Table of Contents
Data integrity refers to the accuracy and consistency of data over its entire lifecycle, as well as compliance with necessary permissioning constraints and other security measures. In short, it is the trustworthiness of your data.
What Is Data Integrity?
Data integrity refers to the accuracy and consistency of data over its entire lifecycle, as well as compliance with necessary permissioning constraints and other security measures. In short, it is the trustworthiness of your data. High data integrity means data hasn’t been altered, corrupted or misused. Achieving data integrity requires asking questions such as, how is the data being transferred? and what is the risk of corruption during that process? Is data access limited to the right people or has sensitive data been compromised? Does the data remain consistent during routine updates?
For an organization to ensure high data integrity, it must invest in proper physical data storage with data integrity constraints. But data integrity is rarely static; once achieved, it must be monitored and maintained. In that sense, data integrity is as much a process as it is a state—there are myriad ways that data integrity can be compromised, either consciously or by human error, which means organizations can never simply check data integrity off their to-do list.
Data Integrity vs. Data Quality
If data integrity can broadly be summed up by the trustworthiness of the data, then data quality is all about its analytic value. Data quality involves the 4 Cs, or the consistency, conformity, completeness and currency of the data. They are, of course, dependent on one another. You cannot have high-quality data that isn’t trustworthy, but data integrity and data quality are increasingly managed separately.
Today, instead of taking on the entire responsibility for managing data integrity and data quality, IT teams are outsourcing data quality tasks such as data preparation to business departments. Not only does this separation of work allow IT departments to focus on their core strengths in hardware deployment/maintenance and security, but also means that business teams receive data faster and, with their unique knowledge of the data, can work to refine its quality level to meet their specific analytic needs. Of course, IT departments will always have a hand in data quality, but employing business teams to do some of the data preparation work required for data quality improves data quality and data integrity efforts overall.
Using Designer Cloud to Shift Data Integrity and Data Quality Work
A key piece of technology is required in order to enable business workers to take part in data quality work: data preparation platforms. A data preparation platform offers all of the engineering power of an IT department to transform data at scale through the lens of a user-friendly platforms that analysts can comfortably operate.
Routinely names the leader in data preparation platforms, Designer Cloud’s ongoing commitment to data quality is evident in its core feature functionality. Unlike any other data preparation product, Designer Cloud empowers non-technical users to do more with their data by constantly guiding them through the process using intelligent suggestions powered by machine learning. Designer Cloud is proud to directly enable better data quality efforts, support data integrity, and enable IT departments to focus on their more challenging data integrity activities.