--------Data Quality

Research by PricewaterhouseCoopers highlights a profound gap between the clear understanding that data is valuable versus the real-world usage of that data in delivering value. Over 70% of executives polled consider data to be one of their most valuable assets. Yet only 40% say that they use it effectively. This suggests that there is a gap between the perceived value of data and the real value being obtained from data.

Building trusted information is an BI best practice. Organizations build and maintain trusted data at every step in the data supply chain. The concept of the data supply chain has no greater relevance than in the BI context.

Based on our experience , we can see data quality operations exist at every major stage in the chain. Each stage is an opportunity to create, enhance, or just maintain the level of trust in the data. The sooner data quality issues are corrected in the chain, the sooner the firm benefits from greater trust. For example, validating and standardizing data at the initial point of contact with the customer, such as a website where they can enter their information, benefits every downstream operation no matter how far-reaching the enterprise. You can multiply the benefit by the count of all the subsequent operations that use the data.

Conversely, the longer an organization waits to cleanse and improve data integrity, the more upfront operations are sub-optimized because of data defects impacting their effectiveness. Moreover, the earlier the data is cleansed, the less the cleansing costs later on. The reason is the count, type, and most importantly, complexity of data quality problems are less. Rather than letting problems build up to the point where correcting them in the data warehouse becomes a large task, tackling the issues as they arise makes each operation simpler.

EIS believes in following the incremental improvement approach, data quality operations lend themselves to pilot project implementations. The success of each pilot is used to build out the data quality infrastructure as part of the overall BI strategy.