Data Lifecycle Management: A Simple and Complete Explanation
Enterprise data can no longer be thought of as separate chunks of information. Today, data is not only everywhere, but it’s also interconnected. At any given organization, the same dataset passes through multiple hands and is used to make informed decisions across departments. Enterprise data has evolved into a homogenous living thing that flows throughout organizational information systems. So how can organizations make the most of the large volumes of data they generated today? Data lifecycle management (DLM) can help towards this end—it ensures that organizations have the right strategies for managing their data. This post will walk you through everything you need to know about data lifecycle management.
What Is Data Lifecycle Management?
Data Lifecycle Management refers to a policy-based approach for managing data throughout its lifecycle so that it’s optimized from its creation to when it becomes obsolete or is deleted. It ensures that data management occurs in a consistent and reliable manner, regardless of where the data originated from or what its purpose is. Data lifecycle is typically broken into stages that start with the collection of data and end with its re-use or destruction.
Stages of Data Lifecycle Management?
Understanding the stages of the data lifecycle is essential to getting the best value from your data.
Each stage has value and provides outcomes that should be managed properly. Failure to manage data well throughout its lifecycle can result in inefficient data usage. Here’s a look at the 5 primary stages of DLM:
This is the first stage of data lifecycle. It refers to any input or source for generating data, including data acquisition, data capture, and data entry by applications, artificial intelligence (AI), machine learning (ML), and sensors.
That said, not all data that is generated is collected and utilized. Your team should be able to identify what information should be captured, the best way of capturing the data, and what’s irrelevant or unnecessary to the project at hand.
When an organization generates large volumes of data from multiple sources, it is common for them to use a data warehouse to store the data and prepare it for use. The data stored in the data warehouse is cleaned and analyzed such that it can be used to make informed decisions.
You should ensure that you store the data in a stable environment and properly maintain it to ensure its integrity, protection, and security.
What value do you accrue from your data? How are you leveraging data analytics results? In this stage, you need to align value with action. How is data shared and used within your organization? You need to establish rules that define the management of the transfer and publication of data and who can access sensitive data.
There are some data sets that can’t be destroyed immediately because they still have value from a compliance or historical perspective, and so they should be archived. The archived data is typically not active and is kept for long-term retention purposes. Most organizations leverage data warehousing capabilities for archived data that are rarely used for decision-making. They also use technology to retrieve such data if need be.
Keeping too much data increases the data management cost, thereby impacting the total cost of ownership and ROI (return on investment) of an organization’s products or services. While it’s mandatory to delete data at some point, you should also ensure that you free yourself by deleting active or archived data that doesn’t benefit your organization in any way.
Data lifecycle management has numerous crucial benefits that include:
When you collect data, there are strict rules that govern data collection that you must follow. Data lifecycle management can help ensure that you comply with data regulations relevant to your industry, such as the HIPAA in the U.S healthcare industry and Europe’s GDPR.
Data lifecycle management can also help you adhere to the rules governing the duration you should keep customer data. For instance, credit reference agencies are required to keep customer credit data for a period not exceeding six years. DLM will allow you to determine how long you’ve stored data and whether you should delete it to remain compliant.
The entire data lifecycle management process ensures that you have measures in place to manage your existing data. As a result, it minimizes the chances of duplication or storage of irrelevant data.
By clearly understanding your data’s lifecycle and properly managing it, you’ll be better placed to heed data quality rules across the various stages. You’ll therefore generate useful data that are crucial for sound business decision-making.
One area where data quality comes in handy is in sales and marketing. Marketers need high-quality data to determine their next move and campaign to enhance productivity. Making decisions based on the quality of data gives them the confidence that they are moving in the right direction.
The primary goal of DLM is to keep data secure. Today, more and more customers are concerned about the security of their personal data, especially given the rapidly increasing cases of data breaches and cyberattacks.
Data lifecycle management ensures that you establish protocols for managing data from the time of its creation to the time it’s deleted. As such, it helps you prevent that data from being accessed by cybercriminals and other unauthorized persons or being infected by malware.
Whereas one of the primary objectives of data lifecycle management is to ensure that data isn’t accessible to unauthorized persons, its equally important objective is to ensure that data is available to the right users at the right time. If data isn’t available, then several workflows and processes could either be interrupted or fail entirely. A good DLM strategy makes data available for users whenever they need it.
Now that you understand what data lifecycle management is and why your organization needs it, let’s look at the best practices for its successful implementation. While this isn’t an exhaustive list regarding everything you should do to create a sound DLM strategy, these are some of the key aspects to keep in mind.
The stages of the DLM process are roughly the same from one company to another (some stages may be split), and they translate into different practices. You must clearly define what to do with your data and how you’ll do it, right from its collection to its deletion.
You should also define your data types based on their importance and how and where they’ll be used. For instance, patient or customer data should be handled differently from accounting data. Each data type should also have a specific retention duration, archive policies, and safe destruction methods. You should also define your data backup and recovery plan. Develop a plan that works best for your organization.
Data lifecycle management encompasses the creation and execution of a data management plan to safeguard, manage, and preserve data at each stage of its lifecycle. As data ages, its value to an organization may diminish. A sound DLM strategy will identify, plan for, and adapt to various changes.
Your data lifecycle management strategy should also be clear on which steps and types of data various individuals can access. Additionally, you should train everyone in your organization on data security, why it’s important, and the rules they should follow.
Dealing with large volumes of data can be frustrating. As such, you not only need a well-oiled DLM process, but you also need the right tools. The tool should be able to provide vital data discovery, classification, and security at all the stages of the data lifecycle management. Using the right tools can help you manage data in the best way possible for your organization.
As your business expands, so will the size and complexity of your data. Data lifecycle management as a practice is vital to organizations of all sizes. Holistic management of data shouldn’t be treated as an afterthought but rather an essential function of an organization. Creating a structure based on DLM enables you to visualize the whole journey of your data across the entire organization.
When you have the full picture of your organization’s data, you will be better positioned to identify any vulnerability points where policies need to be instituted to ensure that your data stays safe. At the same time, it ensures that you make the most of your data to make sound business decisions.
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