Prolifics Guide to Data Privacy

Prolifics Guide to Data Privacy

Data Privacy

Data Management

Data Governance Framework

Enterprise Data Management

Big Data Integration Tools

Cloud Data Management Strategies

Data Governance in Healthcare

Government Data Privacy

Data Management



Data management processes are the basic building blocks of a data governance plan, which you will learn more about later. Effectively managing data is something that can provide your business with a variety of benefits and also ensures that anyone who is served by your business is provided with the data privacy that they are entitled to by law. 

In this brief section, you will learn about the specifics of data management, what the concept of data lifecycle management encompasses, and the main goals of data lifecycle management as they apply to a business.

 

What Is Data Management?

 

Managing data is a process that has a lot of moving parts. The simplest definition of data management comes from Oracle, which defines data management as "the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively."

What this means is that data management is how a specific business organizes the collection and use of data as a whole. Effectively managing data is critical to a business to ensure that it is compliant with all of the existing regulations that apply to it. It also helps to minimize risk and help inform the decision-making process.

The data management process includes several factors and it is important for businesses to have this process defined by formal guidelines to make sure that it remains consistent and that this process can be effectively implemented for all data assets that a business holds.

Each step in the process of how data is handled will have a specific task, procedure, or practice that applies to it. These steps are what come together to form the concept of a data governance framework. Some examples of how data management processes are used within a business include the following:

  • How data is created, accessed, and maintained
  • How data is stored
  • How data is used
  • How data privacy influences all stages throughout the data lifecycle
  • How data is archived
  • How data is eventually destroyed

Each of these individual components will make up the outline of data life cycle management that is used by a business. Sometimes this is referred to as a data management system. Each of these roles can be organized into six main concepts or functions of data management.


How Does Data Management Work?

When a data management system is implemented, there are six main functions that are all interdependent. Each of the functions is executed in multiple ways and can include programs, tools, practices, policies, and more.

The first of these is Data Access. Data management is implemented to make sure that all of the data can be retrieved or accessed from any source. Typically, this function is fulfilled with certain technologies. Data obviously can't be properly used if it can't be accessed.

Data Integration is the second function of a data management system. This is another function that relies on the use of various tools and technologies. This is the process that combines data to create outcomes and provide analytical results. This is one of the ways that effective data management can help inform business strategy.

Another function of data management is ensuring Data Quality. This is the practice of keeping data up-to-date and accurate as well as making sure that it is appropriate for its designated use. This is usually a practice-based function that can be supplemented with technology and protected through internal policy. This function ensures that all of the data remains valuable from collection to usage.

Data Governance is a concept that is directly interlinked with data management. This is the overall framework that houses the entirety of the data management system. Because of that, data management functions as a way to uphold data governance and inform the entire data governance framework of a business.

The fifth function of data management is Data Preparation. This is a task-based function that encompasses the collection, combination, and application of data as it relates to analytics. In order to be properly used in an analytical context, data needs to be concise and consistent across platforms before reports are generated. That is the role that this part of a data management system fills.

Finally, another component of data management that is important is what is known as a Business Glossary. This part of data management refers to a detailed outline of definitions, data owners, and so on. This is the means by which data can be tracked throughout the life cycle. This business glossary lays the foundation for effective Data Lifecycle Management.


What Is Data Lifecycle Management?

Data Lifecycle Management is another way to look at the process of data management. Data that is generated or collected by a business has, essentially, a life cycle. There is a distinct flow that data follows and DLM is the way that businesses can interpret and direct that flow until the data is no longer relevant. More specifically, Data Lifecycle Management is a means of automation for data management.

This life cycle that data goes through does not have a clear definition; however, most sources generally agree that there are seven distinct phases:

  1. Data Capture: In this phase, data is collected or created.
  2. Data Maintenance: This phase covers the processing of data in order to make it usable later on.
  3. Data Synthesis: This phase is not yet widely recognized; however, this includes the creation of data values using other data values and inductive logic.
  4. Data Usage: This is the phase that encompasses all applications of the data outside of this lifecycle. It also includes using the data in a way that supports or informs business tasks.
  5. Data Publication: Sometimes when data is used, it must be sent to other sources outside of the business, and that is what occurs in this phase.
  6. Data Archival: Data Usage and Data Publication are phases that may not occur on a one-time basis. Once data will no longer be used or published, it enters this phase. This is when data enters long term storage.
  7. Data Purging: In accordance to the data governance policies set forth by a business or regulations imposed by law, data must be destroyed at some point. This is the phase in which that takes place.

This is only one of the multiple attempts in data management circles to clearly define the data lifecycle. Not all data will pass through every phase and the characteristics of each phase may differ based on the specifics of a business.

The basic principles of DLM remain the same regardless of the specifics, though. As data passes through each defined phase, it slowly becomes obsolete. The automation of DLM helps ensure that the most recent and relevant data remains accessible at all times and that as data becomes less useful, it slowly phases out. The purpose of this process is clearly outlined when you take a look at the problems that Data Lifecycle Management seeks to solve.


The Three Main Goals of Data Lifecycle Management

Data Lifecycle Management can be viewed as a means of maintaining data throughout its lifecycle or its usefulness for a business. The overarching goal of DLM is to keep information organized and streamlined as it flows through its lifecycle. With that said, Data Lifecycle Management can be broken down into three main goals.

The first of these goals is maintaining data security and confidentiality. Businesses collect and process large volumes of data and the implementation of Data Lifecycle Management seeks to organize that data and the process through which it is collected, used, stored, and then disposed of. This helps to make sure that data is not misused, accessed in a way that is unauthorized, or compromised by an external hacker or internal system corruption.

The second main goal of Data Lifecycle Management is the availability of data. This effective means of organization helps to keep the right data the most readily available during the Data Usage and Data Publication phases of its life cycle. This is essential because if the necessary data isn't available, it can cause a series of failures that can compromise the security of the data and can have significant negative impacts on the efficiency of a business.

The final main goal of Data Lifecycle Management is maintaining the integrity of the data assets within a business. Data is subject to edits throughout its life cycle and DLM is one of the ways that the integrity of that data is maintained. This includes keeping the data accessible to all of those who need to be able to access it, ensuring that it is correct and up-to-date across all access points, and more.

One thing that is worth noting about Data Lifecycle Management is that it does something very important and that is create redundancy. When it comes to data privacy, having multiple instances of the data that is accurate and properly stored helps ensure that it is safe from loss or any manipulation.

Facilitating a strong data governance plan is one of the best ways that a business can support data throughout the life cycle.

Data Privacy

Data Management

Data Governance Framework

Enterprise Data Management

Big Data Integration Tools

Cloud Data Management Strategies

Data Governance in Healthcare

Government Data Privacy


Data Management


Data management processes are the basic building blocks of a data governance plan, which you will learn more about later. Effectively managing data is something that can provide your business with a variety of benefits and also ensures that anyone who is served by your business is provided with the data privacy that they are entitled to by law. 

In this brief section, you will learn about the specifics of data management, what the concept of data lifecycle management encompasses, and the main goals of data lifecycle management as they apply to a business.


What Is Data Management?

Managing data is a process that has a lot of moving parts. The simplest definition of data management comes from Oracle, which defines data management as "the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively."

What this means is that data management is how a specific business organizes the collection and use of data as a whole. Effectively managing data is critical to a business to ensure that it is compliant with all of the existing regulations that apply to it. It also helps to minimize risk and help inform the decision-making process.

The data management process includes several factors and it is important for businesses to have this process defined by formal guidelines to make sure that it remains consistent and that this process can be effectively implemented for all data assets that a business holds.

Each step in the process of how data is handled will have a specific task, procedure, or practice that applies to it. These steps are what come together to form the concept of a data governance framework. Some examples of how data management processes are used within a business include the following:

  • How data is created, accessed, and maintained
  • How data is stored
  • How data is used
  • How data privacy influences all stages throughout the data lifecycle
  • How data is archived
  • How data is eventually destroyed

Each of these individual components will make up the outline of data life cycle management that is used by a business. Sometimes this is referred to as a data management system. Each of these roles can be organized into six main concepts or functions of data management.


How Does Data Management Work?

When a data management system is implemented, there are six main functions that are all interdependent. Each of the functions is executed in multiple ways and can include programs, tools, practices, policies, and more.

The first of these is Data Access. Data management is implemented to make sure that all of the data can be retrieved or accessed from any source. Typically, this function is fulfilled with certain technologies. Data obviously can't be properly used if it can't be accessed.

Data Integration is the second function of a data management system. This is another function that relies on the use of various tools and technologies. This is the process that combines data to create outcomes and provide analytical results. This is one of the ways that effective data management can help inform business strategy.

Another function of data management is ensuring Data Quality. This is the practice of keeping data up-to-date and accurate as well as making sure that it is appropriate for its designated use. This is usually a practice-based function that can be supplemented with technology and protected through internal policy. This function ensures that all of the data remains valuable from collection to usage.

Data Governance is a concept that is directly interlinked with data management. This is the overall framework that houses the entirety of the data management system. Because of that, data management functions as a way to uphold data governance and inform the entire data governance framework of a business.

The fifth function of data management is Data Preparation. This is a task-based function that encompasses the collection, combination, and application of data as it relates to analytics. In order to be properly used in an analytical context, data needs to be concise and consistent across platforms before reports are generated. That is the role that this part of a data management system fills.

Finally, another component of data management that is important is what is known as a Business Glossary. This part of data management refers to a detailed outline of definitions, data owners, and so on. This is the means by which data can be tracked throughout the life cycle. This business glossary lays the foundation for effective Data Lifecycle Management.


What Is Data Lifecycle Management?

Data Lifecycle Management is another way to look at the process of data management. Data that is generated or collected by a business has, essentially, a life cycle. There is a distinct flow that data follows and DLM is the way that businesses can interpret and direct that flow until the data is no longer relevant. More specifically, Data Lifecycle Management is a means of automation for data management.

This life cycle that data goes through does not have a clear definition; however, most sources generally agree that there are seven distinct phases:

  1. Data Capture: In this phase, data is collected or created.
  2. Data Maintenance: This phase covers the processing of data in order to make it usable later on.
  3. Data Synthesis: This phase is not yet widely recognized; however, this includes the creation of data values using other data values and inductive logic.
  4. Data Usage: This is the phase that encompasses all applications of the data outside of this lifecycle. It also includes using the data in a way that supports or informs business tasks.
  5. Data Publication: Sometimes when data is used, it must be sent to other sources outside of the business, and that is what occurs in this phase.
  6. Data Archival: Data Usage and Data Publication are phases that may not occur on a one-time basis. Once data will no longer be used or published, it enters this phase. This is when data enters long term storage.
  7. Data Purging: In accordance to the data governance policies set forth by a business or regulations imposed by law, data must be destroyed at some point. This is the phase in which that takes place.

This is only one of the multiple attempts in data management circles to clearly define the data lifecycle. Not all data will pass through every phase and the characteristics of each phase may differ based on the specifics of a business.

The basic principles of DLM remain the same regardless of the specifics, though. As data passes through each defined phase, it slowly becomes obsolete. The automation of DLM helps ensure that the most recent and relevant data remains accessible at all times and that as data becomes less useful, it slowly phases out. The purpose of this process is clearly outlined when you take a look at the problems that Data Lifecycle Management seeks to solve.


The Three Main Goals of Data Lifecycle Management

Data Lifecycle Management can be viewed as a means of maintaining data throughout its lifecycle or its usefulness for a business. The overarching goal of DLM is to keep information organized and streamlined as it flows through its lifecycle. With that said, Data Lifecycle Management can be broken down into three main goals.

The first of these goals is maintaining data security and confidentiality. Businesses collect and process large volumes of data and the implementation of Data Lifecycle Management seeks to organize that data and the process through which it is collected, used, stored, and then disposed of. This helps to make sure that data is not misused, accessed in a way that is unauthorized, or compromised by an external hacker or internal system corruption.

The second main goal of Data Lifecycle Management is the availability of data. This effective means of organization helps to keep the right data the most readily available during the Data Usage and Data Publication phases of its life cycle. This is essential because if the necessary data isn't available, it can cause a series of failures that can compromise the security of the data and can have significant negative impacts on the efficiency of a business.

The final main goal of Data Lifecycle Management is maintaining the integrity of the data assets within a business. Data is subject to edits throughout its life cycle and DLM is one of the ways that the integrity of that data is maintained. This includes keeping the data accessible to all of those who need to be able to access it, ensuring that it is correct and up-to-date across all access points, and more.

One thing that is worth noting about Data Lifecycle Management is that it does something very important and that is create redundancy. When it comes to data privacy, having multiple instances of the data that is accurate and properly stored helps ensure that it is safe from loss or any manipulation.

Facilitating a strong data governance plan is one of the best ways that a business can support data throughout the life cycle.