A Marketer’s Guide to Data Lifecycle Management (DLM)

A marketer analyzes data on his desktop machine

A Marketer’s Guide to Data Lifecycle Management (DLM)

Since every piece of data gathered by your business varies, each piece should be handled differently. What’s more, the ideal way to handle all that data will inevitably change over time. Do you really need to hold on to years-old customer support tickets for a software you no longer offer? Of course not. 
However, developing a robust system for revising data management protocol and guidelines doesn’t happen automatically. You can’t hire one employee to handle one piece or type of data and expect to operate efficiently. Having a single team member to handle all data creates a communications and operations bottleneck you may not be able to overcome. Everyone on your team needs data access and should be able to solve simple problems surrounding data management.
Data Lifecycle Management (DLM) can help take the stress of data management off of your team. If you’re a business owner or executive, understanding DLM will allow you to structure your business and optimize the flow of data. If you’re a marketer, DLM can help you improve lead generation, lead processing, and flow. Below, learn everything you need to know about what data lifecycle management is and how it can be beneficial for your business.

What Is Data Lifecycle Management?

DLM is the practice of automating data lifecycle processes. The phrase “data lifecycle” refers to the different stages of use and usefulness a piece of data traverses over time. From collection to retrieval to removal, DLM helps you manage your data with care and efficiency. 
With DLM, you can organize pieces of data into tiers. Each tier will have different protocols associated with it. DLM will both instruct users on what to do with a piece of data based on its tier and automate any management processes well-suited to automation. You can also automate migration of any piece of data to ensure that its lifecycle is always tracked accurately and that the most relevant data remains easily accessible.

Three Main Goals of Data Lifecycle Management

The three main goals of DLM are security, availability, and integrity. A good DLM platform will offer all three.


Cybercrime threatens businesses both large and small in every sector. Many cyber crimes target contact information, payment information, and other sensitive data sets. The larger the number of services managing your data, the more points of entry there are for potential cyber criminals. DLM is no exception; it’s another link in the chain, and it must stay strong in order to protect the privacy of your business, employees, and customers.


While your data needs to stay entirely out of reach of intruders, authorized users should be able to have the information they need at all times. Inefficient security systems, poorly designed interfaces, or reliance on inconsistent network connections can produce a lot of friction for users and hamper business operations. If accessing your data becomes much harder than logging into your bank account, you may be losing out on some of the efficiency that DLM could otherwise offer.


Information is only helpful when it’s accurate. DLM that fails to confirm the accuracy of the data it generates or manages is essentially useless. Errors can also occur during data migration, introducing further risk of inaccuracy and complications. You don’t want to spend all of your time managing the data management software. High-integrity DLM will simply just work, allowing you to worry less about data management and focus more on important tasks.

Data Lifecycle Management Framework

Using a basic framework, there are many different ways to model the data lifecycle and its five stages. Here’s a generalized five-step framework to help you visualize your business’s data lifecycle management:

  • Collection
  • Storage
  • Maintenance
  • Usage
  • Cleaning

Here’s a breakdown of what each step entails.

Data Collection

Users, devices, applications, and machinery continuously generate data. Data capture methods differ by industry and data source, but businesses usually have mechanisms in place to either manually or automatically distinguish valuable data from noise. Once you fish valuable data from the vast sea of potential inputs, you’ll need to figure out where to store it.

Data Storage

Businesses must store data in a stable environment in order to ensure they can retrieve it later. Once you’ve made the data amenable to organization, you’ll usually migrate it onto some form of storage media, either with hardware or in the cloud. A newly collected piece of data should live in a readily accessible place, but may move later. Your data storage solution may automatically compress or alter your data to make it easier to store.

Data Maintenance

Your storage environment should be extremely secure in order to protect privacy and prevent cybercrime. Always keep a backup of all of your data in case something goes wrong and you can’t access your primary data repository. Consider implementing automated redundancy to back up your data without having to double check. These are the basic steps of good data maintenance.

Data Usage

Using data is the whole point of collecting, storing, and maintaining it. This is the stage at which users can access data and use it to contact leads, make reports, create visualizations, and do business more effectively. Availability is the principle concern at this stage of the lifecycle. Planning your workday around a single piece of data only to lose access to it through some fluke will cost you time and money.

Data Cleaning

Data cleaning is how you keep things organized. There are two main cleaning processes: archiving and deleting. Data gets archived when it’s no longer relevant to day-to-day operations. It’ll still be around but kept out of the way so that more relevant pieces of data can come to the fore. It’s best to archive data when you don’t have an imminent need for it but still want access to it for reference or reports. Archived data will usually live on a different medium from active data.
Data gets deleted when it’s no longer relevant. This is the end of the lifecycle. Truly deleted data is irretrievable and cannot be consulted for analysis or reports. Some businesses never delete anything, but businesses that gather large amounts of data need to delete at least some things in order to save on storage costs. Only delete something if you are entirely certain you will never need it again, and make sure to follow any applicable data privacy regulations when deleting data. Don’t delete anything you’re legally required to keep.

Benefits of Data Lifecycle Management

The core benefits of DLM are efficiency, compliance, security, and value. A good DLM platform will provide all four. Here’s a breakdown of what to look for in each category.


The most obvious benefit of any type of automation is efficiency. Why waste labor hours on a process that good software can carry out automatically? The several levels of data automation at play in DLM will save countless hours and headaches by swiftly managing every piece of your data.


Complying with data management rules and ensuring that you archive (not delete) any information you may need down the line can get tricky to manage manually. Everyone on your team must stay up to date on what should be archived, and this can lead to miscommunication as strategies and regulations change with time. Your business may fall out of compliance as a result. Automating data compliance with DLM means you only need to communicate changes in protocol once (to the software) rather than repeatedly (to your team).


Your users trust you with your (and their) data. Any security breach violates that trust and could damage your reputation. You are staking your entire business on your security solution’s effectiveness. Automating data management means granting access to data to fewer users, which helps to minimize the number of entry points to your database. Fewer points of entry means lower risk of a security breach.


Not only can DLM save you money, it can also make you money. Large volumes of data, when managed by hand, often contain important information that gets overlooked. If you can’t leverage the data you collect to its greatest potential, why collect it all? With DLM, you can set up custom filters that will find and highlight important or anomalous pieces of data. You can then take that information and use it to the fullest for the good of your business.

Learn More with Aktify

If you’re curious about DLM and other automated tools that help optimize your business operations, look into Aktify.
Aktify uses robust data science and machine learning to foster strong relationships with customers. Aktify’s solutions are invisible, seamlessly integrating with a wide variety of CRMs, DLMs, and other marketing automation platforms. 
No matter your industry, Aktify can help streamline your customer relations and improve efficiency at every stage of your sales funnel. Reach out to Aktify today.

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