Mission Impossible? Overcoming Data Governance Challenges

6 Key Questions to Help Identify the Strength of your Data Governance Program

Jeff ShortisApril 11, 2017

With today’s enterprises relying on big data analytics for business intelligence, implementing an effective data governance program is a top priority. Without data governance there are unanswered questions in understanding your data – “Am I using the right data?” “Is the data I’m using quality data?” After all, data is only valuable if you can translate it into actionable insights to inform strategic and operational decisions.

Creating a comprehensive data governance structure requires a process to deal with the most common problems around data. In fact, if a business can’t answer the following six questions, it’s a sign that they need a stronger data governance program.

Understanding your Data

If you were unable to answer one or more of the questions above, you’re probably experiencing some of the most common challenges related to data governance and big data. Here we’ve outlined some of those key challenges – and solutions.

  • Keep it Secure – Ensuring the security of sensitive and personally identifiable information (PII) is a top priority for an effective data governance program. Having a place to view the data end-to-end is even more important. Many enterprises struggle to reduce data security risks due to unauthorized access or misuse of data, while others have difficulty managing the confidentiality, integrity, and availability of data. By understanding the nature of the data, where it’s stored and how it’s used, enterprises can implement the appropriate data governance guidelines for data use, and specify the right standards and policies around data ownership.
  • All Roads Lead to Data Quality – To keep data usable and reliable, users must trust their data. Most enterprises spend too much time gathering, normalizing, analyzing and reporting on data from multiple sources instead of understanding data provenance to make meaningful improvements. As data flows through the enterprise, the data must be accurate and timely, and must contain the right definitions and meaning. If you can’t pair the right definitions with accurate data, the data may be meaningless and insufficient. To derive business insights and analytics, enterprises must have accurate standardized data across all systems and processes to make solid business decisions.
  • The New World of Data Privacy – Data protection regulations are changing and complying with these new laws requires a strategic process. One new law in particular that takes effect in May 2018 applies to any organization doing business in the EU – General Data Protection Guidelines (GDPR). This new law alone is driving many organizations to institute data governance since it’s imperative for enterprises to have the necessary policies in place to protect sensitive information, as well as ensure third-party data security. To enable data privacy, enterprises must go beyond third-party pre-and post-risk assessments and implement a data governance framework to provide visibility into these policies and how sensitive data and third-party data can be used.
  • Policy Makes Perfect – Many organizations have come to understand the critical nature of a data governance framework. No matter where you are in the implementation process, having visibility into your data is a priority. Many organizations maintain legacy systems as new systems are implemented. Unfortunately, many times those organizations falsely assume their new platform will work seamlessly with their old system. Others experience siloed systems that that cannot cross-communicate across the enterprise. For others, they just don’t have the right business rules in place. In each instance, organizations severely lack any visibility into their data. Whether it is examining current workflows, developing data definitions or the identification and documentation of appropriate business rules, developing the right business processes is critical to the success of a data governance program.

Data is complicated and multi-dimensional. If the goal of data governance is to make accessing data more efficient, by understanding the definition of the data, are there third party licensing constraints, where it is stored, who is the data owner and what policies dictate what can be done with the data, now is the time to determine if you have the right solution in place.

Data Governance is an Art, Not a Science

Data governance is not a standardized solution, but many pieces can be automated, such as extracting metadata, to accelerate both the deployment and on-going operations. With enterprises facing increased global competition, rising client expectations, tightening profit margins and increased regulatory demands, a cloud-based data governance solution can help organizations synthesize and visualize information about data in a manner which is easy to understand.

The right platform can also automate and aggregate data quality metrics to measure and analyze the accuracy, consistency and reliability of data at rest and in-motion. This enables businesses to not only report on data lineage and governance metrics, but to continuously improve them.

The end-result provides a holistic view of data from both a business and technical perspective while also ensuring the appropriate data access controls are in place, making organizations better equipped to govern their data and take control of their business processes while also reducing costs.

To learn more about data governance and how it can help your organization gain knowledge, value and visibility around your data, check out this infographic.