What Is Data Life Cycle Management, And Why Does It Matter?

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.

Data Life Cycle Definition

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.

The Data Life Cycle: An Overview

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.

Goals of DLM

“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.

The Seven Data Life Cycles in DLM

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:

1. Collection

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.

2. Input

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.

3. Processing

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.

4. Output

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.

5. Storage

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.

6. Dissemination

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.

7. Deletion

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.

To read the full article and learn more about the data life cycle, visit KemiNelson.com!