Beheersing van datakwaliteit, hoe doe je dat?
Data quality is an important topic in the financial sector. Regulations require demonstrably good quality data. In addition, it is crucial in order to gain reliable insights into yield and risk, and for management information.
Financial institutions often struggle with data quality and run into the following questions:
- What are the requirements that apply to data quality?
- How can you measure data quality?
- Who owns the data within your organisation?
- How can you make the quality of the data traceable?
- How can you guarantee the data quality of external parties like proxy holders or the custodian?
The challenge is to tackle data quality in a practical and effective way.
Step 1 Start with a practical policy
Data quality starts with the definition of a data quality policy. This states the strategy and targets you want to achieve for data quality. That includes defining the risk appetite for data quality. Important aspects of the policy are the materiality of data quality and the way in which the policy can be applied given the type and size of the organisation.
Data quality is not something IT takes care of, nor something that Finance or Risk look after. Data quality involves the whole organisation. This means that the policy needs to be approved and supported by the top management.
Step 2 Practical implementation
The best way to improve and maintain data quality is to introduce a data quality cycle.
This spells out its entire path through the company, before it finally shows up in the annual accounts, the management dashboard or the regulatory reports. Plenty can go wrong along the way. Important parts of the data quality cycle are visibility of lineage (data flow), preparing a risk analysis and having adequate management measures.
Read more about our approach to data quality and our way of working.
How can we help?
We can get you off to a flying start by providing:
- Workshops in which the approach and documentation is spelled out.
- Templates for all the documents that need to be created, including a well thought-out data quality policy.
- The one-step (or if there is insufficient capacity, staged) documentation of data flows, including the design of the management measures.
- Quick scan and action plan to expose gaps and improve data quality.
Why Sofia Consulting?
- We take a practical approach to data quality that we have successfully implemented with a number of insurance companies.
- Using this approach will give you a head start and you will not need to reinvent the wheel.
- Combine in-depth financial and actuarial knowledge with knowledge of both regulations and available tools.