Discover the foundational components of data quality, why it is important for your business, and how to better manage data quality.
Data quality is an integral part of data governance that ensures that your organization’s data is fit for purpose. It refers to the overall utility of a dataset and its ability to be easily processed and analyzed for other uses. Managing data quality dimensions such as completeness, conformity, consistency, accuracy, and integrity, helps your data governance, analytics, and AI/ML initiatives deliver reliably trustworthy results.
Quality data is useful data. To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies. Data that is not high quality can undergo data cleansing to raise its quality.
When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to cloud analytics, AI initiatives, business intelligence efforts, and other types of data analytics.
In addition to helping your organization extract more value from its data, the process of data quality management improves organizational efficiency and productivity, along with reducing the risks and costs associated with poor quality data. Data quality is, in short, the foundation of the trusted data that drives digital transformation—and a strategic investment in data quality will pay off repeatedly, in multiple use cases, across the enterprise.
Data quality activities involve data rationalization and validation. Data quality efforts are often needed while integrating disparate applications that occur during merger and acquisition activities, but also when siloed data systems within a single organization are brought together for the first time in a cloud data warehouse or data lake. Data quality is also critical to the efficiency of horizontal business applications such as enterprise resource planning (ERP) or customer relationship management (CRM).
The success of data quality management is measured by how confident you are in the accuracy of your analysis, how well the data supports various initiatives, and how quickly those initiatives deliver tangible strategic value (Want to assess your data quality ROI? Use our online calculator). To achieve all those goals, your data quality tools must be able to:
Data quality operates in six core dimensions:
All six of these dimensions of data quality are important, but your organization may need to emphasize some more than others to support specific use cases. For example, the pharmaceuticals industry requires accuracy, while financial services firms must prioritize validity.
Some data quality metrics are consistent across organizations and industries – for example, that customer billing and shipping information is accurate, that a website provides all the necessary details about products and services, and that employee records are up-to-date and correct.
Here are some examples related to different industries:
Healthcare organizations need complete, correct, unique patient records to drive proper treatment, fast and accurate billing, risk management, and more effective product pricing and sales.
The potential ramifications of poor data quality range from minor inconvenience to business failure. Data quality issues waste time, reduce productivity and drive up costs. They can also tarnish customer satisfaction, damage brand reputation, force an organization to pay heavy penalties for regulatory noncompliance—or even threaten the safety of customers or the public. Here are a few examples of companies that faced the consequences of data quality issues and found a way to address them:
You can only plan your data quality journey once you understand your starting point. To do that, you’ll need to assess the current state of your data: what you have, where it resides, its sensitivity, data relationships, and any quality issues it has.
The information you gather during the discovery phase shapes your decisions about the data quality measures you need and the rules you’ll create to achieve the desired end state. For example, you may need to cleanse and deduplicate data, standardize its format, or discard data from before a certain date. Note that this is a collaborative process between business and IT.
Once you’ve defined rules, you will integrate them into your data pipelines. Don’t get stuck in a silo; your data quality tools need to be integrated across all data sources and targets in order to remediate data quality across the entire organization.
Data quality is not a one-and-done exercise. To maintain it, you need to be able to monitor and report on all data quality processes continuously, on-premises and in the cloud, using dashboards, scorecards, and visualizations.
This storied Major League Baseball team relies on data to deliver richer ballpark experiences, maximize marketing opportunities for branded merchandise, and decide how best to invest in players, staff, and infrastructure. Using Informatica Data Quality lets the team cleanse and improve data from 24 on-premises and cloud systems as well as third parties so it can drive new revenue, make faster decisions, and build lifelong relationships with millions of fans around the world.
One of Singapore’s leading financial services and insurance firms, AIA Singapore deployed Informatica Data Quality to profile its data, track key performance indicators (KPIs), and perform remediation. Higher-quality data creates a deeper understanding of customer information and other critical business data, which in turn is helping the firm optimize sales, decision-making, and operational costs.
Data is everywhere and data quality is critical to making the most of it for everyone, everywhere. Keep these principles in mind as you work to improve your data quality:
All of these become much easier with Informatica’s integrated Intelligent Data Platform, which incorporates data quality into a broader infrastructure that touches all enterprise data.
