What is the Data Life Cycle? 5 Main Phases Explained

What is the Data Life Cycle?

The data life cycle is a conceptual model that details every single phase data needs to go through during its lifetime. These phases normally consist of data collection, management, usage, sharing, and deletion.

Why Do You Need to Understand the Data Life Cycle?

Understanding the data life cycle is a foundation for exploiting the full potential of your data. Accordingly, it gives your business a range of transformative benefits as follows:

1. Effective Data Management

Knowing the data’s journey helps you optimize data management processes that include data collection, storage, and retrieval. This helps minimize storage costs and enhance operational efficiency by avoiding wasting resources on outdated or redundant data. For example, archiving unused data frees up computational capacity and storage for high-priority tasks.

2. Adherence to Regulations

Once you have a clear understanding of the data life cycle, complying with data regulations and standards will become easier. In other words, you can proactively spot compliance risks at each phase of your data journey. For instance, you can identify how long data should be kept or when it can be securely deleted to abide by specific regulations.

3. Ensure Data Quality and Integrity

By understanding the life cycle, you can determine where errors in data (e.g., inconsistencies) occur and take immediate action to stop them. This ensures data is error-free and consistent across different applications and systems. Further, proper data cleaning and validation at each phase also ensure data accuracy.

4. Timely Decision-Making

The total volume of data created is booming, with an estimated 394 zettabytes in 2028. Yet, many companies are struggling to derive actionable insights from such data. So, with knowledge about how data works, you can filter out the most relevant and crucial data for analytics. This enables you to generate meaningful insights from data for timely and informed decisions.

5. Innovation and Competitive Advantage

Understanding the life cycle enables your business to uncover the full potential of data. This drives data-driven innovation and insights, hence giving you a competitive edge over competitors.

Pre-Phase of the Data Life Cycle: Data Planning

Before digging into the data life cycle, it’s essential to plan how data will be collected, managed, used, and shared throughout its lifespan. This plan must answer the following questions:

What data should you collect and in which formats?
Which supporting documents are necessary to explain the data, and where to keep them?
Can your data storage systems process the amount of data and safeguard it from threats?
Who is in charge of the quality and integrity of your data?
How can you track who modified the data and when?
Who will decide when and how to share the data? Which rules or policies will govern its sharing?
How and how long will the data be kept over time?

This pre-phase is crucial as it aligns the data life cycle with your business goals, technical requirements, and compliance regulations. Without a thorough plan, you can fail to acquire relevant, high-quality data, coupled with inefficient data management and misleading decision-making.

5 Key Stages of the Data Life Cycle

Once you’ve prepared a rigorous plan, it’s time to go deeper into each stage of the data life cycle. According to the University of Wisconsin Data Governance Program, the cycle consists of the following phases:

Phase 1: Data Collection or Creation

The first stage of the data life cycle is generating or amassing data from different sources. These sources often include internal systems (e.g., CRM software, point-of-sale systems, or web analytics tools) and external sources (e.g., social media platforms or government agencies).

Data Collection Techniques: In this phase, you can leverage different techniques to collect data. The two most popular options are quantitative and qualitative.

Quantitative data collection refers to amassing structured data that is numerical and measurable. Some common quantitative methods include multiple-choice questionnaires, well-structured interviews, observable events, and experiments. These methods are suited for gaining widely applicable, precise insights.

Qualitative data collection refers to collecting unstructured data that is descriptive and contextual. Some common qualitative methods include open-ended surveys, pilot studies, in-depth interviews, and document reviews. Their findings are not quite generalizable. Yet, they offer context-specific insights that explain underlying causes and behaviors.

For example, a retail company gathers customer purchase data through their online stores. Each transaction provides such data points as customer ID, product ID, amount spent, or date of purchase.

Phase 2: Data Storage

Once collected or acquired, data must be stored in a stable and secure environment for easy access and management. Also in this phase, you need to constantly manage hardware components, control access through passwords, as well as offer robust backup and recovery services to prevent irreversible data loss or removal.

To choose the best storage options, you should consider various factors, like data sensitivity, volume, access needs, and compliance with data regulations.

Below are several storage solutions to consider:

Table for Data Storage Solutions

Storage Solutions	Definition	Examples	Use Case
Local Storage	Physical devices that are close to where you gather or use data.	– Hard drives – USD drives – Local servers in a company’s data center	Ideal for: – frequently used data – data that must be kept in a certain geographic location due to data privacy regulations
Cloud Storage	Remote servers which are accessible via the Internet and often hosted by third-party providers.	– AWS S3 – Microsoft Azure	Ideal for: – companies managing vast datasets – scalability and remote access
Databases	Structured or unstructured formats that store and organize data for effective retrieval and management.	– Relational databases (e.g., MySQL or PostgreSQL) – NoSQL databases (e.g., Cassandra or MongoDB)	Ideal for: – web apps – business apps – systems that need to access, extract, and use data repeatedly, often in real-time (e.g., CRM platforms or eCommerce platforms)
Data Lakes	Centralized systems that store large volumes of raw, unprocessed data in its native format	– Cloud-based lakes like Azure Data Lake or AWS Lake Formation – HDFS (Hadoop Distributed File System)	Ideal for: – companies that handle big data, use machine learning, and implement advanced analytics
Data Warehouses	Centralized systems that process and analyze structured and semi-structured data	– Amazon Redshift – Google BigQuery – Snowflake	Ideal for: – business intelligence and reporting systems that focus on historical data analytics
Offline Storage & Archives	Long-term solutions for data that is no longer actively used but still kept for different reasons (e.g., legal compliance or future reference)	– Physical devices without internet access (e.g., blu-ray discs or tape drives) – Cloud-based cold storage (e.g., AWS Glacier)	Ideal for: – retaining historical data (e.g., archived emails or old contracts) – adherence to retention regulations (e.g., HIPAA or Sarbanes-Oxley Act)

Phase 3: Data Usage or Processing

This stage involves organizing, converting, analyzing, and explaining data to deliver meaningful insights. Let’s take a look:

Data Organization

This refers to developing a structured framework to help others understand what your data means and find it easily. To organize your data for effective management, you should:

Give your files clear and consistent names. For instance, you can name a file “sales_data_20241209_v2” instead of “data_december_2024”. This not only allows for easy access and retrieval, but also helps others understand the purpose of your data, even if they visit it years later.

Don’t throw all files in only one folder. Instead, you should organize data types in different folders and use around 2-3 folders to classify your data further.

Data Conversion

This refers to the following tasks:

Clean data by eliminating errors, duplicates, and inconsistencies from your data to enhance its quality and precision.

Use processing techniques such as aggregation, filtering, and sorting to derive valuable insights from your data.

Integrate data from different sources to get a comprehensive view of your data.

Data Analysis

This refers to leveraging statistical techniques and analytics tools to discover patterns, trends, and correlations in your data. The choice of techniques or software depends greatly on your business goals, complexity levels of data, and more.

For example, when Designveloper’s team worked on an eCommerce website project for a retail client, we employed Descriptive Analysis and Superset for data analytics.

Descriptive analysis helped us focus on describing and summarizing data characteristics and changes, without giving any conclusion. For instance, we identified portable fans as the top-selling product on the website, accounting for 17% of the total sales.

Meanwhile, Superset aided in filtering specific criteria, like search results, search frequency, or clicks on a single filter option. Suppose only 1% of users searched and used a filter. This could translate to a very low rate, requiring our team to adjust filters and the search bar to better meet user demands. In case we could increase this figure to 50%, this translates to a good sign.

Data Interpretation

Next, you’ll generate visual representations (e.g., tables or graphs) to convey insights effectively to stakeholders (e.g., the marketing team or investors).

In this phase, no matter what purpose your data usage aims at, consider how it can affect relevant people and communities. This enables you to use the data ethically and responsibly, plus in compliance with all laws to avoid misuse and build trust.

This stage explains how data is shared or made accessible to authorized users.

You can share data in different ways, including transmission via links, emails, or cloud systems. Regardless of options, data distribution requires security best practices like passwords or encryption to safeguard data against cyber threats.

Further, this phase aims to allow stakeholders or systems to reuse data, validate findings, and encourage transparency across processes. In other words, sharing creates a feedback loop where the distributed data can offer new insights. As a result, the data can then be reincorporated into the data life cycle for further use or management.

Reusing or repurposing keeps the data preserved and accessible for a long time. Otherwise, data that is no longer necessary or relevant might go directly to disposal.

Phase 5: Data Deletion or Destruction

Now, you’ve come to the final stage of the data life cycle. That’s destroying or deleting data that is outdated or no longer required and offers no added value to analysis or company knowledge. Besides, when data reaches the end of its retention period, it must be removed from storage systems.

Data disposal is crucial as it helps your business adhere to regulations, reduce storage costs, and avoid privacy risks. This enables more effective data management.

Here are several considerations you should think of when disposing of data:

Ensure data deletion meets relevant regulations and laws required by your industry and region.
Document which data is being removed, when, and why to ensure transparency.
Verify that your data is no longer preserved in backups.
Consult relevant departments (e.g., IT, compliance, or management) before continuing with deletion to prevent unexpected impacts.

Why is the Data Life Cycle Not Always the Same?

Normally, the data life cycle ranges from initially collecting or generating data to eventually removing it when no longer necessary. Yet, remember that not all data follows this linear path. Here are several reasons why:

Some data can never reach specific phases. For instance, some user behavioral data like extensive logs of button clicks or micro-interactions on an app can be amassed but never analyzed. Such data is considered insignificant for decision-making (except for the case of UX improvements), so it’s never reviewed.

The order of phases can vary. Data can go through analysis before being fully cleaned or processed in some cases. One typical example is an Exploratory Data Analysis (EDA) which analysts often use to identify problems (e.g., missing values, inconsistent formatting, or outliers) before adopting a thorough cleaning process.

Data can be used for different purposes. Depending on the final goals, each organization has a different approach to data management. Therefore, they need to modify the data life cycle by adding crucial steps or removing unnecessary ones. Take this example: the US Fish and Wildlife Services wants to focus on the quality assurance (QA) and quality control (WC) of data management. So, its data life cycle covers the following components:

Data Management Life Cycle by the US Fish and Wildlife Services

Be flexible with your data life cycle

A data life cycle provides a structured framework to manage your data effectively, from collection to destruction. However, real-world data management scenarios are often complex and can require a more flexible approach. So, you don’t have to follow all the phases rigidly. Instead, you should:

Adapt your data management processes to specific needs.
Focus on the most crucial phases for the data and avoid unnecessary work.
Reuse or repurpose data that might be eliminated as you consider it inessential.

Final Words

Today’s article has guided you through a journey to discover the data life cycle, from its definition and importance to its main stages. Depending on your goals and data complexity, you can modify the cycle to best fit your data management strategies. If you’re interested in data-related topics, subscribe to our blog today to receive the latest updates soon!