Data Lifecycle Management

The data that drives organizations has a life cycle. Far from occurring organically or independently of user input, the stakeholders in an enterprise are responsible for data lifecycle management at every stage of this process. Data collection, usage, maintenance, publication, storage and deletion are governed by accessibility and security considerations. Find out more about the stakes at each stage of the data lifecycle and how an organization can better manage data across workflows.

Defining the Data Lifecycle

The term “life cycle” is often used for living organisms that are capable of growth and reproduction. Data is not autonomous in these regards, which makes data life cycle management the driving factor behind the responsible collection and usage of information on the part of an organization.

Data cycles do not have an inherent order, though several logical dependencies govern the information ecology of any enterprise. Data must be captured or created in order to enter the lifecycle, which involves the entry of new values into a closed system.

Once data exists, it must be maintained in a system until it has been used. Maintenance may also involve measures to improve the quality of available information, such as cleaning or enrichment. It is also essential that data be accessible to the stakeholders who rely upon it during particular stages of a workflow for the completion of tasks.

An organization may pursue extract-transform-load processes with data. In other cases, movement or integration into systems or across platforms may be critical to maximize the benefit of obtaining and maintaining information in raw or processed forms. It is crucial to account for all of the mechanisms and methods applied to data to account for every aspect of the life cycle.

The publication of data may take the form of making information available in internal reports that will guide future operations or external reports presented to clients or the public. Reporting may call for the latest data or juxtapose readings from different points in time. In general, publication presents performance indicators or outcomes in the context of progress toward short- or long-term goals. This stage is essential for delivering outcomes that inform ongoing operations.

Data storage and recall are necessary to perform a wide variety of analytical functions. Security is often a primary concern during the usage, transfer, storage and purging of data. In some cases, stored data may need to remain accessible for recovery for future reference, which will extend the data lifecycle.

Identifying Data Management Phases

The specific phases of the information lifecycle management process vary in each organization. Data generally passes through the following broad phases:

• Creating Data: Stakeholders acquire or gather data from sources or retrieve readings.
• Maintaining Data: Data entry into systems may include enrichment or standardization.
• Using Data: Access to data informs analytical work or synthesis.
• Publishing Data: Stakeholders present results in internal or external reports.
• Storing Data: Systems securely store data for future reference.
• Purging Data: Data is destroyed when access is no longer needed.

The data lifecycle at any given organization may not include all of these steps. In general, data is always created, generated or supplied and must be maintained for use. Depending on the function of an organization and the type of data, modes of usage and publication may vary.

Sensitive data may be subject to more rigorous compliance or regulatory standards. For instance, the data management practices of an organization that processes records that fall under the purview of the Health Insurance Portability and Accountability Act for medical data or the Family Educational Rights and Privacy Act for student records must prioritize security at every stage of the data lifecycle.

An organization that is seeking to improve the quality and usefulness of data or adhere to security standards should strive for a clear understanding of the unique data lifecycle implicated in existing workflows. Breaking down the stages of a workflow and considering all of the ways that data factors in can be essential for drawing more valuable conclusions and pursuing comprehensive optimization.

Drawing Data Lifecycle Insights

Every stage of the data lifecycle has the potential to offer distinct insights into organizational operations. It can be difficult to obtain precise information about the creation, quality, status, availability and usefulness of information without a life cycle app collecting data. Automating reporting is the best way to create audit trails and gain access to the data and metadata necessary for large-scale insights.

The analytics capabilities of a platform such as Fluix stand out as the best way to monitor and derive useful information about every stage of a data lifecycle and derive useful information. From internal tracking capabilities to support for leading business intelligence integrations, this workflow management software provides organizations with all of the resources necessary to manage and monitor the data life cycle across every stage of operational workflows.

A few of the benefits of tracking data lifecycles and operational workflows in Fluix include key performance indicator-based reporting features and service level agreement status indicators. These elements have important functions for productivity as well as the level of detail and quality of information for use in assessing each stage of data processing and the comprehensive lifecycle of organizational information.

Making a Data Management Cycle Model

A data processing life cycle diagram differs from more general workflow models. Rather than focusing on the roles of agents, staff or stakeholders, or outcomes, this approach prioritizes the function of information in an organizational workflow. While it is impossible to completely differentiate data from broader operational contexts, a model for data management should keep the spotlight on data.

Models that incorporate data collected with field equipment might acknowledge the devices used to take readings or the frequency of measurements, but the types of data that enter the information ecology of an enterprise at this point are the primary focus. The modes by which data is maintained and measures are taken to improve the quality of usefulness of readings should also be acknowledged in a model. Once again, the specific effects of these modifying factors and differences between raw and processed data are crucial.

Methods of data analysis, synthesis and usage must also be acknowledged in an information life cycle model. A model should also account for the manner in which regulatory requirements are satisfied at each stage of the information-management process. This can be particularly important for organizations pursuing process automation. Depending on the types of data in use, these factors may have significant implications for data transmission, publication, storage and deletion protocols.

While it may be helpful to create a conceptual model in advance of implementing workflow management software, the audit trails and logs that Fluix generates can flesh out the details of data generation and usage to provide more in-depth insight into each stage of the data life cycle. The analytical functions of this software are backed up with utilities for maximizing functionality and compliance across workflows.

Implementing Data Lifecycle Management Tools

The main benefit of a workflow management system is its ability to track the applications and usage of data across automated and manual tasks and processes. This software can support a wide range of data creation and entry methods, including signals received from devices used by field services companies.

Whether data entry is done manually or automated, workflow management software makes it easier to pinpoint the start of the data lifecycle and maintain the highest standards with regard to information quality and security throughout maintenance, use, publication, storage and deletion. Fluix features utilities and supports integrations for cleaning and enriching data. Stakeholders can use this software to ensure the best data is available in the most usable formats throughout the data lifecycle.

When it comes to compliance, Fluix comes with the capacity to ensure that information is secured or encrypted, if necessary. From maintenance to storage and purging data, stakeholders can take the manual measures or easily set up automated processes in a code-free, intuitive interface with the support necessary to ensure that data is always appropriately managed.

An organization seeking to optimize every stage of the data lifecycle stands to benefit in several ways. The data that is collected, accessed and used may be of higher quality and more pertinent to the workflows of any enterprise. Transmission, storage or purging of this information may also be made more secure. The right workflow management software has benefits that extend from the data lifecycle to other aspects of operations.