Data Quality in context
Facts about data quality:
- Data quality is a measure of the reliability and effectiveness of data, especially in the context of decision making.
- High quality data is accurate, comparable, timely, useable and relevant.
- Decision making is in part linked to data analysis and therefore data quality.
- There are potentially significant legislative, commercial and competitive penalties relating to poor decision making.
- Data quality issues impact every level of the organisation:
- Data quality from a University perspective
- Industry Comment
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Data quality issues impact every level of the organisation:
Strategic level
- Less effective strategic business decisions
Tactical level
- Compromised decision making
- Inability to reengineer
- Mistrust between internal organisations
Operational level
- Customer dissatisfaction
- Increased costs
- Lowered employee job satisfaction and morale
- Loss of revenue
Data quality from a University perspective
At a Strategic / Tactical level these types are decisions are affected by the quality of the data available...
- what is the financial cost of running a course or program?
- what is cost of implementing a new International program?
- what is the cost of incorrectly forecasting demand for a program?
At an Operational level these types of actions create additional costs
- what is the cost of users keeping their own local version of data?
- what is the ongoing cost of staff, who may be inadequately trained, entering incorrect data?
- what is the rework cost of correcting incorrect data?
The importance of data quality is emphasised in the following comments from leaders in the business intelligence industry:
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"Data quality complacency is not an option. Over all sectors, 75% of companies are now reporting significant strategic, operational and financial costs resulting directly from defective data" Marcus Evans - Producers of global summits, strategic business conferences & corporate marketing events
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"Studies in cost analysis show that between 15% to > 20% of a company’s operating revenue is spent doing things to get around or fix data quality issues" Larry English, Information Impact International, Inc
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"Poor data is like a dirty windscreen. You can continue driving as your vision degrades, but at some point you must stop and clear the windscreen or risk everything" Ken Orr - Cutter Consortium
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"Every organisation has data quality issues, whether they know it or not" Dataflux Corporation - provider of Data Management solutions
- "Data quality must be visible and important to the enterprise – it’s not just an IT issue!" Dataflux Corporation - provider of Data Management solutions