What are the Data Lifecycle Management Tools and Framework

The IoT Academy
4 min readSep 12



No matter how big or small the company is, data lifecycle management is an essential process. Additionally, any organization can handle the data safely and effectively at each stage. They can monitor data from the point of creation to the point of deletion with the right data lifecycle management tools.

Data Lifecycle Management (DLM) works by combining a business and technical approach. Thus, it enhances database development (or acquisition), delivery, and management. Read the post below regarding DLM tools and frameworks.

What is Data Life Cycle? — Introduction

The overall time that data is present in a system is the data life cycle. You can also call it the information life cycle. It includes each step in the data experience, from the initial data acquisition to the next stages.

The data lifecycle includes data collection, processing, analysis, sharing, and deletion. The data lifecycle management framework also revolves around these stages.

Managing the Lifecycle of Data

The goal of data lifecycle management tools is to assist companies in providing end users with the data they require. So, Data lifecycle management must be open to achieve this.

Moreover, documenting the life cycle of a process makes tracking for routine data operations easier. Additionally, it makes it simple to identify and fix failures and bottlenecks.

Furthermore, the data lifecycle framework serves three main purposes. They are Confidentiality, Integrity, and Availability. Therefore, you can interact more effectively with those who work directly on your organization's data team. Additionally, it offers you insights that inspire ideas for potential enterprises or initiatives.

What are Data Lifecycle Management Tools?

Tools for data management are crucial for development, monitoring, data organization, processing, and analysis. Similarly, DLM tools should be highly efficient and effective because they can organize and harmonies data.

Furthermore, these tools also assist in data security, privacy, and data redundancy. Therefore, a combination of software tools and best practices is crucial to efficiently manage and arrange data resources.

List of Best Data Management Tools

Some of the data lifecycle management tools you can use are:

1. Data integration and ETL tools

It is the Extracting, transforming, and loading (ETL) of data. It It describes the process of copying data from many sources into a system that represents the data.

Moreover, the process of combining data from different sources into a single destination is the data integration.

2. Cloud data management tools

Now storage and bandwidth have grown more affordable. Thus, the number of off-premise choices for data warehousing and management has also increased. Businesses with large data to store, analyze, and sort use cloud-based solutions to boost productivity. It is possible by the growth of cloud data management tools during the last five to ten years. However, many smaller companies are also offering consumers a variety of data demand tools.

3. Master data management tools

You can merge all the enterprise’s business applications from several departments into one file using master data management technologies. These master data management systems also help you create a single point of contact for your business.

Data Lifecycle Management Framework

Every business interprets and classifies data, depending on the business model. Hence, it also needs some software tools, and individual data management strategies. Below are the common stages of the DLM framework:

1. Data collection

Companies gather information in many ways either by creating data through data entry or obtaining pre-existing data from other sources. This phase also explains when data values enter your system’s firewalls.

2. Data processing

It involves cleaning and processing raw data for further analysis. Data preparation includes combining data from several sources, validating the data, and executing the transformation. It may also include the reformatting, summarizing, subsetting, standardizing, and enhancing of data.

3. Data analysis

Regardless of how you examine and interpret your data, this is the crucial step. Analysis is necessary for exploring and analyzing your data. It also involves visualization and statistical analysis. Moreover, you can use artificial intelligence (AI) or conventional data modelling.

4. Data sharing or publication

At this stage, predictions and insights transform into choices and directions. Any data offers its full commercial value if you share the information with the relevant team.

5. Archiving

In general, you can save data for later use after gathering, handling, analyzing, and sharing. Thus, it is crucial to preserve metadata about each item in your records. This will help you to retain any value in the future.


Using the effective data lifecycle management tools, you can consolidate all the business software. You can combine all the information of the company’s many departments into a single file. The DLM concept is growing with time by data, IT experts, and by theorizing best practices.



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