Your Guide to the Benefits, Challenges, and Best Practices of Data Governance

Technology   |   Will Davis   |   Oct 25, 2020

Picture this scenario: a group of health insurance analysts want to understand the variation in cost of a medical procedure. However,  the data they receive from partnering hospitals is stored in different systems throughout the organization and its accompanying metadata doesn’t match up, making it nearly impossible for users to understand the context of the data. Plus, on top of all of that, there’s HIPAA to consider, which means that this type of sensitive patient data must be properly protected. 

Enter, data governance. Data governance is a critical component of the umbrella term data management and exists to solve the types of problems described above. Read on to learn more about data governance, data governance frameworks, and why organizations need data governance for an effective data management strategy.

What Is Data Governance

Data governance is an organization-wide process that manages the usability, integrity, and security of data. Promoting healthy data governance demands organizations to ask questions such as: Where does data live throughout the organization? Is the data consistent across each system? Who has access to this data—or doesn’t? Does this level of access encourage new analytic growth? Does it respect regulatory guidelines? 

Establishing data governance requires organizations to create data governance frameworks, which defines who can take what actions, with what data, in what situations, and what methods they can use. These principles guide data analytics at every stage of the collection of data, and will be unique to each organization depending on the type of data, data systems, and regulatory requirements they must contend with. 

Organizations need a data governance framework to guide data through its lifecycle, but it isn’t necessary to use data governance systems or software. Software systems do exist solely for data governance, but they aren’t the only way—or even the best way—to establish a data governance framework. The key is having a framework in place and ensuring that everyone who works with data adheres to it.  

Why Does Data Governance Matter? 

Data governance ensures that data is trusted, documented, and accessible. Without proper data governance, organizations run the risk of an incredible amount of inconsistencies between data systems. Not only can this make data integration incredibly challenging, but it also may lead to analytic errors down the road. 

A lack of data governance can also lead to hefty fines for failure to comply with data regulations. Often, complying with data regulations demands that organizations enforce strict data permissioning controls, which certainly should be included in any data governance framework. But data governance isn’t just about restricting data—in fact, a big part of its focus is opening up pathways for the right analysts to have the data they need. Good data governance means looking for opportunities to monitor and provide access to data in a responsible manner so that analysts aren’t encouraged to perform analytics by using tools that aren’t vetted or secured. 

The Benefits of Data Governance

Here are some of the main benefits of an effective data governance strategy: 

  • Eliminated data silos.
    Data governance centralizes data efforts and eliminates one-off efforts by departments or individuals, which opens up greater data access and analytic opportunity to the organization. 
  • A consistent understanding of data.
    Data governance principles enforce uniform metadata, which makes categorizing and integrating data much simpler.
  • Higher-quality data.
    Data governance establishes a plan to keep data consistent, accurate, and complete throughout the entire data process.
  • Meeting compliance.
    Data governance accounts for regulatory requirements. Some of the most common regulations include the European Union’s GDPR, the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA).
  • Improved data management.
    As a key piece of data management,  data governance guidelines help establish best practices that extend to other areas of data management: legal, security, compliance, etc. 

The Data Governance Framework

Every organization will need a unique data governance framework designed to move its goals forward. Here are some suggestions for how to get started establishing a data governance framework:  

  • A mission statement or vision statement for why data governance is crucial to the organization.
    Understanding the bigger picture of data governance can ensure that the organization stays focused on enforcing it. For example, a mission statement may include the importance of fostering a data culture that relies upon clean, consistent and understandable data for decision-making. 
  • The short-term and long-term goals for the organization.
    These goals should address the main pain points in the organization, both in the long-term and short-term. This may include meeting certain regulations or developing a manner in which data can be securely shared out to customers, for example. These principles should also include the success criteria and how the goals will be measured.
  • The data rules, definitions, and policies.
    Develop a data glossary to contain data policies, standards, and definitions that get at the nitty-gritty of data governance. It should outline how data should be used day-to-day, accountabilities within an organization, reporting, and control mechanisms.
  • The roles and responsibilities of data stakeholders.
    Data governance frameworks should also establish the roles and responsibilities for each group involved with data. These roles can include who to consult, who to inform, who to hold accountable, and who is responsible for each step. These roles can also include who is responsible for record-keeping and who has access to which pieces of information.
  • The guidelines for timing.
    Data governance frameworks should include when the processes should be deployed. Details like this can be what makes a data governance framework successful. After all, the right timing is key to success, and that extends to data governance.

data governance framework

Cloud Data Governance

As more and more organizations move to the cloud, data governance shouldn’t get left behind. The cloud presents new challenges with privacy, security, and management that may require organizations to rethink their data governance strategy. For example, organizations might consider including more principles and guidelines about privacy, while focusing less on the assembly line of who is working with data, given the broad accessibility of the cloud.   

However, data governance shouldn’t be considered a burden; it can be a strong asset in helping simplify the transition to the cloud. Predetermined roles and responsibilities can simplify the transition and ensure that accountability is in place when moving analytics to the cloud. 

Data Governance Best Practices

Data governance best practices can vary widely between organizations depending upon their unique data needs. All the same, here are some best practices for data governance that can be applied to most organizations and industries: 

  • Remember that data governance is a practice.
    Sometimes data governance is lumped in with other projects as a one-time occurrence. But data governance is a practice that will influence the quality of data. It’s best to continuously enforce and adjust data governance.
  • Communicate frequently.
    Communication is crucial for data governance to benefit an organization. There must be frequent communication between the set roles and responsibilities, as well as between IT and business users.
  • Focus on operation.
    Data governance needs to fit into the day-to-day operation of the organization. A data governance strategy must integrate seamlessly with the way an organization does business.
  • Educate employees and stakeholders.
    If employees or stakeholders don’t understand the benefits of data governance, it may be difficult to implement the strategy. Educating stakeholders by showing how data governance helps business goals can help make the strategy more effective and integratable.
  • Utilize metrics and reporting.
    Focusing on a limited number of KPIs and other goals and using the metrics and reporting can keep a data governance plan relevant and useful.
  • Begin small.
    One of the best ways to get a data governance strategy off the ground is to start small. Focus on small victories that data governance can help with before expanding to the entire lifecycle of data throughout the entire organization. 

Where Does Data Preparation Fit Into Data Governance?

As mentioned, a big part of data governance is ensuring that data is usable and accessible for analytics. And since the first stage of any analytics project is data preparation, that makes data preparation an important component of a data governance framework. 

Historically, data preparation fell onto the shoulders of IT, but it’s now largely considered to be the responsibility of analysts.  For one, this is a more efficient approach—instead of a small task force chasing down issues of data quality, there are more eyes on the data—but it also leads to better curation for the end analysis. IT will still curate the best stuff, make sure it is sanctioned and re-used (this ensures a single version of truth and increases efficiency). But, with business context and ownership over the finishing steps in cleansing and data preparation, these users can ultimately decide what’s acceptable, what needs refining, and when to move on to analysis.

Considering that modern data preparation is a collaboration between IT and data analysts, organizations should capitalize on the opportunity to ensure that data governance practices are carried through during this transition. One of the best ways to do so is by providing data analysts with a data preparation platform that has the best of both worlds—the user-friendly interface that analysts need with the security and governance that the organization requires—like Trifacta. 

Powered by machine learning, the Designer Cloud data preparation is incredibly easy to use. But it also seamlessly integrates across any cloud, hybrid or multi-cloud environment to connect to organizational data and prevent data silos. And with permissioning controls and ever-updating data lineage, it is optimized to support a data governance framework. 

To learn more about Designer Cloud get started with our data preparation platform today.