Data life cycle management helps companies maximize value and minimize risk. A DLM program should target specific organizational needs and integrate with existing processes. Learn why data life cycle management is essential to ensure confidentiality, accuracy, and relevance.
Data life cycle management (DLM) plays an integral role in modern business. It’s also crucial for startups and entrepreneurs seeking a competitive edge. This discipline helps reveal hidden patterns and emerging trends to foster better decision-making. Use it to gain relevant insights, test theories, or track progress.
What is data life cycle management, and why are companies talking about it? How can your business benefit from DLM? Talk to a data scientist for specific details, and then read this blog. In it, we’ll discuss the data life cycle definition, briefly examine the seven phases, and explore ways to maximize your efforts. Let’s get started.
Table of Contents
The data life cycle is relatively straightforward and involves multiple steps to collect, clean, process, and archive data. This sequence of stages describes the various actions taken on data units from beginning to end. The required techniques can vary between teams depending on multiple factors, including confidentiality, timeline, and purpose.
Data life cycle management, or DLM, is the process that oversees each phase. It helps direct data collection, data analysis, and data dissemination. The necessary tasks can involve identifying sources, classifying sets, and managing archives for later retrieval.
DLM can also require establishing database retention policies and frequently reviewing or updating information. A data scientist’s work can help companies implement new security measures with solid proof backed by relatable metrics. It is the lifeblood of thriving businesses.
“The primary goal of this cycle is to ensure compliance.”
However, it provides multiple benefits to the companies that use it. For instance, DLM also focuses on data accuracy. This advantage can help reshape marketing campaigns, realign teams, foster public interest, and support business transitions.
Data life cycle management means understanding the value and potential of data throughout the process. Additionally, team leaders must know how to communicate findings and theories creatively. Many use data visualization to help display concepts, share evidence, and demonstrate ideas. Unfortunately, visualizing data is virtually impossible without an organized DLM approach.
Part of organizing data is establishing a standard life cycle. This helps teams track which data has already been used and separate it from new or unused data. The process also gives meaning and structure to otherwise chaotic input. In many cases, data scientists follow the same steps for each project. Still, teams can dictate what data to collect, how to use it, and when to destroy it.
NOTE: Many industries have data retention requirements or deletion stipulations.
“At the most fundamental level, data life cycle management aims to protect businesses from liability.”
DLM gives weight to marketing teams and relieves stress from executives. Data life cycle management helps companies meet or exceed industry expectations while protecting business assets. Essentially, it eliminates avoidable risks and points toward feasibility instead.
Data science is gaining traction in today’s competitive environment because it provides practical feedback from typical and unlikely sources. Meanwhile, data life cycle management helps confirm data values to enhance that feedback and, from it, offer actionable outcomes. DLM is the key to keeping up with the Joneses without losing sight of what’s right.
A data scientist will help ensure teams meet specific goals using the structured seven-step process described below:
Data collection is the first step in DLM. It involves a customized approach designed to accumulate and measure as much information as possible. DLM teams can specify the variables and direct the objectives to achieve team goals. This systematic strategy supports innovative fields like science, business, and manufacturing because it draws from multiple sources to answer crucial questions.
Use the data collection step to formulate theories, make predictions, and compare concepts. Avoid misusing resources on distorted findings or compromising your company’s integrity—anchor DLM with comprehensive gathering.
DLM really starts during the data input phase. This is when an experienced data scientist provides information and metrics to support the next steps. The process offers teams and software the necessary facts for later consideration. It usually involves digital databases designed to help further define the project goals.
Data input is crucial to data life cycle management because it also initiates the hunt for missing information. Some teams even introduce machine learning tools and artificial intelligence to help streamline and prevent mishaps. Regardless of the techniques, this phase includes everything from standard data entry to fast cloud-based downloads.
This data life cycle management step is just as critical as the first two. The reason is that it helps teams convert raw data into machine-readable formats. Following a strict regimen is essential because sloppy handling during processing stages can compromise data values or threaten regulatory compliance.
“How teams use the data after processing is where the customization starts and careful management becomes crucial.”
Data processing is about filtering the data collected in step one. Experts can also perform a preliminary analysis of it to confirm its usefulness. Many use this phase to collate data into various categories for later examination, evaluation, and implementation.
This is where a data scientist can create a quantitative summary of various DLM activities, actions, or applications. It is what the team or software spits out after data collection, input, and processing. This data life cycle management phase also helps anchor research, inspire theories, and initiate insightful collaboration.
Think videos, audio recordings, digital communications, textual records, and printed documents when defining data output. Use it to develop testing criteria or manipulate it to translate a specific message. Present compelling data through multiple mediums, and in the process, keep it organized and simplified for optimal comprehension.
Data storage means keeping records of data collections, times, techniques, sources, and outcomes. It creates a trail for teams to follow when innovating or developing theories. Ensure easy access to the annuls and maintain a sustainable campaign. Depending on accuracy and security concerns, use cloud storage, a USB flash drive, or manually written files. Then use diligent data life cycle management skills to keep things running smoothly.
Data life cycle management wouldn’t be complete without dissemination. You’re not collecting information to fill up storehouses that will sit untouched. DLM has a specific purpose, including sharing relevant data with others. This can involve social media posting, in-person interactions, or private presentations to educate and motivate audiences.
The data dissemination phase is exceptionally touchy because of fair use laws, rules against misinformation, and reputation concerns. Talk to a data scientist for help in making announcements with integrity.
Deleting data might seem counterproductive, but it’s mandatory in some situations. Also, data deletion helps teams with quality control and provides an archive for later use. Establish long-term storage, make room for more input, eliminate obsolete information, and inspire more innovative problem-solving.
“Establish a data life cycle team to protect yourself from start to finish.”
If you’re going to use data, you must collect it. But gathering it requires standardized ethics, and each subsequent DLM phase is equally delicate.
DLM programs are tailorable to your project. However, teams must follow all seven data life cycle management steps to avoid misunderstandings, mishaps, and mistakes. Implementing a DLM program also has unique stages to ensure maximum efficacy. This complex approach usually requires help from a data scientist or data analyst with experience integrating revolutionary concepts.
DLM involves seven additional phases, so take notice. Here is a brief description of each:
Use phase one of DLM to recognize data requiring timely collection, processing, and analysis. This information comes from all the sources your company draws data from. Also, classify data based on importance, relevance, and sensitivity. You can create categories and label them confidential, personal, or public.
Establishing a workable retention policy is essential to data life cycle management. It helps create parameters for metrics, categorizes information, and ensures regulatory compliance. This phase also determines how long teams keep data and when they’ll remove or destroy it. A data scientist will base retention policies on legal and business requirements.
“Teams can build custom controls, use encryption, and store data on cloud-based platforms for safekeeping.”
Data security anchors all data life cycle management programs. This critical phase describes the measures taken to restrict access, protect assets, and provide backups.
Develop a plan for archiving data when you’re done using it. Delete unwanted or unusable stacks to create space for new information and support innovation. Categorize data as active but unable to be deleted because of legal or regulatory barriers. A data scientist can help you determine what those are.
Data monitoring describes a crucial phase in data life cycle management. It’s part of a regulatory review routine that helps teams use and store data according to expectations. This integral step also aligns compliance concerns with each DLM phase to protect project integrity and shield stakeholder interests.
The continual improvement process of data life cycle management involves frequently evaluating and re-evaluating data. It could also include reviewing DLM efficiency or inefficiency. The primary purpose of this phase is to meet organizational needs and adjust the projected trajectory as needed.
DLM’s final phase features rigorous compliance checks to avoid costly consequences. It acts as the overseer of all other data life cycle management stages. This is where a data scientist can ensure regulatory compliance despite software glitches or otherwise.
All data lifecycle management approaches should be compatible with organizational needs, company policies, and local or federal laws. They should also integrate effortlessly with existing IT and business processes. Talk to a data scientist for more information.
Examples of data life cycle management are everywhere. Consider a sales email campaign. Suppose you want to send it to subscribers but are unsure how to avoid the spam folder. DLM gives helpful insights to improve marketing strategies and prevent reputation damage.
Another DLM example is when companies launch targeted outreach programs. Their ability to attract, educate, and convert consumers or investors depends on quality data collection and analysis. The data dissemination phase is also crucial, so develop and follow a structured DLM for the best results.
Here are proven strategies to help you get the most out of data life cycle management programs:
- Define your processes. In other words, clearly define your project’s goals and decide how to use the data collected.
- Pick efficient data processing tools. Manage big data and massive volumes without getting overwhelmed. Also, use software to help you organize and secure intellectual property.
- Collaborate through each step. Communicate with your team to develop a tailored DLM approach. Then talk to a data scientist for more tips.
Data life cycle management defines the processes required to manage data from start to finish. Each phase plays an essential role by providing crucial feedback and helping teams protect their assets. Use DLM to foster more collaboration, cooperation, and relevance.
Maximize the value of your data with DLM strategies. Meanwhile, minimize the risks and target specific organizational needs while integrating existing processes. Then reach out to a data scientist to help structure an impactful DLM.