The definition of business intelligence is “the transformation of raw data into meaningful and actionable information to improve your business.” Asset managers are now turning their attention towards leveraging data quality information with business intelligence to underpin their data governance efforts.
For those institutions working on process improvements around the capture, investigation and resolution of data quality issues, one of the key outputs should be the accumulation of intelligence about data quality. Collecting not just the raw data quality metrics such as the number of gaps, differences, errors and so on but process and management information as well. For example, how often do we see a particular problem? How long does it take us to fix it? Is the data quality improving over time and where does the bad data come from in the first place? Being able to provide answers to these intelligent questions requires a level of analytics embedded within the day to day operational processes around data quality management and the tools used to support those processes.
The benefits of applying business intelligence over data quality information are broad. Such insights can empower the firm to make better and faster decisions, identify possible areas for cost savings, provide insights into data quality, and help to align the organisation towards the broader data management goals. This level of visibility is very useful for your internal teams across data management, data governance, compliance and more. But it can also be valuable as a means of communicating performance and achievements to senior management, or indeed, highlighting problem areas to justify further development, as well as to clients, auditors and regulators who show an interest in your data governance capabilities.
Firms who are not leveraging such management information often find that they have no clarity about what is working and not working within their data quality and governance programmes. They can’t see the metrics for measuring progress and driving change, and find it more challenging to implement effective data governance.
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