Data Governance vs. Data Management in Salesforce

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Every organization, small or large, collects data from multiple sources. This comes with a responsibility to ensure that policies, processes, and tools are in place to handle data in a consistent and secure manner, while complying with applicable laws and regulations. 

If you searched for data management vs data governance, lots of results would provide some confusing explanations as to what they actually are. With Salesforce orgs generating tons of data (often consuming data from other systems), understanding governance and implementing management practices is more crucial than ever before. This article takes a look at the differences between the two and how they relate to each other. Let’s dive in.

What is Data Governance?

While there are a few definitions for data governance, I prefer to see it as a collection of principles and practices that govern data from its inception to its archival/purge – the “data life cycle”. 

Data life cycles refer to the various stages that data moves through:

  • Creation
  • Storage
  • Usage
  • Archival or Purge

It’s a simple yet comprehensive definition that covers all aspects of governance from the point at which data is generated. This overarching discipline touches almost every aspect of business today. More and more businesses are realizing the value of data; they are beginning to treat it as an asset to maintain, or even as an opportunity to gain a competitive advantage in an increasingly competitive global marketplace.

Data governance life cycle

As a result of effective data governance policies, businesses can ensure that their data is secure, trustworthy, properly managed, documented, and auditable, as needed. While compliance is one aspect that drives businesses to implement best practice, data governance can also help in growth and improved business outcomes. 

Data governance improves access to reliable data that is both usable and secure. In turn, this leads to improved decision making at all levels of the organization. Effective data governance also helps businesses avoid making haphazard decisions – decisions that could adversely affect their reputation, leading to trust issues within the organization, as well as with customers.

What is Data Management?

Data management is the process of storing, organizing, securing, and maintaining data. A key goal of data management is to implement measures that ensure data is viable to be used for its intended purposes only, and properly discarded when it ceases to provide value. 

Organizations are creating and/or collecting data at an ever-increasing rate, but merely storing data isn’t of much value unless something is done with it. Another challenge that businesses face is understanding what data is already available to them. Efficient data management practices help to solve these issues by keeping data organized, secure, and accessible for authorized individuals.

Data Governance vs Data Management: What’s the Difference?

Although data governance and management are linked with each other, it is important to understand the differences between them. This will allow you to prioritize and allocate appropriate resources. 

Data governance is a business strategy that is used to securely leverage data to create value and reduce data-related risks to the organization. 

Data management is primarily an IT (Information Technology) practice that is used to ensure accessibility, reliability, and security of data.

So, what does this mean in practical terms?

On the one hand, IT teams focus on “management” aspects when they implement processes and tools to collect, store, organize, and process data to maintain quality and trust. On the other hand, “governance” focuses on identifying data and the associated assets, to ensure the company can reap the benefits. This is not to say that data governance doesn’t require any tools to achieve its purpose, it just means that these tools serve a broader purpose. 

Let’s say that a Salesforce admin decides to implement Dun & Bradstreet’s Optimizer tool for continuous data cleansing and data enrichment – this is an example of data management. But if a business implemented a data catalog of business entities, or defined the roles and responsibilities associated with managing data in the organization (e.g. data stewards and approvers), this would be an example of data governance in action. 

Here’s another way of looking at it – auditors are tasked with defining policies and procedures to ensure financial reports are fair and accurate, whereas the financial management arm of the business executes on these policies. In a similar way, data governance pertains to the definition of processes and policies in relation to data and the people who interact with it. 

Another example can be seen in the construction industry. The blueprint of how a building should be erected is analogous to data governance, and the construction of that building is analogous to data management. Governance also includes the technology required to achieve its goals – tools that help users build a business data catalog, including defining business terms (and any synonyms for those terms) and related attributes. Some tools have the capacity to score and evaluate data quality. Others may have capabilities related to data access policies and the ability to connect with different systems to consistently apply data governance policies.

While the table below summarizes the main differences between data governance and data management, both are integral to reaping the benefits that data has to offer.

Summary

Data governance and data management are both important aspects of gaining valuable insights from data. I discuss these topics in more detail (amongst other data-related topics) in my book, Salesforce Data Architecture and Management. In writing this book, my intention was to help architects and developers understand critical topics, and assist them in implementing practices in their own organizations. 

Many professionals are already using tools to manage data in their Salesforce orgs (e.g. tools that dedupe or enrich data from various sources). However, data governance practices must also be implemented to ensure policies and processes are in place to help achieve strategic business goals. Ultimately, when it comes to data governance vs data management, both are essential parts of the puzzle.

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