Seven Metrics to Assess Your Data Quality in Collibra

Strategies to use to improve the quality of your data and build data best practices into your company’s DNA with Collibra

Your business depends on accurate data. Inaccurate, incomplete, inconsistent data diminishes the quality of customer experiences, hinders operational efficiency, and threatens regulatory compliance, ultimately exposing your organization to unnecessary risk, instead of giving you the information you need.

Data governance initiatives seek to solve these problems, and to provide the business with trusted, high quality data that will boost marketing effectiveness, customer satisfaction, and ultimately revenue. Data governance tools like Collibra Data Governance Center provide a broad set of capabilities to identify and manage datasets. Sustainable data governance requires a solid foundation of quality data. It requires the right people, the right processes, and the right technology to turn raw, untamed data into valuable business insights.

Data quality refers to the ability of a set of data to serve an intended purpose. Low-quality data cannot be used effectively to do the thing with it that you wish to do. Data quality and data governance share a ‘symbiotic relationship’. Data governance needs appropriate data quality tools to not only clean the raw data, but to illustrate data errors, peculiarities and issues, in order to help compile the best standards and monitor the data quality against policies for critical data elements over time. Precisely partnered with Collibra to make industry-leading data validation and data quality monitoring capabilities an integrated component of the Collibra Data Governance Center.

There are lots of good strategies that you can use to improve the quality of your data and build data best practices into your company’s DNA. Although the
technical dimensions of data quality control are usually addressed by engineers, there should be a plan for enforcing best practices related to data quality
throughout the organization.

After all, virtually every employee comes into contact with data in one form or another these days. Data quality is everyone’s responsibility. Assessing data
quality on an ongoing basis is necessary to know how well the organization is doing at maximizing data quality. Otherwise, you’ll be investing time and
money in a data quality strategy that may or may not be paying off.

Download this ebook to explore 7 metrics to access your data quality in Collibra and avoid exposing your organization necessary risk.

Seven Metrics to Assess Your Data Quality in Collibra eBook
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