Data Governance: What It Is, It's Importance, and How To Get Started

A recent Gartner survey shared that, “61% of companies said their governance goals included optimizing data for business processes and productivity but only 42% of that group believed they were on track to achieve it.” Data governance is often viewed as a prohibitive, controlling, and time consuming process designed to slow down work. Traditional approaches to data governance can make it a complicated effort, detouring teams from implementing it, but it doesn’t have to be. There are new investments being made to simplify how teams are introducing data governance inside their company. 

What is data governance?

Data governance is responsible for the overall management of the availability, usability, integrity, and security of the data being used. It involves creating practical and responsible processes, policies, expectations, and ownership around the management and usage of data – ensuring it meets company objectives and at the same time complies with required regulatory demands. Data governance answers questions like:

• Who is the owner for specific data?
• What are the current policies and expectations for data security, and privacy?
• What ways is data being identified, classified, accessed, and shared?
• What are the processes for data governance?

Being able to answer these questions confidently enables a company to form data governance frameworks.

The importance of data governance for connected companies

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According to Gartner, “By 2027, 80% of data and analytics (D&A) governance initiatives will fail due to a lack of a real or manufactured crisis.” Data is one of the most important assets that a company has, and organizations need a framework that helps to avoid bad-quality data, compliance violations, and non-actionable insights. Data governance is especially important for companies that are using third parties because it ensures the proper use of data across the connected ecosystem. Introducing a data governance framework helps in setting clear guidelines and boundaries for data handling, and it ensures that the data shared with or accessed by third parties is the right data, and aids in regulatory compliance by ensuring that data practices align with industry-specific compliance regulations. Data governance gives teams the control over data flows, enabling companies to track data usage and identify any violations quickly. Effective data governance fosters a culture of ownership, responsibility, and visibility, resulting in a company's most valuable asset being safeguarded.

The principles driving traditional data governance

Data governance has an important role in ensuring that a company understands the value and risks of the data they’re storing or sharing. When introducing data governance, starting with widely accepted principles helps to create a structured approach. Which is why historically most companies have followed these universal governance principles from the Data Governance Institute.

1. Integrity: Controls and processes should be implemented to ensure firms maintain data integrity, prevent data corruption, and address data quality issues promptly.
2. Transparency: Practices should foster transparency, ensure policies and decisions are well communicated, and provide visibility into data sources, flows, and usage.
3. Auditability: Data governance requires comprehensive documentation that is traceable to meet compliance requirements.
4. Stewardship: Data ownership should be defined and owners should be responsible for data quality, integrity, and proper usage.
5. Checks and balances: There should be no conflicts of interest or unchecked.
6. Standardization: Data governance should establish and enforce consistency across the organization. 
7. Change management: Firms should have clear policies for how and when data should change. Data governance recognizes some data changes frequently, while other information should alter only after considerable review and impact analysis.

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The above list of traditional principles provides a clear starting point for establishing policies, procedures, and standards to align with a company's goals and regulatory requirements. While these traditional principles help teams know where to start, they should be viewed as just that, a starting point, because a one-size fits all approach won’t work. 

Traditional vs modern approaches to data governance

Teams need a tailored approach to data governance because each organization has unique needs, regulatory requirements, and operational challenges. A traditional one-size-fits-all strategy when deciding on data governance principles often falls short in addressing specific data issues, such as varying data standards, privacy concerns, and compliance requirements across different industries. Alternatively, modern approaches leverage nimble, decentralized, and business specific guidelines. Focusing on a modern customizable data governance approach ensures that policies and processes align with the organization's goals, resources, and risk appetite, and facilitates more effective data management. Tailored approaches also accommodate the distinct roles and responsibilities within teams, enhancing accountability and fostering a culture of data ownership that is relevant and actionable for every stakeholder involved. But, adopting modern data governance has its challenges; it requires a deliberate cultural change, training, and stakeholder buy-in.

First phase of implementing data governance: the riscosity approach

The first step in data governance is knowing what data you have and where it’s going, and making it part of the tech stack vs forcing it as another step in a day-to-day workflow. This is done by using a combination of automation, discovery, and continuous tracking. At Riscosity, we believe in building a data governance foundation tailored to a company’s specific needs, without introducing busy work. To accomplish this, we start with an automated comprehensive scan to catalog and provide a view of all vendors and APIs (it gets better - we’re surfacing those shadow APIs too) that a company has within their ecosystem. Starting with a data catalog gives teams a single source of truth to empower anyone with visibility, manageability, and control of all the data spread across the company.

The Riscosity approach to data cataloging allows teams to:

• Easily discover, monitor, and control any data across environments. Our catalog shows where data is being sent, what specifically was sent, what jurisdiction this data falls within.
• Collaborate. Make all governance efforts visible by exporting clear reports.
• Implement tailored governance policies. Scale controls to manage which vendor has access to what data to stay in compliance with regulations such as the GDPR, PIPL, DPDP.
  
  

Once teams know what data they have, the next step is to create tailored policies that are scalable without needing to tap into engineering resources. Since Riscosity is deployed at the source, policies are embedded into the data flow vs being forced into an employee flow. Within a few clicks, policies can be tailored to regulation specifications, tools, regions and more. Once a policy is launched, every step to enforce it is automated. These steps can include blocking or redirecting sensitive data, alerting when new data goes into a highly regulated location, replacing data that is in violation with pseudo inputs, and more. All done with no code required.

Take the next steps in data governance

‍Data is a crucial part of what makes a company successful, and knowing how to protect it while using it is key. Unsure if your team's current data governance framework has the key fundamentals incorporated? Let us help! Find a time to get a free data governance audit with our team now.