Discover the importance of data quality in business intelligence (BI) decision-making. Learn how statistics can be twisted to prove any point, making data integrity crucial. Explore the impact of poor-quality data on strategic decisions and find out how data profiling can improve key decision-making. Join us at Data Quality Matters to unlock the value of…


“Lies, damn lies and statistics”

lies, damn lies and statistics

As Mark Twain is famous for saying, statistics can be twisted to prove any point.

For many managers, BI applications are a critical decision-making tool. The statistics, trends and analytics presented are used to determine future actions and to track historic performance.

For this reason, improved BI outcomes must form part of any data quality business case.

Poor quality data makes decision-making a lottery

There are two significant ways in which poor-quality data can make this kind of decision-making a lottery.

Most visibly, a manager may be faced with two sets of reports, about the same business area but from different systems. These may provide contradictory analysis – one suggests investing more in a region and the other suggests pulling out.

Which does she believe? She has to make a strategic decision that could cost the business millions and cost her her job!

Possibly of more concern, the reports may show a clear leaning toward a particular position – but this may be based on incomplete or inaccurate underlying data.

One of my clients showed a report indicating a 87% compliance with a key supply chain metric. On investigation we found that the underlying data was only populated for 17% of the records – in fact, they had no idea whether or not they were compliant.

Are they making good decisions?

Often, things don’t go as planned: why does business intelligence fail? Unravelling the reasons behind these failures is crucial for future success.

Ever wondered how corrupted data destroys analytics? The answer lies in understanding its insidious effects.

Assess data quality

Many organisations are using data profiling applications to test whether the underlying data supports the reported findings. An increased understanding of data quality issues means two things:

1.) You can make better-informed decisions using your confidence in the underlying data to weight reported metrics.

2.) Steps can be taken to address data quality problems that are critically affecting your business.

Improving your key decision-making must be a good idea!

Response to “Data quality critical for BI”

  1. “We hold these truths to be self-evident” (or do you trust your data?) | Data Quality Matters

    […] Is the quality of my source data adequate? There is no point in reporting on “Sales per Quarter” if you are not capturing the date of sale for many of your records. Data quality measures will give you an indication of the degree of confidence with which other metrics can be used, as discussed in Lies, Damned Lies and Statistics […]

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