Data quality is an important element that significantly impacts the success of any data program within an organisation. Often, entities overestimate the reliability of their data while underestimating the harmful effects of poor data quality. Data quality stands as a crucial element in ensuring that datasets meet various criteria, such as accuracy, completeness, uniqueness, and fitness for purpose. Ensuring good data quality is also crucial when it comes to AI and machine learning models.
These models rely on accurate data to predict things like future transaction volumes. However, if the input data contains errors, these models are likely to produce even more mistakes. The impact of subpar data quality extends beyond mere inconvenience and can lead to substantial financial losses, as per a Gartner report suggesting an average annual cost of USD 12.9 million due to poor data quality issues. Such statistics emphasise how important it is for organisations to prioritise data quality within their governance frameworks.
Integrating data quality governance into the broader spectrum of data lifecycle management, control mechanisms, and usage policies is crucial. It allows for proactive planning to address and mitigate the repercussions of inadequate data quality incidents. However, the success of these programs is closely linked to the quality of the data they manage. Organizations often overlook the fact that governance strategies should encompass not just the control and usage of data but also its quality.
To do this, data governance needs to include strategies for ensuring data quality. Things like deciding what data is most important, adding helpful details to the data, checking the data carefully, and keeping track of where it comes from are all important for making sure the data stays good within the governance ecosystem. Starting from the beginning of handling data, it’s important to focus on getting data ready by cleaning, clarifying, and making it error-free. Since different parts of a business might handle data differently, it’s crucial to have a balanced approach that considers both upstream and downstream cleanup efforts.
One of the key techniques is prioritization, tailoring data cleanup strategies to fit specific business goals and maintaining lineage tracking helps figure out important data sources, making it easier to use resources effectively.
Another important technique is annotation Establishing standardised methods to attach quality information to datasets makes it easier to check them quickly, allowing for gradual improvements and protecting important information from being lost.
Data profiling involves generating information about data values, like what values are there, what’s missing and determining data outlier. Techniques like data deduplication, resolving entity ambiguities in names and places and identifying and handling outliers contribute to improving data quality.
Data Deduplication
In a system that relies on numbers, each record should have only one voice. However, there are many cases in which the same record, or the same value, actually gets duplicated, resulting in data quality issues. Getting rid of duplicate data is super important for good data. Tools help find and fix repeated records, like names or addresses. Fixing these helps organize data better, making it easier to understand.
Addressing Outliers
Another approach is to identify outliers of the data early on and eliminate them coupled with addressing data completeness issues, are crucial steps. Managing missing data records or anomalies ensures the overall data is good and trustworthy.
Lineage Tracking
Lineage tracking for data is a force multiplier. If you can identify source datasets of high quality, you can use them to make decisions about the outcomes based on that great data. This is especially useful when combining different datasets. It helps solve conflicts and choose the best data. Data lineage tool should be robust enough like SCIKIQ which helps tracks from record to report data lineage at all levels.
Dimensions and Evaluation of Data Quality
The evaluation of data quality involves checking different aspects like completeness, uniqueness, validity, timeliness, accuracy, consistency, and fitness for purpose. These measures help understand how good the data is for specific needs.
Ensuring high data quality is fundamental for successful data governance programs. Strategies like prioritising, annotating, profiling, and managing data lineage significantly improve data quality, making it crucial for informed decision-making and achieving business objectives. Understanding the importance of data quality in complex data landscapes is pivotal for sustained success and competitive advantage.
Therefore, investing in robust data quality measures remains imperative for organisations to maximise their data-driven endeavours. Additionally, robust data quality management supports the integration of AI and automation technologies, influencing business decisions, workflows, and customer satisfaction. Ultimately, investing in data quality not only mitigates risks associated with poor data but also unlocks the potential for innovation and informed decision-making within organisations.
