Data Quality

Data Governance vs Data Management (Difference Explained)

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When it comes to data handling, two key terms often conflated are data governance and data management. While they may sound similar, these critical concepts serve distinct purposes in the data landscape. 

Below, we’ll break down the nuances between the two to understand their roles and how they work in tandem.

What is data management?

Data management involves the organization, storage, retrieval, and processing of data throughout its lifecycle. It encompasses tasks like data integration, storage optimization, and data cleansing. Data management tools like Validity DemandTools can help optimize the process.

Data management focuses on operational efficiency and meeting the immediate needs of an organization’s data users.

What is data governance?

Data governance is the framework of policies, procedures, and standards that define how data should be managed, maintained, and used across an organization. It involves establishing rules for data usage, designating ownership and accountability, and providing the structure necessary for effective data management.

Data governance ensures compliance with regulations and standards.

What’s the difference between data governance and data management?

If you’re still wondering, “How is data governance different from data management?” at this point, the differences may become clearer once we break them down by scope, focus, and implementation.

Scope: 

  • Data management deals with the technical aspects of handling data, like storage and processing.
  • Data governance addresses the broader aspects of data strategy, including policies, compliance, and risk management.

Focus: 

  • Data management focuses on the day-to-day operations of handling data efficiently.
  • Data governance focuses on establishing rules and guidelines to govern data usage, ensuring its quality and reliability.

Implementation: 

  • Data management leverages tools and technologies to manage data more effectively.
  • Data governance establishes policies and procedures to govern data usage and employs data stewardship to ensure compliance with regulations.

How data governance and data management work together

Data governance and management are complementary processes that work hand in hand to ensure the effective use of data within an organization. While data management handles the technical aspects of data handling, data governance provides the framework and guidelines to ensure appropriate and responsible use of data.

This creates a symbiotic relationship wherein data management deals with the “how” of handling data, while data governance addresses the “who,” “what,” “when,” and “why.”

Documenting success with data governance

Implementing data governance is a significant investment for any organization, and measuring its success is crucial for ensuring that it delivers tangible benefits and returns on that investment. Here are some key metrics and approaches for measuring the effectiveness of data governance initiatives:

1. Data quality metrics

One of the primary goals of data governance is to improve data quality. Measure metrics like data accuracy, completeness, consistency, and timeliness to assess the impact of data governance efforts on data quality improvements. Regular data quality assessments and audits can provide valuable insights into areas that need attention.

2. Compliance and risk management

Evaluate the organization’s compliance with regulatory requirements, industry standards, and internal data management and privacy policies. Measure the reduction in compliance risks, such as data breaches or regulatory fines, as a result of implementing data governance practices.

3. Data access and security 

Assess data governance effectiveness by controlling and protecting sensitive data from unauthorized access or breaches. Monitor metrics such as user access controls, data encryption, and compliance with data security standards to ensure data security and privacy.

4. Data usage and consumption

Measure the usage and consumption of data assets across the organization to gauge the effectiveness of data governance in enabling data-driven decision-making and business outcomes. Track metrics like data utilization rates, user adoption of data governance tools and processes, and data governance impact on business performance.

5. Stakeholder satisfaction

Gather feedback from stakeholders, including business users, data owners, and IT professionals, to assess their satisfaction with data governance initiatives. Conduct surveys, interviews, or focus groups to understand stakeholders’ perceptions of data governance effectiveness, usability of data governance tools, and alignment with business objectives.

6. Time and cost savings

Measure the time and cost savings achieved through streamlined data management processes, improved data quality, and reduced errors. Quantify the reduction in manual effort, data processing time, and operational costs resulting from data governance initiatives.

7. Data governance maturity

Evaluate the maturity level of data governance practices and capabilities within the organization using frameworks such as the Data Management Maturity (DMM) model or the Capability Maturity Model Integration (CMMI). Track progress over time and identify areas for continuous improvement to advance the organization’s data governance maturity.

By monitoring these metrics and continuously assessing the effectiveness of data governance initiatives, you can demonstrate the value of data governance, identify areas for improvement, and optimize your data management processes to drive business success.

Useful tools for data management

As mentioned, data management relies on various tools and techniques to streamline data handling. Each tool serves a specific purpose in managing different aspects of the data lifecycle. For example:

  • Data cleansing tools: Identify and correct errors in data to ensure accuracy and consistency. Validity DemandTools is a secure data quality platform that enables organizations to clean and manage Salesforce data in less time, allowing everyone to do their jobs more effectively, efficiently, and profitably.
  • Data integration platforms: Streamline the process of combining data from multiple sources and help integrate data from various sources into a unified format for analysis.
  • Database management systems: Manage and organize structured data efficiently.
  • Data modeling tools: Design and visualize the structure and relationships within the data.

Handle your data with ease

In summary, while data management focuses on the technical aspects of handling data, data governance provides the overarching guidelines for its effective use. These processes manage data efficiently and responsibly to drive informed decision-making and business success.

That said, if you’re looking for more accuracy and profitability from your data, Validity DemandTools can help improve the performance of your sales, marketing, and support teams (plus, the revenue and retention they generate). 

To learn more, talk to our team of data experts today.