When your business invests in an effective enterprise data management (EDM) solution, you will gain a core advantage of holding your critical data in one manageable location. In a time where the vast quantity of data typically found in asset management firms, these systems are becoming more critical than ever.
However, to truly harness that data and use it to guide meaningful decisions within your organisation, you need a clear idea of its quality.
Poor data quality can lead to significant issues if it is used to guide a company’s direction. If it is outdated, inaccurate, ambiguous or incomplete, this can subsequently result in wayward strategies, as well as data not complying with regulatory standards and a loss of trust from your customers and other audiences.
This is where data quality metrics become fundamentally important. Here, we outline what these are, why they are essential when using an EDM, and examples of how these can be applied.
What are data quality metrics?
Data quality metrics determine whether data is fit for purpose, and measure how the quality of data changes over time. These metrics should track the range of characteristics that can denote data quality, which include:
- Consistency – a lack of contradictions throughout your data sets
- Accuracy – no errors throughout your data
- Completeness – all data is collated and available with no “holes” in it
- Auditability – your data is accessible and its records can be traced back
- Integrity – the data is displayed in the same structure and format (e.g. all dates appear as DD/MM/YYYY)
- Timeliness – the data is representative of current conditions and is not out-of-date
- Uniqueness – there are not duplications throughout the held data
- Distribution – is there anything preventing data being published and passed onto relevant individuals?
By using data quality metrics to measure your information, you can assess whether your data meets your requirements for compliance, and if it is viable for the processes and data consumers that support your business operations.
How do data quality metrics work in practice?
In practice, data quality metrics are there to support the responsibilities of an organisation’s data managers and stewards. Their duty is to ensure that data collected is compliant, accurate and usable, and this can be incredibly challenging to determine without access to metrics that can judge the attributes highlighted above.
Without these metrics, data can only be assessed on a qualitative basis. This can be enough to inform a data manager that 90% of the information in a data set is accurate or valid, and that 10% of it isn’t. But, it can’t give you insight into what state this 10% is in – it could be adequate, or it could be really bad – if these metrics aren’t applied, you can’t tell.
Furthermore, qualitative data analysis is typically more manual and subjective than the application of stringent quantitative metrics. This adds a greater margin of error relating to the quality of data within an organisation, and differences of opinion over whether data collection processes are in need of improvement.
As we’ve already alluded to, poor data can be extremely costly and detrimental to business development, with Gartner estimating it costs the average organisation over £7.7 million annually.
This is why data quality metrics are so crucial. Now data-driven decision-making is widely considered the way for companies to pursue growth, streamline processes and meet the needs of their various audiences, these metrics lend much-needed support to the data managers and stewards on the front lines by providing a clear sense of the value of their stored data.
Of course, it is not enough for metrics to just provide a one-off snapshot of data quality – it needs to analyse this over time, and illustrate if data quality is improving or deteriorating. They should also provide information that can flow into a security master analysis, allowing for a comparison between a firm’s fixed-income data and its equities data.
Fundamentally, these metrics are vital in identifying any issues of how data is collected and if any data is not of the standard required to be useful to your organisation. Without these benchmarks in place, you risk taking unwise steps under the guidance of inaccurate or unreliable information.
5 examples of key data quality metrics
A few standout examples of data quality metrics include:
- The ratio of data to errors – measuring the number of known errors in a data set with the data as a whole, helping you establish whether data quality as a whole is improving
- Data transformation error rates – this indicates if there are any problems when data is transferred from one format to another, which can then guide improvements in overall data quality going forward
- Records with delayed changes – this metric can illustrate how timely your data is by recording how often it is updated, and highlight if any data is outdated before it is used in relation to your strategies
- Amounts of dark data – dark data cannot be used effectively due to quality problems, and so assessing how much of this you hold will give you a strong understanding of your data quality
- Data time-to-value – a calculation of how often it typically takes to derive results from a data set, and how this influences the quality of data to hand and how readily it can be put to use in your organisation
If these and other metrics are measured over time, it offers firms a clear understanding of their data’s quality and whether this is in need of improvement. This then allows the business to:
- Identify problematic or troublesome areas in their data sets or in their processes for collecting data
- Manage data vendor relationships and recognise if vendors are providing the quality that you expect
- Budget time and staff resources more appropriately in accordance with the data at hand once it has sufficiently improved
With data quality metrics supporting greater transparency and understanding of the data that an organisation has available, teams can feel reassured when benchmarking performance and guiding changes to make their operations more cost-effective, efficient and reliable.
Benefitting from the value of quality data
We hope this has explained why data quality metrics are so important in the pursuit of ensuring that your business strategies and operations are guided by the best available data.
With the information that companies can glean from their data critical in maintaining a competitive edge and supporting the needs of clients and customers, these metrics play an all-important role in ensuring they’re led by data that has real value.
At Fundipedia, we recognise that access to quality, accurate data is no longer optional. That’s why we work closely with asset management firms to introduce software that allows them to harness their data like never before, from capturing data from a variety of sources, to governing, distributing and reconciling this in a secure, compliant manner.
If you’d like to know more about our end-to-end solution for your fund data, get in touch with us today for a free consultation.