cycle, collection time, product channel and competitive products.
To support such EIS operations, there must be a data infrastructure. In fact it has been seen that for every $1 spent on EIS software there is $9 spent on the data preparation. Furhermore, the executive analysis shifts focus every now and then exacerbating the requirements from the preparation.
This is mitigated with a data warehouse. The warehouse enables searching the definitive source of data, creating special extract programs from existing systems, dealing with un-integrated data, compiling and linking detailed and summary data, finding an appropriate time basis of data and changing business needs.
The EIS analyst can go to various levels in the warehouse to find such information. For example, the departmental level of processing, the lightly summarized level of processing, or the archival or dormant level of data. The drill down for the data is also in this order of levels in the warehouse.
One specific technique in the data warehouse that comes useful to EIS processing is event mapping. The simplest way to show event mapping is to use a trend line - for examples revenues varying over the months.  Superimposing trend lines also gives correlated information.
Detailed data is prerequisite for EIS/DSS processing but just how much detail has also been discussed in this book. It's argued that there is no limit to the amount of detail desired. Since the requirements can vary and vary frequently, being prepared with as much detail as necessary is important. However, there is another approach that says just as much as the processing requires which is based on Zeno's paradox. In Zeno's paradox it is suggested that a rabbit can not outrun a turtle as long as the turtle has a head start on the rabbit.  The underlying reasons for these arguments involve, storage and processing costs, volume that presents a challenge to readability, and reuse of previous analysis is difficult.