White Paper

Three Reasons Your Data Governance Initiatives Are Failing

Three pitfalls to avoid for successful data governance

With growing regulations and greater public concern over data privacy, companies are increasingly focused on data governance. And with good reason – businesses must have strong, successful data governance in order to mitigate risks as thoroughly as possible.

While there’s no single way for a business to approach data governance that will ensure perfect results, there are some pitfalls to avoid.

Download our latest white paper to explore:

  • Three key reasons many data governance initiatives fail
  • How to maximize the value of your data and ensure compliance with a data governance strategy that prioritizes data quality

Do numeric fields have letters in them, or do name fields include email addresses instead? Are all account IDs unique? Is personally identifiable information (PII) finding its way into other fields that may not be properly masked, creating risk?

Regulatory compliance requires good quality data – and, of course the rest of your business does, too! But how do you know if you have good data?

Data profiling gives you visibility into the completeness and validity of your key data elements over time, with statistical summaries and the ability to drill down into details for further analysis. Rather than repeatedly pulling and reviewing data, or running SQL queries, a data profiling tool provides a way to quickly, yet deeply, assess data quality at a detailed level based on the actual data content and store the results for easy, ongoing review and collaborative evaluation.

Data governance is the set of policies, processes, rules, roles and responsibilities that help organizations manage data as a corporate asset. However, data governance tools do not apply or enforce policies and rules against your actual data; nor do they test your data to measure compliance with those rules.

Data governance requires data quality rules & metrics to assess and quantify data errors and issues to measure compliance with regulations and policies and monitor data quality trends.

successful data governance
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