Professional Services

Data Analysis & Data Quality

“Poor data quality is the norm rather than the exception, but most organizations are in a state of denial about this issue.”

Gartner Group

While organisations today are being enabled to make fast decisions, we frequently find that those decisions are being made using inaccurate data. MIP enables organisations to collect, organise and act upon information that is based on accurate data.

Maintaining quality data across the diverse set of transaction, decision, and collaborative processing applications that exist in most organisations today is not a simple task, and the corporate mergers, acquisitions, and reorganizations that occur in many corporates exacerbate the problem. New IT data integration and migration projects designed to provide business users with consistent and clean business information often fail because the IT organisation cannot handle, in a cost effective and timely manner, the complex data quality issues involved. These issues frequently arise because the project is undertaken without a clear understanding of the source data that has to be extracted and loaded into the new system.

Data quality is a key success factor in CRM and other Systems Projects. At any given time, according to industry analyst estimates, roughly two-thirds of the Global 2000 are engaged in some form of data migration or data integration project. Concurrently, the same industry analysts’ report that upward of eighty-eight percent of these migration projects either overrun or fail. They often:

• Exceed the planned delivery date
• Overrun their budget
• Are not used due to a perception of lack of quality data, user confidence goes down.
• Are cancelled before they are completed from a combination of the above issues.

At MIP we believe that if you cannot measure your data quality you cannot manage your data. The process of information quality improvement is one of continuous process improvement of any and all processes, to eliminate the causes of defective data.

“One of our key criteria for MIP was to ensure a high degree of knowledge transfer during the data warehouse implementation project. They were extremely successful in achieving this and we are now reasonably self-sufficient, although we will continue to use MIP for quality assurance and expert consulting as needed, as well as to assist us in reviewing outputs.”

Rick Vosila, Chief Information Officer, Unilever