Enterprises are collecting, producing, and processing more data than ever before, but IT teams quickly learn that having reams of data doesn’t do much good if it’s incomplete, inconsistent, or out-of-date. Data is measured by its accuracy and usability, but it must also be relevant to the context of specific business issues to be valuable.
Since data provides the foundation for all manners of analysis and business decisions, organizations must ensure that the data on which they rely is always high quality and consistently accessible. Bad, or low-quality, data, in contrast, leads to issues, which can derail businesses and have serious negative impacts on growth and profitability. Examples of issues with poor data qualityinclude:
In this blog, we’ll cover the multiple dimensions of data quality and what enterprises can do to ensure they are relying on high-quality data to support their business operations and objectives.
In its simplest form, data is high quality if it accurately describes the real-world. And to accurately describe the real world, data must exhibit six necessary characteristics. High-quality data must be:
In order to ensure these six data quality requirements, organizations need to establish checkpoints across the entire data pipeline. This helps prevent data downtime, eliminate data reliability challenges, and provide early warnings if there are data quality issues.
Whether you’re working with structured data tables or unstructured data in a hybrid data warehouse managed by a metastore, it’s important to create data quality programs that ensure all data users have access to high-quality assets.
Establishing effective data quality programs help ensure that data is accurate, complete, and timely throughout the entire data pipeline, from ingestion to consumption. To create effective data quality programs, businesses should be able to provide:
With these six dimensions of data quality embedded into technology and business operations, enterprises can trust their data and be confident that it will lead to better decisions, effective decision-making processes, and ultimately, improved financial results.
Data is becoming the lifeblood of enterprises. In this context, data quality is only going to become more important. “As organizations accelerate their digital [transformation] efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance,” says Ted Friedman, VP analyst at Gartner.
Organizations must improve data quality if they want to make effective data-driven decisions. But as data teams collect more data than ever before, manual interventions alone aren’t enough. They also need a data observability solution like Acceldata Torch, with advanced AI and ML capabilities, to augment the manual interventions and improve data quality at scale.
Book a free demo to learn how Acceldata can help your enterprise overcome poor data quality at scale.