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Experian’s powerful profiling capabilities enable rapid landscape analysis, providing intelligent and actionable insights within minutes of installation.
If you need some help our expert consultants will be on hand to help you meet your objectives and improve the data maturity of your organisation.
Role-based permissions and obfuscation mean once you’ve found and assigned value to your mission-critical data assets, you can control who has access to it and protect it.
Why conduct a Crown Jewels Analysis?
- Quickly and easily find your most important data
- No reliance on IT
- Assign risk, business and monetary value to your data
- Apply contextual metadata to your Crown Jewels
- Effectively monitor and report on the quality – and therefore safety – of your Crown Jewels
- Discover relationships between disparate datasets and bring them together in one place
- Conduct Content, Format and Pattern Analysis in a few clicks
Crown Jewels Analysis in action
- Banking-Supporting a progressive data quality and governance program
The siloed nature of the bank’s separate business areas meant that the key challenge for the business lay in having a consistent view of all its data. It found that whilst the data within each department was actually fit for everyday operational needs, it lacked a wider view that is important for cross-organisational functions such as HR, Finance and Marketing. This view is absolutely critical to enable them to deliver key strategic activity such as regulation, compliance, reporting and customer interactions. One such example is Bank of England reporting where there is a requirement to report by industry classification and region.
The bank, therefore, initiated a data governance and quality program that would combine all the data within the business to make it accurate and consistent for use by business-wide applications, now and in future.
Technology was top of the list in terms of enablers of the project. The bank needed a solution that would give them the functionality to support the necessary reporting and quality. The biggest challenge would be changing the mindset of each business area – meaning the technology had to be simple enough for non-technical business users.
The bank defined nine key competencies to create a roadmap of initiatives to move towards an optimised approach, including:
- Policy – a framework that covers all electronic information and has buy-in from the very top of the business.
- Stewardship roles – the introduction of data quality stewards within each business area who understand their own data and processes.
- Data quality management – a set of processes to allow the stewards and central data governance team to discover, identify, manage, and report on data quality issues.
- Reporting – regular internal data quality reporting upwards to the board around the status and successes of the program.
Experian's technology was chosen as the most appropriate data quality tool to power the program. With functionality that supports data governance and quality initiatives, it offers agile, sophisticated functionality that makes complex data analysis straightforward and supports the creation of data rules to run the reporting that the bank needs to monitor long-term data quality thresholds. In addition, it can take in business data and PAF files and make sure that addresses are correct, in the right fields and that correct SIC codes are logged for reporting and compliance purposes.
Experian's ease of use was a key factor in the choice because it is accessible and simple to use for the non-technical users who form part of the council and are involved in reporting.
The bank’s data governance and quality program illustrate how a forward-thinking organisation can reap the rewards of implementing a well-defined and managed program.
Experian sits at its heart and the business-user lead, rules-based functionality has allowed them to successfully deliver on its requirements. They use Experian to analyse the data and find out overall levels of data quality which can be compared to overall risk appetite, information which has allowed them to prove value and grow the business case for data quality.
The bank now has a progressive data quality program in place and is moving steadily up the data quality maturity curve. Using Experian, it is meeting key objectives including:
- Increased trust and confidence in data
- Increased value from data
- Ability to exploit data more effectively to earn more income and revenue and profit
- Ability to contain and manage operational risk - reputation, financial loss, non-compliance and impact on the customer.
Find out more about what our data quality management platform offers