Joining a Data Organization? What You Must Know about Data Lifecycle Management
Organizations are increasingly data driven, so they rely on heavy data to run every aspect of their business, from acquiring customers to creating new products. Following the data-driven approach to business is one of the leading trends today. More data is being generated than any other time in human history and it continues to expand across many sources.
Data utilized properly can do everything — from driving industry to improving medical care and even influencing elections. And to be useful, all data must be managed throughout its life cycle from the creation of that data all the way to its deletion. This can be really challenging if an organization does not have the right implementation of a data lifecycle management in place.
Consider data lifecycle management from the standpoint of a single piece of data moving through the company. A single piece of data, for example, is captured during the sales process and then entered into a database. This new data will then be accessed or shared for subsequent processing, reporting, analytics, or other purposes, or it may simply sit in the database and become obsolete. The data may even have logic of validations in the process, but this data will eventually become obsolete. Now, it needs to be archived, purged, or deleted.
The process of moving data from one stage of its lifecycle to the next is known as data lifecycle management. It defines and automates the stages of useful life and determines data prioritization, acting as the catalyst that drives data from one stage to the next.
Data life cycle management is not concerned with the individual pieces of data included within a given record, but instead with the record as a whole. Hence, it is a system that is designed to answer the question of when this information should be deleted.
Stages of Data Lifecycle Management
Before we get into the details of the 6 stages, let’s understand how these same names can be clubbed together to define even broader stages.
Everything about life is a data generating process. Hence, we can think of data generation as the first phase of the data lifecycle. Once that data has been generated it then must be collected, which is essentially the process of recording the information generated by life. Data collection could take the form of anything from surveys to medical records and sales data. And each organization will do this differently based on their needs.
After collection, this data has to be stored somewhere, either on a computer hard drive or a cloud-based server. Through analysis, we extract information and convert it into intelligence. That is the last stage of our data lifecycle and allows us to leverage this newfound intelligence in a way that adds real value or, simply put, allows us to act.
All data is created somewhere. However, a considerable quantity of data may be lost since it may be produced on personal drives, written down but not preserved, or contained in weird and unique data formats. As a result, data creation exists in data lifecycle management to maximize accurate data collection. It is about the acquisition of data from across the business based on where it is used, what it is used for, and who can use it.
The data collection process also needs to specify the types of files as well the sensitivity of the data because this is where governance begins to slot the data based on whether it is private, sensitive, restricted, or public. This safeguards the company’s intellectual property as well as its client connections.
This is where we do maintenance of data — we synthesize the data. Implementing data redundancy and security techniques, and storing data in ways that it cannot be unintentionally altered are all examples of data storage. We want to make sure that the stored data has no single point of failure and data storage must be a compliant part of the structure when we create our data lifecycle management strategy.
As an organization, our policy should make sure that data storage must be compliant with contracts and local laws. It is also important to define various data recovery plans so we have a plan for continuing to access the data by using a temporary backup while it recovers the drive.
A data lifecycle management policy or program should define who and what can use the data. Once we set up the previous two stages correctly, the data usage phase is relatively straightforward because users or systems or processes trying to access data will have their access mapped to the sensitivity.
During the utilization phase of the data lifecycle, data is used to support organizational activities. Data can be examined, manipulated, edited, and stored. To ensure that all data updates are fully traceable, an audit trail should be maintained for all critical data.
Usually, data usage and sharing are combined as one phase. Data may also be made available for sharing with others outside the company. Data sharing is the most vulnerable point in a poorly managed data lifecycle process. The more individuals who share data informally, the more likely it is that information is not in accordance with governmental or policy rules. We need to not only balance the needs of users to share data across different systems and users but also the sensitivity of that data.
Data archiving is about storing data after the active life of that data. Mostly, users and systems do not need a majority of the data on a day-to-day basis for operations. But once in a while, you may want to access the data; maybe for ad hoc, reporting or analytics purposes. Hence, data should always be archived before it is destroyed.
A good data lifecycle management strategy must define when, where, and for how long we need to archive some data.
This is the final stage in the data life cycle. The volume of preserved data rises gradually, and while you may like to save all of your data indefinitely, this is not possible. Storage costs and regulatory requirements put pressure on you to delete data that you no longer need.
The elimination of all copies of a data item from an organization is known as data destruction or purging. There are two sides to it at the core. There needs to be a strategy for destroying active data from the second stage (data storage) as well as archive data. This means complying with internal governance, policies, and local and international laws. So, basically, this step depends on the specific type and sensitivity of the data item.
In simple terms, data lifecycle management is the process, the policies, and procedures for managing data within an organization throughout its entire life — right from creation through retirement. It is not a specific product or protocol, but rather a comprehensive approach to data management in an organization.
It runs on a policy-based system that governs the flow of information between many applications, systems, databases, and even storage media throughout its useful life. The power of the data lifecycle is that it’s universal. The concept is not specific to one industry, and it can really empower whoever the end user is to make better decisions with the data they have.