Businesses collect data from a wide variety of sources, from customers to third parties, and how that corporate data is handled and stored will impact whether it’s useful. To make sure your data is secure and accessible, data governance practices should be followed closely and updated regularly to keep up with new data practices. Data governance also needs to be outlined clearly and shared directly with each employee, though it’s up to your managers to enforce the policies and ensure compliance.
Read on to learn more about data governance, best practices and why it should be at the top of your priority list.
of organizations are taking data quality needs more seriously in light of COVID-19-related data decay
*(source: Validity Research: State of CRM Data Health 2022)
What is data governance?
According to the Data Governance Institute, data governance is “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
Essentially, data governance refers to a set of principles and processes that formally manages a company’s data through every stage of its lifecycle.
Each and every company will have its own data governance framework due to industry and company-specific needs and requirements. However, the shared goal of data governance is to guarantee that data is secure, high quality and relevant to the business’s goals.
With proper data governance, your teams can benefit from streamlined data processes with ensured compliance and accessibility even as your company collects increasingly more data.
Data governance at the macro level vs. the micro level
It’s important to distinguish between the two definitions of data governance, which differ on the macro and micro levels.
On the macro level, data governance is a political concept. It refers to the governing of data between countries, which is also known as international data governance.
On the micro level, data governance is a data management concept, as previously discussed. This type of data governance will be the concept covered in this article as it encapsulates the principles and rules of corporate data governance.
Data governance vs. data management
Data governance details and documents the corporate data policies and procedures that ensure your company’s data can be leveraged for current and future use. On the other hand, data management works to put these policies into action. In other words, data governance acts as the blueprint, while data management is the actual execution.
What are the components of data governance?
Your data governance framework should incorporate the best ways to identify and fix or remove low-quality data. To keep a competitive edge in today’s fast-evolving data landscape, data quality is an integral part of your data governance strategy.
See how DemandTools ensures your data remains your most valuable asset.
Most companies are handling some form of customers’ personal data, from medical records to email addresses to financial information, which makes data privacy and regulatory compliance a cornerstone of a comprehensive data governance program.
An up-to-date data governance framework will ensure that all data compliance laws and regulations are in place before data is even collected. This allows your company to track the data it captures from source to accessibility to use.
Similar to data privacy, data security is a compliance necessity within data governance processes. Some ways to guard data against security breaches include:
- Multi-factor authentication
- Activity monitoring
Malware is a huge threat to the sensitive data housed by your company’s data warehouse. If your business suffers from digital security threats, any potential leak can hurt — or even destroy — your company’s future success.
Your data security should track:
- The source of your data
- Where it’s housed
- Use of data
- Disposal of unnecessary or inaccurate data
All of these details and more should be a part of your data governance framework — especially as your company’s data assets grow over time.
Data software tools
The answer is through top-of-the-line data software tools that can monitor and analyze data effectively, efficiently and securely. By utilizing a data management platform your company can maintain the quality of your data without wasting time and additional resources.
These types of tools can help managers set guidelines and accountability measures for all data users, which will improve data governance.
Learn more about data governance in our eBook “The Three Fundamental Pillars of CRM Data Management”
Why does data governance matter?
If your data governance hits every component successfully, your business can organize critical data so it’s always accessible to those who need it, when they need it. This can benefit your company enormously, guaranteeing:
- Higher-quality assurance. Established data governance will lead to more effective data, as handling and use are standardized to reduce data errors and clutter.
- Better decision-making and business planning. Clearly defined data governance protocols empower employees to feel confident in the quality of your company’s data.
- Faster and improved compliance. Your company will be able to profit from decreased costs and resources in other areas of data management, including data security and privacy.
Data is your strongest company asset, and data governance will ensure that it won’t become a liability in the long run. As an ongoing process, data governance is an investment into the maturation of your data over time. It’s in the best interest of every company, across any industry, to implement a comprehensive, and consistently improving, data governance framework.
of organizations agree that data is the lifeblood of their company and a key growth driver
*(source: Validity Research: State of CRM Data Health 2022 )
Who is responsible for data governance within an organization?
However, there are those who have primary governance responsibilities. Below we will discuss who is responsible for data governance in your organization:
Data Governance Manager
This is the person, such as a Chief Data Officer (CDO), responsible for data use and integrity across the business. They are in charge of setting the data governance framework and ensuring it is implemented across various systems and business processes.
Their responsibilities include:
- Collaborating with the steering committee and data stewards to standardize data approaches between business units.
- Giving final approval to data glossaries and definitions.
- Ensuring data quality.
- Administration of master data management (MDM).
- Sharing and training colleagues on data governance frameworks.
- Providing feedback to the steering committee.
The CDO or data governance manager usually heads up and helps create the steering committee.
Cross-functional steering committee
For data governance, the steering committee is responsible for informing general strategy, as well as maintaining deadlines and achieving previously set goals.
Only 20% of organizations use a cross functional team to manage their data
*(source: Validity Research: State of CRM Data Health 2022 )
How to implement data governance
First, your chief data officer or data governance manager needs to establish company-specific goals and decide how you measure your progress toward them. This will help them build a basic setup for your policies, which will then lead your company to the technology and teams required to effectively carry out data governance processes.
Once your company has done this, it can then identify the data stewards and steering committee members of your company’s data assets and business units. Collaborate with these employees and involve them in the ongoing evolution of your data governance framework whenever possible.
As the data governance framework is finalized, document and internally distribute the policies to all employees that will be working with data within your company. Implement training and education resources to ensure that all data rules are being followed to compliance standards and regulations.
What are the challenges of data governance?
Implementation of data governance processes and programs is challenging for most organizations — especially as they evolve and consistently change with new data policies. This is where it’s important that data stewards or system admins are involved in steering committee meetings and updates in order to resolve any difficulties in practice.
Below are additional challenges that may face your data governance framework:
To educate each person that interacts with data in your company, it’s essential that your data governance framework details exactly how its policies improve the employee experience.
For example, data governance improves data quality. Outline how these policies will help to address data errors, such as data duplication, which can waste important time, money and resources for your sales and marketing teams. For C-suite executives, document the correlation between high-quality data, maintained by good data governance, and successful business decisions.
Organizational leaders should engage with different team managers to discuss how certain data governance policies or issues affect their specific work. This will promote policy clarity.
Acceptance and communication
Make sure every steering committee meeting is well-documented so that all relevant stakeholders can be kept up-to-date on data governance plans, changes and achievements. Transparent decision-making and change management is best practice for data governance.
Meanwhile, data stewards and system admins should be properly prepared to implement all data governance policies. They are often the go-between for the steering committee and the daily users of your data and business systems. By effectively communicating plans, organizations can achieve company-wide acceptance of data governance processes.
Budgets and stakeholders
The solution is to demonstrate its overall business value. A good basis for this is to give real-life examples of company issues that arose from bad data quality — which grew from little to no data governance practices. Another method is to show how specific data governance program policies can help achieve overall company goals.
Once implemented, your organization should set up quantifiable ways to measure data quality improvements as a result of data governance. Similarly, share materials that show the correlation between data governance, and data quality and successful business strategies and results.
Standardizing and flexibility
Beyond standardization, your company also needs to be flexible to adjust to changing needs and requirements. Open communication and the right data management tools can help your company uphold and implement successful data governance practices.
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