Data governance is a bunch of strategies and practices that ensure high quality through the complete lifecycle of your data. Data Governance is a practical and actionable framework to assist a wide range of data stakeholders across any organization in identifying and meeting their data requirements.

1. Best Practices of Data Governance
Data governance is a bunch of strategies and practices that ensure high quality through the complete lifecycle of your
data. Data Governance is a practical and actionable framework to assist a wide range of data stakeholders across any
organization in identifying and meeting their data requirements.
Data governance ensures that data is secured, appropriately documented, managed, trustworthy, and audited. Effective
policies and procedures of data management lead to better business results and drive business growth rapidly, and in
the current competitive market, it’s a requirement for organizations. Today, enterprises gather a staggering amount of
internal and external data. In order to use that data effectively, manage risks, and reduce costs, effective data
governance is a must.

2. Importance of Data Governance:
1. Data Governance Saves Money:
In simple, data governance increases efficiency. Duplicate accounts result in duplicate efforts, or at the very least,
it causes wastage of time tracking down duplicate accounts in your marketing, sales, finance, or analytical efforts.
Data governance eliminates the occurrence of errors in your database, providing your business a solid database to
work from saving precious time that would otherwise be used to correct your existing data. Time saved is money
saved.
In addition, data governance forces a business to precisely define its core data and the rules governing that core
data. Originating a data governance framework is an excellent opportunity to get everyone under the same roof
about core data definitions. The enforcement of this ensures greater operational efficiency over time.

3. 2. Bad Data Governance is Risky
Lack of robust data governance is a security concern for two reasons: outside security risks associated with dirty,
unstructured information and regulatory compliance issues.
Bad data and poorly designed data pose a security risk for the simple reason that if you have dirty, unstructured data
clogging your database, how could it be possible for you to quickly identify when something goes wrong and how
could you efficiently monitor which data is at risk? Good data governance tools and practices make it easier to
monitor what is happening across your database and make it easier to see what areas may be at risk
3. Good Data Governance Provides Clarity
Consider a moment to imagine what the assurance of good data would mean to your business. Effective data
governance offers the peace of mind that the data is generally clean, standardized, and accurate. The effects of this
reverberate throughout a company.
• Here are some of the benefits that this clarity will provide:
• Assurance that your metrics are accurate
• Insight into what your most essential metrics might indeed be
• Greater confidence in your analytics

4. 4. Role of Data Governance
The critical role of data governance is to ensure that the data quality remains high throughout the entire lifecycle of
the data and the implemented controls are in parallel with the objectives of the organization’s business. Data must be
used effectively and efficiently to deal with the organization’s demands. Data governance determines who can
perform a particular activity due to what data, in which situations, and using what methods.
5. Benefits of Data Governance
The significant benefits of data governance include delivering better data quality, reducing data management costs,
increased access to required data across the organization, lowering risks of errors being introduced, and ensuring
that clear rules regarding access to data are both set, enforced, and adhered. Data governance helps improve
business decision-making by providing the management with better and greater data quality, resulting in competitive
advantages and increased revenues.
6. Best Practices of data governance
On the one hand, you can master a lot from others who have been on a data governance journey. However, every
organization is different, and you need to learn the data governance practices all the way, starting from the unaware
maturity phase to the nirvana in the practical maturity phase.

5. Nevertheless, find below a collection of best practices that will apply in general:
• Start small. As in all aspects of business, do not try to boil the ocean. Strive for quick wins and build up ambitions
over time.
• Set clear, measurable, and specific goals. You cannot control what you cannot measure. Celebrate when goals are
met and use this to go for the successive win.
• Define ownership. Without business ownership, a data governance framework cannot succeed.
• Identify related roles and responsibilities. Data governance is teamwork with deliverables from all parts of the
business.
• Educate stakeholders. Wherever possible, use business terms and translate the academic parts of the data
governance discipline into meaningful content in the business context.
• Focus on the operating model. A data governance framework must integrate into your enterprise’s way of doing
business.
• Map infrastructure, architecture, and tools. Your data governance framework must be a sensible part of your
enterprise architecture, the IT landscape, and the tools needed.
• Develop standardized data definitions. It is essential to strike a balance between what needs to be centralized and
where agility and localization work best.

6. • Identify data domains. Start with the data domain with the best ratio between impact and effort to raise data
governance maturity.
• Identify critical data elements. Focus on the essential data elements.
• Define control measurements. Deploy these in business processes, IT applications, and reporting where it makes
the most sense.
• Build a business case. Identify advantages of rising data governance maturity related to growth, cost savings, risk,
and compliance.
• Leverage metrics. Focus on a limited set of data quality KPIs related to general performance KPIs within the
enterprise.
• Communicate frequently. Data governance practitioners agree that communication is the most crucial part of the
discipline.
• It’s a practice, not a project.