Introduction to Data Governance and Data Management

Salesforce Data Management Guide for Admins | Smartsheet

Each association, small or huge, gathers information from multiple sources. This accompanies an obligation to guarantee that approaches, cycles, and devices are set up to deal with information in a steady and secure way while applicable laws and regulations.

With Salesforce org 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.

What is Data Governance?

While there are a couple of definitions for Data Governance, it is an assortment of standards and practices that oversee information from its beginning to its authentic/cleanse the “information 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 exhaustive definition that covers all parts 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.

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 practices, data governance can also help in growth and improved business outcomes.

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 test that organizations face is getting what information is now accessible to them. Proficient information the board rehearses helps to tackle these issues by keeping information coordinated, secure, and open for approved people.

What is the difference between Data Governance and Data Management?

How Does Data Governance Differ from Data Quality? | Redpoint Global

Despite the fact that data management is linked together, it is important to understand the differences between them. it is vital to understand the distinctions between them. This will allow you to focus and allocate the 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 to focus on “Management” aspects when implementing processes and tools to collect, store, organize, and process data to maintain quality and reliability.
On the other hand, “Governance” focuses on identifying the data and associated assets to ensure that the business 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.

Suppose that a Salesforce administrator chooses to carry out Dun and Bradstreet’s Optimizer device for constant information purging and information improvement, this is an illustration of information for the executives.
Be that as it may, assuming a business executed an information list of business elements or characterized the jobs and obligations related to overseeing information in the association (for example information stewards and approvers), this would be an example of Data Governance in action.

Another example can be seen in the construction industry.

The blueprint for how a building should be erected is analogous to data management, and the construction of that building is analogous to data management. Governance also includes the technology needed to achieve its goals, tools that help users build a catalog of business data, including the definition of business terms (and any synonyms for those terms) and related attributes. Some tools have the ability to score and evaluate data quality. Others may have capabilities related to data access policies and the ability to link to different systems to consistently apply data management policies.

In the table below, see how key differences between Data Governance and Data Management, both are key to reaping the benefits that data has to offer.





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