What is a Data Warehouse?

One of the best ways to leverage intellectual capital is through a Data Warehouse. Intellectual Capital refers to the intangible stuff that creates value for a company, ranging from intuitive thinkers in the workplace to having great strategic partners within the value chain. Intellectual Capital is the main ingredient behind Market Value Added which is the additional value created over time above what was originally invested into the business. Therefore, it is imperative to find ways of expanding and leveraging intellectual capital. One way of doing this is to implement a Data Warehouse.

A Data Warehouse is a central location of data that combines all of the existing subsystems and legacy systems into one single structure. Applying this concept to the management of information is extremely important since most organizations have fragmented pockets or silos of information scattered all over the organization. When everything is located in one place, Manager's can capture all of the necessary information for decision making. Consequently, the main reason for building a data warehouse is to improve decision-making. The benefits of a data warehouse include better customer service, lower production costs, increased profitability, and quicker turnaround times for making decisions. One of the most powerful applications of a data warehouse is to engage in data mining. Take the following example on how to better understand the customer:

A clothing catalog company with over 2 million customers has decided to mine its databases and develop better customer groupings. Instead of coming up with the usual four or five segments, 5,000 different marketing cells were developed. For example, it was noticed that 850 customers had purchased a blue shirt and red neck tie. This data is important since these same customers are more likely to buy a navy blue jacket than the average customer. It may pay to send a special offer on navy blue jackets to these 850 customers. If the data is correct, the success rate could be as high as 10%.

One way a data warehouse can improve decision making is to improve the database itself. Business rules can be added to calculate valuable measurement information, such as inventory turnover rates or product margins. You can also eliminate dirty data during the course of building the Data Warehouse. For example, bad customer records, outdated names, and other bad data should be removed when building the Data Warehouse. Things can be done to make the data more user friendly. For example, the customer type code "599" doesn't mean anything, but if you change it to "NPO" it means nonprofit organization.

There are several approaches to building a data warehouse, such as virtual or dimensional. The dimensional approach seems to work best since it represents a true separate warehouse that is easy to navigate. Some other things to consider when building a data warehouse include:

- Design the Data Warehouse around the strategic goals and objectives of the organization.

- Don't throw all data into the Data Warehouse, load only the useful data.

- Get the best people you can find since design and implementation of a data warehouse is extremely difficult.

- Make sure you allow for growth - the data warehouse will need increasing storage, memory, etc.

This article has touched on the very high points in data warehousing. There is a lot more to cover, but the main point is to start moving in this direction since data warehousing is a powerful tool for leveraging intellectual capital.