Businesses today use all kinds of data for a host of unique applications. Metrics can indicate how effective a website is or how user-friendly a landing page can be to navigate. Data can also point to leads generated, compliance issues, and tons of customer service insights. You might already be using innovative reporting solutions, such as Power BI Reporting, to regularly implement data-driven insights. Regardless of how your enterprise uses and prioritizes data, there’s another aspect of analytics to consider—maintaining data governance.
For the foreseeable future, data privacy and compliance will be sensitive topics for any company. It’s also been challenging for most digital brands to stay ahead of certain data management risks. Securing your company data also means maintaining proper structure and oversight of your business intelligence.
Assembling, sharing, and initiating change within your company’s process of managing data requires setting some governing policies. Audits may be necessary, as will be tracking efforts to verify the data continues to be accurate and valuable. Today, we’ll dive into what data governance actually entails. We’ll also provide plenty of insights to consider for maintaining data governance benchmarks and standards.
What Is Data Governance?
Imagine trying to collect all of your company’s raw data and reports without establishing parameters. It would be a sea of figures and metrics that would be nearly impossible to decipher.
Without data governance, your data becomes a free-flowing cascade of complex assessments and information. Establishing a data governance structure or method of categorizing, prioritizing, and sorting the relevancy of the data will make it more valuable and actionable. It is essential to establish a data governance policy or a set of rules, and it is not a task that can be completed once and for all.
Consistent data governance and adaptability to industry trends are key for maximizing data usage. Metrics timelines should be regularly reevaluated.
Why Is Data Governance So Important Today?
Data governance has always been important. But in today’s fast-paced and ever-changing digital landscape, companies must be ready to move the goal posts as needed in response to changes in customer demand, industry shifts, and compliance. The data you collect today might be less valuable to your company tomorrow. Alternatively, the data governance you have in place to reduce risks today might not be effective in risk-reduction efforts next month.
The emphasis on compliance, security, and privacy with regard to data and business intelligence only continues to grow. In today’s business world that heavily relies on digital technology, enterprises need to prioritize maintaining their data governance efforts to stay ahead.
Corporate Governance Quotient (CGQ)
Maintaining control over data governance is important for overseeing personalized reporting, data accessibility, and general business intelligence metrics. It’s also mission-critical for Corporate Governance Quotient or CGQ. CGQ is always top-of-mind for publicly traded brands. CGQ also represents a standard of excellence and an example for all businesses to follow. Explore these CGQ insights as inspiration for your data governance formation:
- CGQ measures a business’s quality of overall governance.
- CGQ follows eight core analytics to produce a CGQ score.
- CGQ expands continuously with benefits for environmental, government, or social government criteria.
A few of the factors that go into determining a business’s CGQ score include:
- Structure of the board of directors.
- Director education.
- Auditing and accounting concerns.
- Bylaw provisions.
- Qualitative factors.
- Insider stock ownership.
How Data Governance Reduces Risks
Data governance reduces risk in a pivotal way. First, with suitable structures and oversights in place, data governance can mitigate the siloing of critical data. It also means having provisions in place for safe and secure data sharing. A flexible approach to data governance will ensure the quality and accessibility of your data is kept to a high standard.
Establishing Data Governance Policies
When you set out to establish or revamp your data government policies, do so in alignment with your industry’s compliance requirements. Be sure your policies also support those risk management and security goals. Then, follow through with these essential steps for creating a successful data governance initiative.
Delegate Data Compliance Specialists
A data compliance policy isn’t going to govern itself. Consider assembling a team of dedicated compliance specialists to routinely audit and oversee your efforts on an ongoing basis. Define the stewards’ responsibilities, the primary points of contact for data concerns, and the data governance policies to protect. Set the necessary frameworks for these specialists within the data collection parameters.
Define Your Data Applications
As with most company policies, you’ll want to provide clear and concise definitions for all data classifications and applications. Anyone with access to your assorted datasets should understand with 100% certainty how to identify, apply, and securely share those metrics. Here, you’ll also label which data is intended for public, internal, or private access only.
Monitoring and Controlling Data Access
Create a workflow of data accessibility permissions with restrictions. Users should only be able to access the data they need to carry out their unique roles at the company. This is also known as role-base access control (RBAC). Developing layers of control for data access will reduce data-related privacy, security, and compliance risks.
Auditing for Compliance and Accountability
Conduct routine audits of any data governance policies you have in place. These audits will help you determine your success in compliance accountability. Your teams will be able to identify areas for improvement with ease. Enable audit trails using Power BI Reporting so you can track user activity, access attempts, and data modifications.
Data Governance and Compliance Examples
Every business model is unique. As such, expect that your company’s data governance policies will be unique and ever-changing. To help you understand how data governance might apply to your specific business, explore some of these data governance and compliance examples:
- Data usability
- Metadata
- Data security
- Data preservation
- Data quality
- Data integration
Think of your data governance as the over-arching guide for defining customer data, product data, retention policies, data authorship, data curation, and data accessibility.
Common Tracking Challenges with Data Governance
There will always be obstacles to managing, securing, and governing data. Here are just a few of the more common tracking obstacles to consider as you develop your data governance policies:
- Even well-structured data is often under-structured.
- Data categories or filters can often be too loose or too strict.
- Data-related laws have to be a priority and are often changing.
- Improper categorization of data leads to 10x more time in data cleansing efforts.
- Customers are increasingly more aware and suspicious of companies’ data practices.
- Bringing in outside data always comes with certain risks.
- Fines for compliance, privacy, and security failures are steep.
Whether it’s to better serve your customers or to regulate your data more securely, maintaining data governance is key to making the most of your data. With the right reporting tools and software reporting, including Power BI Reporting, data governance can be easier to manage and much easier to maintain.
