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Data Warehousing and Archive Management There sources for this module's Case are: Mailvaganam, Hari (2011). Data warehouse review: Introduction to project management. http://www.dwreview.com/Articles/
Data Warehousing and Archive Management
There sources for this module's Case are:
Mailvaganam, Hari (2011). Data warehouse review: Introduction to project management. http://www.dwreview.com/Articles/Project_Management.html
Dyche, Jill (2011) The politics of data warehousing, http://www.taborcommunications.com/dsstar/00/0208/101300.html
Data warehousing IS politics in action. Kurt Thearling has written:
The design of the data architecture is probably the most critical part of a data warehousing project. The key is to plan for growth and change, as opposed to trying to design the perfect system from the start. The design of the data architecture involves understanding all of the data and how different pieces are related. For example, payroll data might be related to sales data by the ID of the salesperson, while the sales data might be related to customers by the customer ID. By connecting these two relationships, payroll data could be related to customers (e.g., which employees have ties to which customers).
Once the data architecture has been designed, you can then consider the kinds of reports that you are interested in. You might want to see a breakdown of employees by region, or a ranked list of customers by revenue. These kinds of reports are fairly simple. The power of a data warehouse becomes more obvious when you want to look at links between data associated with disparate parts of an organization (e.g., HR, accounts payable, and project management).
Consider an exception report showing all projects more than 90 days in arrears that are managed by someone with less than two years of experience. This report would be nearly impossible to generate without the links between different databases that the warehouse provides. In addition to the capability to link data together, a data warehouse can give users the ability to view data at different levels of aggregation.
You might start out looking at the total number of employees with Ph.D. across the company. The next step might be to drill down into the sales organization, to see how many people there have a Ph.D. (and so on, deeper into the company). This aggregation of data would be automatically handled by the reporting software using the raw data contained in the warehouse. Typically organizations that benefit from data warehousing have grown to the point where they are no longer able to answer the business questions that they are interested in. This usually happens because both the data volume and question complexity have grown beyond what the current systems can handle. At that point, the business becomes limited by the information that users can reasonably extract from the data system.
That being said, most decisions to build data warehouses are driven by non-HR needs. Over the past decade, back office (supply chain) and front office (sales and marketing) organizations have spearheaded the creation of large corporate data warehouses. Improving the efficiency of the supply chain and competition for customers rely on the tactical uses that a data warehouse can provide. The key for other organizations, including HR, is to be involved in the creation of the warehouse so that their needs can be met by any resulting system. [Thearling, Data Warehousing]
Thus, it's clear that this topic can't be ignored.
As noted earlier, you should consult material from the Background Readings for an in-depth description of data warehousing and the politics of data warehousing or related other materials you find yourself (be sure to reference properly whatever specific sources you draw on).
Case Assignment
When you've read through the articles and related material, scanned the websites, and thought about it carefully, please compose a short (5- to 7-page) paper on the topic noted above -- that is:
To what degree is it possible or desirable to separate the technical issues involved in creating and managing data warehousing from the political issues (i.e., the distribution of costs and benefits to different components)?
