Creating dashboards with scorecards, reports, graphs, interactive maps, and other elements is fun when your data is ready for analyses. Unfortunately, most of the time in a business intelligence project you deal with suboptimal datasets. Typically, the data quality does not meet your expectations. Especially important is to have a good data quality for the most important data in an enterprise, the master data.
In order to create meaningful analyses, you need good data. Raising the data quality and maintaining the master data should be a part of a successful business intelligence project. Including the data issues in a project's lifecycle means creating a viable business intelligence solution delivery framework, no matter if this is a self-service or an enterprise-level centralized implementation.
Planning a Self-Service Delivery Framework
Maintaining data quality requires good planning for the self-service delivery framework. Included among the information you may need to identify is the following:
- The part of the data in your organization constituting the master data
- The kind of data quality issues that exist in your data
- Who the key people and roles are that manage the most important datasets
- Whether you have appropriate software in place, or whether you need to introduce some new applications specialized for resolving data quality issues and maintaining the master data
Of course, you should also consider the time ...