What is Data Quality?
Data quality is defined as the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency.
It is a critical aspect of data management, ensuring that the data used for analysis, reporting, and decision-making is reliable and trustworthy.
By tracking data quality, a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.
When collected data fails to meet the company expectations of accuracy, validity, completeness, and consistency, it can have massive negative impacts on customer service, employee productivity, and key strategies.
Why Is Data Quality Important?
Data quality is important because it directly impacts the accuracy and reliability of information used for decision-making. Quality data is key to making accurate, informed decisions. While all data has some level of “quality,” a variety of characteristics and factors determines the degree of data quality (high-quality versus low-quality).
9 Popular Data Quality Characteristics and Dimensions
Different data quality characteristics will likely be more important to various stakeholders across the organization. A list of popular data quality characteristics and dimensions include:
What is Good Data Quality?
Data accuracy is a key attribute of high-quality data, a single inaccurate data point can wreak havoc across the entire system.
Without accuracy and reliability in data quality, executives cannot trust the data or make informed decisions. This can, in turn, increase operational costs and wreak havoc for downstream users. Analysts wind up relying on imperfect reports and making misguided conclusions based on those findings. And the productivity of end-users will diminish due to flawed guidelines and practices being in place.
Poorly maintained data can lead to a variety of other problems, too. For example, out-of-date customer information may result in missed opportunities for up- or cross-selling products and services.
Low-quality data might also cause a company to ship their products to the wrong addresses, resulting in lowered customer satisfaction ratings, decreases in repeat sales, and higher costs due to reshipments.
And in more highly regulated industries, bad data can result in the company receiving fines for improper financial or regulatory compliance reporting.
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Top 3 Data Quality Challenges
Data volume presents quality challenges. Whenever large amounts of data are at play, the sheer volume of new information often becomes an essential consideration in determining whether the data is trustworthy. For this reason, forward-thinking companies have robust processes in place for the collection, storage, and processing of data.
As the technological revolution advances at a rapid pace, the top three data quality challenges include:
1. Privacy and protection laws
The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which gives people the right to access their personal data, are substantially increasing public demand for accurate customer records. Organizations must be able to locate the totality of an individual’s information almost instantly and without missing even a fraction of the collected data because of inaccurate or inconsistent data.
2. Artificial Intelligence (AI) and Machine Learning (ML)
As more companies implement Artificial Intelligence and Machine Learning applications to their business intelligence strategies, data users may find it increasingly difficult to keep up with new surges of Big Data. Because these real-time data streaming platforms channel vast quantities of new information continuously, there are now even more opportunities for mistakes and data quality inaccuracies.
Furthermore, larger corporations must work diligently to manage their systems, which reside both on-premises and through cloud servers. The abundance of data systems has also made the monitoring of complicated tasks even more challenging.
3. Data governance practices
Data governance is a data management system that adheres to an internal set of standards and policies for the collection, storage, and sharing of information. By ensuring that all data is consistent, trustworthy, and free from misuse within every company department, managers can guarantee compliance with important regulations and reduce the risk of the business being fined.
Without the right data governance approach, the company may never resolve inconsistencies within different systems across the organization. For example, customer names can be listed differently depending on the department. Sales might say “Sally.” Logistics uses “Sallie.” And customer service lists the name as “Susan.” This poor-quality data governance can result in confusion for customers that have multiple interactions with each department over time.
Emerging Data Quality Challenges
As data changes, organizations face new data quality issues that need quick solutions. Consider these additional challenges:
Data Quality in Data Lakes
When data lakes store a variety of data types, maintaining data quality is doubly challenging. Organizations need effective strategies to ensure data in data lakes remains accurate, up-to-date, and accessible.
Dark Data
Dark data describes data that organizations collect but do not use or analyze. It can present a big problem. Uncovering valuable insights from dark data while maintaining its quality is a growing concern.