Demystifying the Difference Between Data Integrity & Data Quality

Print Friendly, PDF & Email

In this special guest feature, Amy O’Connor, Chief Data and Information Officer at Precisely, believes that while many use the terms data integrity and data quality interchangeably, there are some important differences between the two that must be kept in mind. Amy leads the global IT and cloud operations, data analytics, and information security teams responsible for delivering a world-class digital experience as the company continues its rapid growth in data integrity. She has extensive experience using data-driven approaches to align IT services with user needs. Amy earned her B.S. in Computer Science and Electrical Engineering at the University of Connecticut School of Engineering and her M.B.A. from the D’Amore-McKim School of Business at Northeastern University.

Enterprises today are leveraging data to power nearly every function of their business. From informing better decision-making processes to enhancing customer experiences, data has become the lifeblood driving results for any modern organization. However, there is a major risk associated with that data if it’s not trusted. If a company is leveraging data that lacks this crucial component, it can do more harm than good when it comes to making business decisions.

While many use the terms data integrity and data quality interchangeably, there are some important differences between the two that must be kept in mind. A truly robust data system ensures data addresses these distinctions, helping businesses maximize the accuracy, consistency, and context of the information being stored.

Understanding Data Quality

Let’s start by discussing data quality. When thinking about data quality, it’s important to understand that it serves as a subset of data integrity. It refers to the reliability of data and should be benchmarked based on key questions such as:

  • Is the data set complete? In other words, is the data present representative of a large percentage of the total amount of data needed.
  • Is the data unique? Stored data sources must be free of redundant or extraneous entries.
  • How valid is the data? Information must conform to the syntax and structure that is defined by the requirements of the business.
  • When was the data sourced and is it timely? For data to truly benefit an organization, it must be as up-to-date as possible.
  • How consistent are the data entries? Data must be represented and stored in a standard way.

Evaluating data quality based on whether it’s complete, unique, valid, timely, and consistent helps organizations ensure the information is designed to help drive results. It’s important to note that just because a company has high-quality data doesn’t necessarily mean they’ll find it useful. Even if a database is accurate and valid with customer names and addresses, it may still lack the context that can be achieved through supporting information, such as who the individuals are and what relationship they have with the business.

Enter Data Integrity

Data integrity comes in to provide that context on reliable and accurate data. It helps the information be useful for the organization, ensuring databases include information that is complete, accurate, consistent, and in context.

When looking at data integrity as a whole, it can be achieved through four main pillars, one of which is quality. Here’s how they each play a role:

  • Integration: Whether it’s originally stored on legacy systems, relational databases, or cloud warehouses, it’s important for data to be seamlessly integrated into a singular view that can quickly provide businesses with greater visibility.
  • Quality: In order for data to be useful, it must be complete, unique, valid, timely, and consistent, as discussed above.
  • Location Intelligence: Adding a layer of richness and complexity through location insight and analytics helps make data much more actionable across organizations.
  • Enrichment: Leveraging external sources of information to enrich internal data helps add additional context, nuance, and meaning. In order to gain a more contextualized and complete view, information pertaining to the business, consumer, or location intelligence will help power more effective analysis.

Maximize the Power of Data

Organizations are leveraging data to drive greater decision-making processes and results across nearly every facet of the business. Data has become the most critical asset for businesses, but ensuring its quality is only the tip of the iceberg. Beyond the surface, data integrity is not only enriching the insights being delivered — it’s making them actually useful to power the way businesses operate.

From the onset, the road to true data integrity starts with ensuring quality, but it’s imperative that companies go beyond that point. They must make a concerted effort to shore up any inconsistencies that may exist, while also finding ways to add greater accuracy, consistency, and context to the information it relies on for critical business operations and decisions.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: @InsideBigData1 –

Speak Your Mind