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7 Best Practices for Data Governance to Support BI

Author: Bankim Sheth | | September 15, 2022


 

Business intelligence (BI) refers to collecting and analyzing data produced by your operations. Analyzing business data allows you to identify insights that will support business decisions that produce growth.

 
Today, business decisions aren’t made based on theories or the instincts of highly experienced employees. Instead, decisions are being made based on your business data, and you need to use best practices for data governance to ensure that your business intelligence is going to work for you.

How Best Practices for Data Governance Support Business Intelligence

Business intelligence is proving to be far more effective in making business decisions than relying on the expertise of individual employees. Here are some insightful statistics about business intelligence:

  • Business intelligence makes business decisions happen five times faster
  • 54% of enterprises reported that cloud-based BI is critical to their operations
  • The BI market is projected to reach over $33 billion in 2025
  • North America is number one in adopting BI software
  • Over 46% of businesses are using a BI tool to support business strategy

 
The key to a successful BI program is the ability to collect accurate data. The adage, “garbage-in, garbage-out” applies when gathering business data. Therefore, data quality is a critical concern for businesses using BI. Using best practices for data governance will go a long way to ensuring that the information you collect is accurate and isn’t going to lead you in the wrong direction.

How Data Governance and Data Quality Work Together

Data governance and data quality are different principles, but they work together to ensure the validity of your business data.

The role of data governance is to identify the business data in your organization, and then develop standards describing who owns the data, how the data is collected, stored, analyzed, and communicated.

Data quality refers to the process of monitoring the integrity of your business data, identifying how to troubleshoot problems, implement improvements, and perform continuous monitoring. This assumes that data governance is in place to ensure that data is usable. Ensuring the accuracy of data that can’t be incorporated into analysis tools is a waste of time.

Best Practices for Data Governance

A complete data governance program can improve your data infrastructure, compliance, and business decision-making. Here are key best practices for data governance that will help you establish an effective program.

  1. Start with your business leaders

    Data governance is something that crosses all organizational business units and will have a big impact on how your company does business. To be effective, you’ll need C-level support and enthusiasm. Consider obtaining support for each executive to take responsibility for a data domain or business data topic.

    Having senior level support will help to make data governance a priority within the business and produce the budgets and time allocation necessary to develop a valuable data governance program.

  2. Develop a measurable set of goals

    One of the first things you want the executive team to do is to establish goals for the program. Review problems that have arisen in the past that could be resolved by a data governance program. Also, identify what the executive team would like to be able to do with business intelligence that they can’t do now.

    That will lead you to goals like increasing productivity, improving customer satisfaction, making decisions more effectively, ensuring that data can be trusted, and more.

  3. Include compliance issues in your program

    One of your goals may be to improve compliance or simplify the process of staying in compliance. But however you view the compliance issue, make sure that you identify compliance requirements and incorporate those requirements into your data governance approach.

  4. Make plans to integrate data from multiple sources

    Whether you’re facing data silos internally, or data coming from multiple external sources, you’ll need to determine how to make that data useful. As mentioned earlier, it’s important that your data can be analyzed by end users. You may need to get support for acquiring new technology and/or changing business processes to bring all critical data into useable form.

  5. Ensure data security at the source

    Cyber security is critical, especially given the fact that hackers are getting more sophisticated and data breaches can be devastating. Find opportunities to secure your data at the source or as close to it as possible. This will avoid having sensitive data exposed in insecure data storage. It will also trigger you to find ways to store sensitive data that needs to be part of your business intelligence system with the proper security rules and access controls.

  6. Develop an effective data governance framework

    Your data governance framework should be tailored to your business. Your framework needs to be a collection of rules, processes, technologies, and roles that ensure security and compliance. Every business has its own priorities in terms of drivers and compliance requirements. The framework one company uses will typically not serve another company well.

  7. Empower Employees

    Once you have a plan for your data governance program, make sure that the organization will take advantage of the business intelligence systems it will support. Educate your workforce about the importance of the data governance program and how it will empower them to see data in a way that will help them be more effective in their work. If you really want to be a data-driven business, you’ll need to consider the culture change that you’ll need to navigate.

    Education and training are key. Giving tools to end users will only be useful if they are comfortable with the new tools and understand not only which buttons to push, but how they can find new insights using the technology correctly.

How Datavail Can Help

There’s a lot more to data governance than what we were able to cover in this blog post, and our experts have the background and knowledge to help you develop and implement a data governance program that fits your organization’s own unique situation and needs.

You can learn more about how one organization managed their data quality issues with a data governance program by downloading our case study, “How a Data Governance Program Improved Data Quality.”

And if you have questions about your organization’s data quality, data governance, or business intelligence, contact the data quality and data governance experts at Datavail for more information.

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