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built on the wrong data, or on out-of-date or incomplete data, the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
- 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.
- Establish a data quality “firewall” … to
recognize data quality issues in incoming data and block low-quality
data from entering your data warehouse.
- Implement a process at the back end for auditing and verifying
BI IT Solution
- 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.
- 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.
- 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.
- 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.
Program Business Mistake 2 - Spreadsheets
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