White Papers & Analyst Reports
Building a robust
business case for high quality master data - An Information
Difference White Paper
Projects addressing data quality or master data management frequently struggle to get approved by senior management, and only around 60% of such projects proceed with a proper business case - causing high rates of cancellation and failure.
This white paper explains the key elements for building a proper, quantified business case. It presents the measures that are favored by corporate finance departments, and helps develop a strong business case for data quality and MDM projects. A number of real-life examples with quantifiable benefits are also included.
Armed with the materials in this white paper, you will be in a good position to deliver a high quality business case for your data quality or MDM project!
The Information Difference is an analyst firm focusing on Master Data Management (MDM). Its founders are pioneers who helped shape the MDM industry, with in-depth MDM global project experience.
Overcoming objections for data
governance
Data governance is an outstanding business strategy that leads your company toward greater efficiency, lower risk and increased revenue. Proper management of data underpins the success many strategic initiatives. However, it is not always easy for others to see its value.
This white paper discusses the techniques that have been successful for data champions as they "sell" the importance of data governance to their company. It examines guidelines for optimal project selection, tracking the value of data governance and techniques for overcoming objections to a data governance program.
Leveraging open source data quality - Practical examples
Poor-quality data affects all data-related projects and refers to the state of completeness, validity, consistency, timeliness and accuracy that makes data appropriate for a specific use.
This Technical White Paper presents the main challenges of data quality and drills into specific uses of data profiling and data cleansing. It highlights, through 32 distinct use cases, how open source data quality technology can be used to alleviate issues related to poor-quality data.