What is data quality?
Data is a key foundational pillar organizations need to build their evolving operations. However, bad data—information that can’t be trusted—has the power to do more harm than good, with consequences that are both immediate and long lasting.
That’s why today’s companies are focusing on data quality. By understanding how to identify bad data and methods to improve it, your organization has a chance to leverage good data to its full potential.
What is data quality?
Data quality refers to how well a dataset meets certain criteria that are important for its intended purpose.
Quality data is mission critical to all data-driven initiatives within an organization. Many enterprises are adopting strict data governance standards to ensure all lines of business have access to and are leveraging the best, most accurate information available.
Ultimately, quality is also a measure of trustworthiness. The more your stakeholders trust their data, the more likely they are to use it in support of key business objectives. Quality information also generates meaningful insights your business can use to inform decision-making, navigate obstacles, and boost long-term growth and profitability.
The cost of poor data quality
Data issues—redundancies, missing values, outliers, or outdated information—make your dataset less reliable, therefore rendering it lower quality. If these errors aren’t properly addressed, they increase the chances you’ll encounter negative business results down the road.
Consider how important information is to your organization. Data flows through almost every business function, so if issues aren’t resolved quickly, they could significantly impact operations—and worse yet, cost your company substantially.
According to Gartner’s most recent estimates, poor data quality costs organizations nearly $13 million annually. Aside from its immediate effect on revenue, low-quality data also hinders long-term decision-making and disrupts the entire data ecosystem. This is a big reason why data is often blamed for ill-conceived business strategies or operational inefficiencies.
Of course, data quality is especially important for companies that integrate artificial intelligence (AI) into their daily operations. AI models are trained on data—so, if the information isn’t up to standard, the algorithm won’t perform as well as intended.
Perhaps one of the most well-known examples of AI-related data failure is Zillow’s “data science disaster.” In 2021, the real estate company used a machine learning (ML) algorithm to predict home prices on houses it bought that year, hoping to flip them for a profit. However, Zillow’s models ran on incomplete datasets that didn’t factor key variables into the equation. Ultimately, the company purchased more homes than it could sell, resulting in a $300 million loss in a single quarter. Worse yet, the incident forced Zillow to lay off 25% of its workforce.
Dimensions of data quality
Not all datasets are created equal. Some are fit for purpose, but others have a long way to go before they’re ready for application. Altogether, there are six essential characteristics that determine data quality:
Data accuracy is the level to which data represents the true value of the attribute it’s intended to measure. Simply put, accurate data correctly reflects the real-world element it was designed to capture.
For instance, healthcare organizations rely on medical records to ensure precise recording of patient histories, allergies, medications, and other essential factors. Likewise, marketers require accurate customer data to send targeted, personalized messages to the right consumers.
Regardless of the application, accuracy is a cornerstone of effective analytics. It ensures information is trustworthy, reliable, and suitable enough to inform decisions and fulfill its purpose.
Data completeness refers to the extent to which a dataset contains all necessary information for a specific purpose. In other words, a complete dataset gives you the full picture, whereas an incomplete one only tells you part of the story.
Missing, incorrect, or incomplete entries will compromise the accuracy of data analysis and skew interpretation. Consequently, this may lead to poor decision-making, operational inefficiencies, and inaccurate insights. What’s also important to note is that some industries are subject to strict regulations related to data reporting. Failure to maintain complete databases increases your risk of legal issues, compliance fines, and reputational damage.
Data consistency refers to data that’s uniform and coherent across multiple databases, systems, and applications within an organization. In other words, it ensures information remains the same regardless of how or where it’s accessed.
Many organizations have trouble keeping track of data as it flows across lines of business and through different platforms. Like a game of telephone, it’s easy for datasets to fall out of sync when they’re passed from one department silo to another. This complicates numerous business processes, including forecasting, marketing, compliance, collaboration, and more. As data sources grow and diversify, it becomes increasingly important to integrate information into a single, unified view.
Timeliness is the extent to which data is up-to-date and available at the right time (i.e., when it’s needed to fulfill its intended purpose). Information that’s collected relatively recently may be considered timely, unless new data comes in that renders it obsolete.
Timeliness is especially important to data quality, as outdated information could lead someone to make an ill-informed decision. For example, investors rely on having the latest, most timely stock prices available to make trades as profitable as possible. But if they’re using old data, they may miss out on a big opportunity that costs them money.
Data validity refers to whether the data adheres to formats, values, and rules that are appropriate for the context in which it’s to be applied. Valid data reflects its real-world counterpart, which could include an attribute, event, or entity.
Invalidity issues arise for many reasons, most notably because of data entry errors, system glitches, or falsification. When information is meant to follow a specific format, but it doesn't, it’s considered invalid.
For instance, a database of ZIP codes is only valid if it contains the correct characters — numbers, not letters. But, if they’re entered incorrectly or duplicated, the data becomes invalid.
Lastly, uniqueness is an indication that all data entities are represented by a single instance. In short, it’s about identifying and eliminating duplicate entries so that information is recorded only once.
This is key to preventing confusion, miscalculation, and misinterpretation. Many organizations with a mature data governance strategy practice data cleansing—the process of detecting and correcting or removing inaccurate records from a database. It may also involve correcting improperly formatted or duplicate data entities.
Benefits of data quality
Organizations that invest in creating and maintaining quality data put themselves at a significant competitive advantage compared to those that don’t.
Here are some of the most substantial benefits related to data quality:
- Improved decision-making. Organizations with high-quality information can make tough choices with confidence. Better yet, they make decisions that align well with both short- and long-term goals. This leads to increased profitability, enterprise resilience, and more effective business performance.
- Better collaboration. Maintaining a consistent database empowers all teams to access the same information. With a single source of truth across the organization, employees can stay on the same page, remain aligned, and focus on a common goal.
- Enhanced customer experience. Quality data champions accuracy, enabling sales, marketing, and product teams to deliver an unparalleled customer experience. For instance, marketers can leverage customer profiles to personalize interactions across multiple channels, carrying information over from one platform to another. With rich insight into target buyers, organizations can sell products and services more effectively, optimize budgets, and plan better campaigns.
- Greater efficiency. High-quality data breaks down silos and ensures the latest, most relevant information is readily available. That means employees can quickly retrieve data and confidently use it to their advantage, whatever the application may be. This is particularly helpful when it comes to gathering insights, as quality information is much easier to process and analyze.
- Increased AI capabilities. Good data is the cornerstone of any AI application. Prioritizing governance and maintaining quality datasets ensures your algorithms have a healthy stream of information to feed on, allowing them to train and perform as effectively as possible.
Tools for improving data quality
Maintaining data quality isn’t an easy task. With six dimensions to manage, it can involve a lot of heavy lifting. As a result, many data quality tools have emerged from the woodwork to help enterprises simplify the process.
In short, data quality tools are technologies for identifying, understanding, and correcting flaws that hinder enterprise performance and decision-making. When governance standards aren’t met, tools support businesses by diagnosing underlying problems. This empowers teams to rectify issues quickly before they create a bigger conundrum.
And, with the rise of big data, such tools are taking on a much larger role than ever before. In fact, Gartner estimates that 50% of organizations will implement data quality solutions to better support their digital initiatives in 2024.
A comprehensive tool covers a wide range of data quality, management, and governance tasks, such as:
- Data cleansing. Solutions prepare data by removing or correcting anomalies, improperly formatted entries, and duplicate information.
- Data integration. A robust data quality tool helps consolidate information from multiple data sources into one master record.
- Big data processing. Large volumes of data may be structured, unstructured, or mixed. Data quality tools clean and process this information to make it searchable, usable, and reliable.
Data quality monitoring tools are particularly useful, as they automatically analyze and detect errors within a given dataset.
Data quality vs. data profiling
Data quality and data profiling are two halves of the same coin, but not exactly the same.
Consider data quality a broader category of criteria organizations use to evaluate their data’s utility. It involves ensuring information is free of errors to make it as useful as possible.
By contrast, data profiling is a process of analyzing data to understand how relevant it is or how it can be improved. Profiling data involves looking at the structure and content of a dataset to identify patterns, outliers, and anomalies.
Essentially, profiling is a workflow for identifying areas of improvement. In other words, it supports the overall data quality management process by pinpointing exactly what must be done to make the data as accurate, timely, consistent, valid, complete, and unique as possible.
Data quality best practices
Although there’s no one-size-fits-all approach to data quality, there are several best practices that will help you proactively and collaboratively manage your data.
Here are a few of the most essential:
- Make data quality a company-wide priority. If only part of your team is committed to quality data, you can’t expect your efforts to be very successful. All stakeholders, from top to bottom, must understand and champion their responsibilities. To win executive buy-in, it helps to have a C-suite sponsor advocating on behalf of your initiative. Plus, if you lead from the top, everyone else is more likely to follow.
- Eliminate information silos. With siloed data, it’s practically impossible to glean a holistic view of your business. Integrating data across disparate systems will help create a single source of truth, making it much easier to identify errors and inconsistencies.
- Think of data quality as a continuous process. Simply put, you can’t “set and forget” data quality. You must constantly cleanse, maintain, and improve information workflows to uphold the highest standards—otherwise, quality is sure to fall by the wayside.
- Implement data governance policies. Beyond rules and requirements, governance is a collection of procedures, roles, standards, and metrics that ensure effective and efficient use of information. Deploying and enforcing well-crafted policies will ensure everyone in the company promotes a culture of high-quality data.
- Automate data streams. One way to streamline data integration is to use a comprehensive tool that automates the data pipeline. This not only accelerates cleansing but also significantly reduces latency, allowing you to produce high-quality and real-time analytics.
Future-ready data management
Inadequate data poses a business risk and seriously hinders long-term growth and sustainability. According to McKinsey, 60% of tech executives highlight poor data quality as the primary roadblock to scaling their data-driven operations.
Indeed, quality data is the bedrock that forward-thinking organizations need to evolve. Whether it’s AI, automation, or another digital initiative, your company’s future depends on valuable insights—the type that only high-quality data provides.
Technologies like Teradata VantageCloud can help you make the most of your enterprise intelligence. By leveraging cloud scalability and the flexibility to process, integrate, and analyze data at scale, VantageCloud empowers you to unlock your data’s true potential.