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.

Specific examples of how business intelligence can benefit asset managers include the ability to identify hot spots and trends in data quality on a particular data set so you can then prioritise your data cleansing and process efforts effectively.  The capture of root cause information and closure reasons over data issues can uncover potential structural issues that can be addressed hopefully resulting in a sustainable improvement of data quality at source and reducing the cost burden of poor data within the business.

Having business intelligence embedded as part of the data management process can help the firm identify potential for addressing issues both in process and/or in systems that can result in improved efficiencies in the data management operations.  It can even help to decide whether to source new suppliers for certain data types based on the insights gained.

With BI, firms can also examine performance and progress in data quality over time.  BI gives the ability to demonstrate the effectiveness of controls and policies that exist over data quality both internally and to external third parties.  Only when you have visibility and controls over data quality are you able to have control over your data governance processes.

Many firms already have some level of business intelligence capabilities or tools in place, but often these require specialist IT skills and resources to implement and maintain.  The future of effectively leveraging business intelligence is to empower the data management team within the existing data quality management process by providing BI tools that can be made available on a self-service basis, that are easily configurable and enable them to deploy the analysis back to the parts of the organization that need to understand the impact of data quality.  Being able to gather this intelligence as an integral part of the daily process of managing data quality, rather than an after the event exercise in a separate BI tool, is what many analysts and technologists are now referring to as Embedded Analytics.  The advantage of this embedded approach is that the firm gets data quality business intelligence out of the box with little or no extra effort.

The new Curium Business Intelligence toolset, CuriumBI is a web based module and available in v6 of the Curium data management platform.  To find out more click here.