The larger a business becomes, the more difficult it gets to manage the data it generates in a consistent and safe manner. While this might seem like a benign issue at first, the consequences of poorly managed data can quickly lead to things like data sprawl, lost files or privacy violations that can severely harm a company.
- A proper data lifecycle management strategy is crucial for any business once it reaches a certain size.
- DLM not only ensures the security, accuracy and availability of an organization’s data, but also compliance with various data regulations.
- Once you’ve created a data lifecycle management system, you can go even further and implement an information lifecycle management strategy to achieve even greater insight and control over your organization’s data usage.
Understanding exactly what data lifecycle management (or DLM, for short) is can be daunting, as the topic can be overwhelming for people without a background in the field. That’s why we’ve put together this crash course on DLM. This article will give you a better idea of what DLM is, how you can implement it and its benefits.
DLM is crucial for any business environment to ensure that data is handled responsibly so that it is secure, accessible and accurate.
The three primary goals of DLM can be summarized as: confidentiality (the data can only be accessed by those with authorization), availability (the data can be accessed when it’s needed) and integrity (the data is consistent for all users).
The three stages of the data handling lifecycle are data creation and acquisition, data retention and protection, and data retirement.
Data Lifecycle Management (DLM) Defined
Data lifecycle management is the process of managing information, following the life of data from the moment it’s first created and stored, up to the time it gets archived or destroyed when it stops being useful.
This constitutes the entire lifespan (or lifecycle, if you will) of any piece of information. This ensures that data is managed in a consistent and responsible manner, no matter its origin or purpose. All of this is achieved through automated policies that are defined in advance, to minimize the potential for human error anywhere in the process.
Data Lifecycle Management vs Information Lifecycle Management
In a broad sense, data lifecycle management and information lifecycle management (ILM) are the same thing. However, ILM operates on a much larger scale because it takes into account a greater amount of information and data than DLM does.
DLM is primarily concerned with what you might call metadata (a file’s age, name, edit times, size, etc.), whereas ILM also involves file-specific data, such as phone numbers, email addresses or pretty much anything else you can think of.
Because the difference is just one of scope, any ILM system inherently includes a data lifecycle management framework as its core. Thus, before you even start thinking about a comprehensive ILM process, you’ll need to set up your data lifecycle model first.
The Three Stages of Data Lifecycle Management: Create, Retain, Retire
Data lifecycle management is usually divided into three separate stages that cover data throughout its life. These are data creation and acquisition, data retention and protection, and then data retirement. Occasionally, these stages are further separated into smaller ones, like data collection, data protection and data destruction.
1. Data Acquisition and Creation
This first stage is exactly what it sounds like. This is when the data or file is created or transferred into the system, which includes creating and naming the file, classifying it for easy retrieval and determining access controls. For example, files can be marked as “internal” or “external,” which then decides how tight of a lid it needs to be kept under.
2. Data Retention and Protection
Once the data has been acquired, it needs to be stored and maintained, which is what this second stage covers. This includes ensuring data redundancy (by backing up), safety (by employing sufficient protections such as strong encryption and physical server security) and accuracy (by continually validating and auditing files to ensure accurate data).
3. Data Retirement (Archiving or Destruction)
When data is no longer useful, it needs to be responsibly retired. This can mean archival storage for information that needs to be retained or potentially referred back to in the future, or destruction for files that are outdated or otherwise useless.
What Are the Benefits of Data Lifecycle Management for Businesses?
Now that we’ve covered what exactly DLM is, it’s time to look at how it can serve the interests of your business, including what sorts of nasty problems you can avoid by implementing it.
Properly implemented data lifecycle management:
- Ensures that you comply with any data regulations relevant to your industry, such as Europe’s GDPR or the HIPAA in the U.S. healthcare industry
- Helps prevent data sprawl by establishing procedures for data storage in a centralized location
- Ensures that data is always up to date and available for retrieval by anyone with the correct authorization
- Makes sure that you’ll have full control over all your company’s files, so they’re much less vulnerable to external (or even internal) attacks
- Provides agility and efficiency in data use because your organization’s files will be easily accessible
Data Lifecycle Management (DLM) Best Practices
Now that you know what DLM is and why your business should implement it, let’s discuss the best practices for implementation. Although this is by no means an exhaustive list of everything you need to know when creating a data management policy, these are some of the most critical things to remember.
- Create and define data types that govern how each file type will be handled. These types of data can be anything from receipts to customer data to anything else that an organization might have as a record.
- Use a consistent naming scheme that is applied across all of your organization’s data. By ensuring that everything is named appropriately, data becomes easier to manage and access.
- Save new data through an extensive backup plan. This ensures redundancy and protects you from potential data loss from accidents, disasters and ransomware (check out our ransomware statistics for more on that). One way to go about achieving this kind of redundancy is by using a business cloud storage service to store your data.
- Consider implementing an enterprise file sync and sharing service (or EFSS for short) such as Egnyte Connect, which features multiple tools for automating the handling of information throughout all the phases of its useful life. You can check out our Egnyte Connect review for more information about how it can help you create data-storage policies.
- Archive data if it is seldom used, or if it simply needs to be retained to meet regulatory standards. Examples of commonly archived data are things like customer records or transaction receipts.
- Clear up space by destroying data when it reaches the end of its useful life. However, a proper destruction or deletion policy is critical to ensure that nothing that should be archived for later reference is accidentally deleted. It’s important that your business is aware of any regulations that apply to its activities, and treat data accordingly to ensure compliance.
With that we’ve reached the end of our short guide on data lifecycle management. Hopefully you now have a clearer understanding of how your organization or company should ideally handle every piece of data, as well as how you can go about implementing a robust data management system (we also have a guide to what data governance is).
What did you think of our crash course on data lifecycle management? Did we answer all your questions? Are you still as confused as you were when you started? Has your company already taken steps to implement DLM? If so, what benefits have you seen as a result? Let us know in the comments below. Thank you for reading.