It’s estimated that the daily data generated will exceed 463 exabytes globally by 2025. But how will this vast sea of data be handled and used most effectively? How will it be best organized and managed?
This is where data governance and data management can save the day. Instead of drowning in an ocean of data, they work together to help us set data rules and make those data policies actionable and useful for decision making.
Let’s take a closer look at these two concepts, how they are different and how they complement each other to establish more effective ways of handling large volumes of data in your organization.
What is data governance?
Data governance refers to the rules governing a company’s overall plan for adequately using, processing, and storing information. A data governance program enables businesses to develop data policies. These are:
- Who has access to and uses data
- Determining data storage techniques and retention periods
- Establishing data quality standards
- Ensuring content protection
- Reducing the operational risks involved with sensitive data storage
Creating management for a company’s data requires a practical approach such as:
- Maintaining regulatory compliance
- Minimizing risks
- Improving data security
Data governance consists of numerous crucial factors that must all work together for success.
Data may be consumed by a variety of entities, including people, applications, and analytical models These data consumers influence data governance rules and have become increasingly specialized. The data stewardship role, to manage and meet the requirements of data consumers, is becoming more frequent in businesses. Often, organizations will also use data officers to enforce their data governance framework within company divisions.
Data policies and regulations
Companies must establish standards and processes to maintain data quality, monitor data repositories for accuracy and consistency, and reduce data duplication throughout the organization.
Data policies specify who has access to data, how long data is retained, and where specific data types can be stored.
What is data management?
Data management is primarily about executing on the established data governance strategy. It’s the process of placing the data governance plan’s standards and policies into action, which can include duties such as:
- Establishing and enforcing rules that govern who has access to what data
- Ensuring proper disposal of data at the end of its lifecycle
- Creating and maintaining data security protocols to comply with business information security policies
- Taking the necessary precautions to reduce the danger of storing sensitive data
- Developing a master data management system that provides a unified view of data across the company
Using a data management plan ensures that data is handled by policies throughout its entire lifecycle, from creation to retirement.
Several components can be included in a company’s data management strategies. Such as:
Using data management tools
IT teams in charge of data management must design and adhere to business processes, then leverage tools to achieve their goals. This typically requires a set of data management tools to ensure data quality, data accessibility, and auditability to track data access privileges and usage.
People are one of the most critical aspects of an effective data management process. Therefore, one of the cornerstones of data management is ensuring that people are not only informed about data policies, but consistently adhere to them throughout the organization
5 key differences between data governance and data management?
Consider data governance versus data management. Data governance is a comprehensive set of policies implemented throughout a company.
The definition of data management is more restricted. It focuses on carrying out the specific operations that enable data governance.
1. Data governance is about business rules, not data
Most company managers recognize that data is crucial but fail to manage and value it effectively. Without control and value, a company can’t make informed judgments with its data. Ungoverned information may actually be detrimental to an organization and lead to poor decisions resulting from wrong conclusions.
A data governance compliance program and data managers ensure a company’s data:
- Conforms to an acceptable level of data quality
- Is standardized and consistent across the organization
- Is accessible by data consumers who create business value
This is where business rules must be set to more effectively organize and use the data. A business rule is a declaration that specifies or constrains a business feature. A business rule identifies the organization’s structure or conduct.
Business rules are essential for data stewards. A business rule expresses actions and limitations on specific data and associated operations, such as:
- Creating data
- Updating data
- Deleting old data
- Distributing data
Data stewards’ responsibility to set business rules may help form a company’s standards.
2. Data governance is about enforcing consistency, not data
As a data-driven company, success depends on your ability to quickly and accurately make business decisions.
Data is consistent if it can be used by any data consumer in the organization, implying that there are no data set clashes. When data consistency is compromised, you may find yourself in a position where a data-driven process fails, or business decisions are delayed or inaccurate.
That is why maintaining data consistency is critical, particularly across large organizations where data originating from a diverse set of systems is used by a broad set of stakeholders. One important way this can be fostered is through a common data model, where data is classified via a known set of terms or units. This helps to promote data consistency and interoperability through standardization.
3. Data governance is about data agility, not data
Suppose you are establishing a data governance strategy within your business and wonder why some part of it stopped working. In that case, you may forget an essential component of your implementation: data agility.
Data governance demands an open and flexible approach and one that can adapt to shifting business or market requirements.
Say, for example, a new partner needs access to data. Or, a new operational system is deployed and needs to be incorporated into your data management strategy. Data governance requires the ability to rapidly respond to the changing conditions in your data ecosystem.
4. Data governance is about monitoring, not data
Monitoring data gives you the exact view you need to keep an eye on quality and back up governance efforts. Discrepancies can be automatically spotted through data monitoring, speeding up remedies for quality and governance purposes.
In the end, data monitoring is crucial because it ensures that personnel always have immediate access to the information they need to conduct day-to-day operations and make better decisions through enhanced analytics.
5. Data governance is about communication, not data
Effective communication is essential for the successful launch of any organizational project.
There are numerous reasons for communicating data governance and its relevance, some of which you are undoubtedly aware of. But, of course, the most straightforward response is that a company founded on the values of honesty and transparency, as most companies strive for, must, by nature, be transparent with its employees. This is especially so when it comes to something as critical to its everyday operations as data and governance. So let’s look at the how:
- When describing your policy, avoid using unclear terminology
- Avoid using business jargon that requires much knowledge to grasp, keep it simple
- Instead of waiting until the data governance plan is already in place, be proactive and gather input throughout the creation stage
- Be optimistic by emphasizing the new data governance program’s advantages for the company as a whole and, if applicable, at the individual level
- Participate more by enlisting as many supporters of the new data governance as possible
- Be approachable by helping people realize how the plan would change their life for the better
- Outline concrete instances relevant to their work and aspirations, and address their questions
- Be consistent by updating users about the data governance implementation, adjustments, achievements, and failures
- Innovation is critical, reach out to users both inside and outside the organization while keeping control of the messaging style
Of course, all the above must be data driven. Remember that your approach determines the effectiveness of your data governance program. This is from its conception to its implementation throughout your company.
A transparent, well-planned communication plan can turn any situation around with the correct language and confidence-building protections.
Data governance and data management work hand in hand
While data management and data governance are different, they complement each other and serve the same goal of building a robust, trusted data foundation that enables your company’s employees to perform their best work.
So, when it comes to data management vs. data governance, data management is the actual action needed. Data governance, on the other hand, is the strategic guidance on which the action is based.
Are you looking to implement a comprehensive data governance and management strategy? Intertrust has you covered. We help organizations define and execute their data governance strategies, and ensure trusted data interoperability between all stakeholders, inside the organization (and outside too). Contact us today to learn more!