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  • Writer's pictureThe Bluemetrix Team

How to Capture Data Changes with Data Governance Automation

The historical challenge of collecting quality data

Enhanced data quality is an output when effective data governance is implemented across organisation. But, the process of operationalising data governance collection has challenges:

  • Data sets that are often too small to be meaningful, reliable, or impactful.

  • Data that does not accurately measure what it intends to measure or provide insights into end-user behavior.

  • Human error when capturing data, including ambiguous, duplicate, and badly transformed data.

  • Limited understanding of the business value of data governance.

  • Lack of support from leadership and accountability for data ownership.

  • Budget constraints and limited resources to data governance compliance tools.

  • Lack of data documentation and understanding of who has access to it.

Ninety percent of today’s data has been created in the past couple of years, according to an IBM study, hence the need to address the challenges of successful data governance simply, quickly, and cost-efficiently.

Why is capturing data changes vital?

Data in an organization changes on a daily basis. Some of these changes are critical to how an organization runs its business, and some have no impact. The dilemma facing executives is to identify the critical changes as they occur and ensure the relevant data owners are notified about these changes so they can act on them. Automation, correctly designed and implemented, allows these changes to be captured as they happen and brought to the attention of the people who need to know.

Many organizations manage their data reactively, i.e. they only do something when they find an issue or error. According to Gartner's research, “the average financial impact of poor data quality on organizations is $9.7 million per year.” Furthermore, the cost of bad data can be an astonishing 15 percent to 25 percent of revenue for most companies.

This is where Bluemetrix Technologies comes in, offering a proactive data governance solution for resolving bad data quality issues and enabling the automation of governance procedures in a controlled manner.

Solution – Bluemetrix Data Manager Control

1. Monitor and report on data governance rules

Bluemetrix assists organisations with enforcing data governance rules by monitoring and reporting on the real-world state of data at the company, for example, who has access to it, where it is located, and how sensitive it is, etc.

The Bluemetrix Technologies game-changing plan of action:

  • Data Access Auditing – Reporting on who has access to what data and how they use it.

  • Data Quality Implementation and Reporting– Validating the consistency and integrity of data over time.

  • Data Changes– Capturing change in the data as it happens and where relevant reporting these changes to the data owner/stewards.

  • Anonymization of PII data –Mitigating security vulnerabilities, increasing security awareness, and creating customer trust and loyalty.

2. Monitor and report on the real-world application of data governance rules

After successfully enforcing data governance rules, organisations may still lack visibility into the best way data is processed, updated, or changed. Bluemetrix Technologies can analyse data changes and show its effects, enabling the application of more relevant data governance rules.

3. Auto collect and populate data governance and catalogue tools

Data is constantly changing. One of the biggest issues is capturing this change in an organisation’s metadata to ensure that the metadata always provides a true view of the underlying data. Bluemetrix allows the capture of this meta-data at the pipeline creation stage, and by automatically updating the data catalogue to represent these changes it helps data owners ensure their metadata is up to date and correctly represents the underlying data.

Operationalise Your Data Governance

Whatever an organization’s industry niche, they should not wait for false, irrelevant, obscure, or misleading data to create a hole in their corporate financial bucket.

Operationalize data governance procedures can be done with self-service analytics that provide a fresh, simplified, and unified view of data across an entire organisation.

We call it BDM Control – it will change the way you view automated data governance for quality data.




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