BI Program Mistakes - Ignoring Data Quality

Not Facing Up To Data Problems

Most companies believe their data quality is acceptable, regardless of how many disparate storage silos it is distributed amongst. They accept the limited of the occassional bad decision, made in isolation, believe it is unlikely to bring the company to its knees.

However, bad decisions made every day at key points in a business process is a serious business problem. Whilst a single bad decision may only result in acute, short-term pain, the compounding effect of systemic poor decision-making erodes long term corporate performance.

BI relies on accurate data. If an enterprise BI application is built on the wrong data, or on out-of-date or incomplete data, the value of the system is compromised long before information reaches the business user.

Compliance demands have led organizations to review the quality of their data in a one shot program. Few IT departments formally manage data quality on an ongoing basis.

BI Business Solution

1. Business leaders must be made aware of the unexpected and sometimes disastrous effects that poor data quality can have on business results and key strategic initiatives.
2. Establish a data quality “firewall” … to recognize data quality issues in incoming data and block low-quality data from entering your data warehouse.
3. Implement a process at the back end for auditing and verifying the data.

BI IT Solution

1. Overcome issues with disparate data sources by standardizing business intelligence on a solution that supports an open data strategy and that uses a common metadata model.
2. Address data using business input to establish which data is the right data and how to define it. This collaboration is the only was to ensure the right data assets are available to BI applications.
3. Implement IT capabilitys [Enterprise Information Integration (EII), and Extract, Transform, and Load (ETL)] to provide direct access to data and a common metadata model that ensures consistent business rules, dimensions, and calculations across all sources and all BI capabilities. These IT tools ensure data integrity across user groups, BI capabilities, and geographic locations.
4. Use a modular deployment, to stage data quality resolution and provide manageable feedback loops for users to ensure data is accurate and consistent at every stage. A phased approach allows IT to address data quality issues at the source, before the information is delivered to end users.

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