The Myths Surrounding Data Quality
Debunking data quality myths
Data lies at the heart of the financial markets, providing the fuel for efficient and successful operations. Investors and financial firms with access to the best quality data – in terms of its completeness, timeliness, and accuracy – have an advantage over those less well informed.
The increasingly digitised capital markets have opened a plethora of new market data channels, which could potentially compromise data quality, control, and management across the industry unless they are professionally managed. Financial firms need to check the quality and provenance of this abundant market data and identify any risks and issues it poses, or risk facing operational disaster. This is a challenge as some financial instruments can generate thousands of market data points per day.
One way in which firms can optimise their use of data is by understanding the myths around data quality, which can help them to navigate the complex landscape of data quality management with a clear understanding and strategic approach. Data quality refers to the accuracy, completeness, reliability, relevance, and timeliness of the data.
There are several myths around data quality that need to be debunked, which are as follows:
Quantity over Quality: Having more data is more valuable and generally better for a firm than having less but higher quality data. In reality, a smaller but verified database containing quality data is more useful than a large, unverified one. The latter can lead to bad or misinformed decisions being made and lead to processing and storage challenges.
Ensuring Data Quality is Only IT’s Responsibility: There is a misconception that data quality is solely the responsibility of IT departments. However, maintaining data quality requires collaboration between IT, business stakeholders, data owners, data users and employees.
Data Quality is a One-Time Fix: The belief that data quality can be achieved through a one-time cleansing effort. However, data quality is an ongoing process that requires continuous monitoring and maintenance over time, with data becoming outdated and unusable.
Perfection is Essential: The assumption that data needs to be 100 per cent perfect to be used for analytics is a myth. In reality, a certain level of data quality is sufficient for most analytical purposes and firms should not waste time seeking to become too perfect in their quest to improve data quality.
Data is a Business Asset: The myth that data itself is a business asset. Data only becomes an asset when it is processed, managed efficiently, and is quality-assured.
Unprofitable Investment: There is a misconception that investing in data quality is not a profitable endeavour. While the benefits may not be immediate and the costs evident, ensuring data quality can improve business performance and decision-making in the long run.
Fixing Data Errors is Sufficient: The belief that improving data quality is solely about fixing erroneous data. In reality, it is about preventing errors from occurring in the first place through proper data generation and collection processes, ensuring that data sources are reputable and high quality. Firms should have a holistic overview of the provenance of their data, and understand where shoddy data may be stemming from.
Customer Experience is Unaffected: There is a myth that poor data quality does not significantly impact client experience. However, high-quality data enables better client understanding, personalisation, and service, and can be a key competitive advantage for a firm.
Without a deeper understanding of these myths, financial firms may unknowingly be shooting themselves in the foot when seeking to achieve an optimal level of data quality. It is important not to forget that accurate, quality data is crucial for compliance reporting, and failure to maintain data quality can result in financial penalties and damaging reputational consequences. Regulatory bodies rely on this data to assess firm's compliance.
Firms can address these data quality myths in several ways, including:
Establishing Data Governance: Implementing a robust data governance framework that clearly defines roles, responsibilities, policies, and processes for data quality management. This ensures accountability and a shared understanding among employees.
Invest in Tools and Resources: Allocate appropriate resources, including data quality tools, software, and skilled personnel to effectively manage and improve data quality. A combination of automation and manual intervention will likely be required.
Measure and Report: Establish data quality metrics and regularly measure and report on data quality levels to stakeholders. This helps quantify the impact of data quality and drive improvement efforts.
Debunking and addressing data quality myths can undoubtedly put financial firms at an advantage when navigating this digital and data-driven age. The importance of data quality in ensuring a firm’s financial, regulatory and reputational progress cannot be downplayed.