What is data management?
Data Lifecycle Management (DLM) combines the best practices from the various stages of the data life cycle: production, data cleansing, data management, data protection, and data governance. It defines how the data is captured, prepared, transported, managed, analyzed, and governed at each phase of the data life cycle.
By following DLM, businesses can ensure that the correct data is in the right place at the right time, enabling them to capitalize on data insights and create new opportunities. By leveraging data science, companies are provided with a holistic view of data, making it possible to monitor data usage across the various stages of the customer’s journey and detect any data misuse or breach.
The fundamental requirement for any software platform is data. In today’s complex environment, we may generate data from many sources, such as network operations centers, mobile devices, social media sites, and other data-sharing environments. All of this data has to be managed and used in a manner that keeps the customer safe and adheres to internal and external regulations.
According to the National Institute for Standards and Technology (NIST), data life-cycle management (DLCM) “is the application of a set of principles, processes, and technologies for data management and storage. The objectives of DLCM include the protection, enhancement, and reuse of electronic data within an organization.”
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3 Key Components in any Data Management Lifecycle Process
Each organization has a specific process for creating, managing, and deleting data. We know this as the data lifecycle management process:
The creation of data involves capturing data, defining purpose, classifying data, and removing redundant data.
Management of data is composed of processing, merging, aggregation, classification, and data selection.
In the final stage of the data lifecycle process, data deletion is where the information in the datasets is purged from the system.
What are the stages of the data management lifecycle?
The logical process of data life-cycle management can be divided into six integral stages.
1. Data Creation | Data Collection
The first stage in the data management lifecycle is data collection, sometimes referred to as data creation, and the collection of data that occurs when someone uses a product. Depending on the product, companies may collect data through a channel like an email, a web form, a website, or other means.
At this stage, businesses collect data from the original sources. This data may be passed through software platforms for verification purposes but may also include manual data collection.
2. Data Storage and Maintenance
Once data has been stored in the right form and format, the system must maintain it. In reality, this is an ongoing process, taking into account additions to the database, machine learning, business rules, etc.
To achieve continuous data integrity, businesses need a way to easily migrate data to the cloud or a new environment to be stored. The most common way to store data is by using a Relational Database Management System (RDBMS), the underlying framework for most data warehousing systems. RDBMS enables businesses to manage and maintain data in a trusted manner.
In addition, RDBMS supports almost all programming languages for data manipulation and query functionality. Regularly, the RDBMS performs maintenance on the data in it by retrieving data from data sources and storing and/or deleting data at predetermined intervals.
3. Data Usage
Data collection is a critical step in the data lifecycle. Data usage is also a vital step in the process, with the actual use of the data depending on the application and the business environment it operates in.
Data processing and calculations occur to extract helpful information from the data at this stage. However, all this information is rarely fit for direct use, as data might not apply to the task at hand, or the data may be unstructured.
To maintain data integrity, businesses must track the actual data usage and ensure it adheres to business rules and other standards. We consider data that we can use successfully to be good data, but only if the information surrounding the data is accurate and reliable.
While we cannot alter the data after it’s used, it can be beneficial for:
- Clear and visual reporting.
- Value-added business processes.
- Data analytics and data monetization.
- Business collaboration applications.
- Customer engagement applications.
- Network analysis applications.
- In-database features.
4. Data Sharing
Data sharing is a must in any data management lifecycle, especially as business applications have become increasingly interconnected.
There are four broad types of data that need to be shared:
- Data that needs to be shared among individuals in the same business area.
- Data that needs to be shared among data teams.
- Data that needs to be shared among customers.
- Data that needs to be shared among groups using a shared enterprise resource planning system (ERP).
With many of today’s software systems becoming increasingly interconnected, the above four data types are increasingly prevalent. A perfect example of this is Dialpad, which uses a virtual PBX to provide an integrated communications system with more functionality than your traditional business phone system. (Click here to learn more).
As well as the need to share data among organizations, businesses are increasingly adopting a sharing culture across organizations, both in the private and public sectors. This increases the possibility of data being shared among various departments or teams of employees. The purpose of data sharing is to improve productivity and minimize data maintenance.
This idea of sharing data has grown in parallel with cloud computing.
5. Data Archiving in the Data Management Lifecycle
Data archiving is a fundamental part of the data management lifecycle and is one of the most important data lifecycle activities. In fact, it is the lifecycle activity in which data is often tested, cleaned, and archived.
Data archiving ensures that data is protected, with the information being preserved and available for future access. Data archiving allows business users to reclaim their access to data by having access through the IT department instead of trying to retrieve data from the various servers themselves.
With businesses facing the challenge of extracting more value from their data as they grow, the need to have access to archived data has become more pressing. Data archiving is one of the essential stages in the data management lifecycle, and it is also one that many organizations overlook. This can lead to the loss of valuable information in a data disaster or simply prolong the time to recover from the disaster.
6. Data Deletion | Data Re-use
Data deletion is the final step in the data management lifecycle, where the right to delete data is allocated and enforced. To fulfill its objective, the role of a data deletion committee must be carefully set up.
With data deletion, the right to delete data is defined, and the data manager must ensure that the right to delete data is honored. A deletion process must be implemented, with an effective data deletion policy and an action plan that outlines how to achieve the objective of data deletion.
Beyond data deletion, the elimination of duplicate or redundant data is also critical in the process. Some organizations have an obligation to delete duplicate or redundant data in cases where the value of the data has already been captured. It is vital that companies look into data optimization methods to make their businesses more scalable and efficient while also protecting their users.
For example, a data reuse plan can help drive better business results by identifying the correct data to integrate with the next iteration of the business processes.
Why is data management important?
The right information is the key to successful business practices, which makes data management so important.
When data management is executed right, it can help businesses streamline their processes and improve customer service. Companies can also obtain more insights to analyze performance and better understand their customers.
By taking a data management approach, businesses can collect information quickly and cost-efficiently and store that information in a secure environment. In addition to data lifecycle
management, other automations may provide you with better access to data, for instance, using an order management system e-commerce or a visitor behavior analysis tool.
What are the benefits of a data management lifecycle?
There are numerous advantages to data lifecycle management, such as reducing costs by allowing data to be accessed and utilized more widely. Data-driven businesses also save time as it enables them to eliminate costly and time-consuming data management processes and encourages more collaborative data sharing among employees and users.
1. Compliance with data regulations
Data management lifecycle management is essential for businesses to comply with data regulations. One of the central regulatory bodies is the European Union’s General Data Protection Regulation (GDPR) which replaced the 1995 Data Protection Directive.
The GDPR considers both economic and societal considerations, with the overriding goal of enabling individuals and businesses alike to benefit from digital technologies in a safe and secure environment.
2. Access to the right data
Data management lifecycle management enhances data availability and accessibility to enable employees to make better and quicker decisions, improve customer experiences, and be more agile in the face of change. This is particularly important in terms of Internet of Things app development (IoT) and deployments, as organizations are now looking to gain higher levels of data visibility.
3. Improved operational efficiency
Operational efficiency and agility are vital in today’s business world. Data management lifecycle management can help increase the efficiency and effectiveness of the IT infrastructure and lead to a more streamlined and efficient approach to daily operations.
4. Improved customer experience
Data management lifecycle management provides businesses with the ability to unlock the value of their data to serve their customers better and boost sales.
5. Data Governance
Cyber governance training of IT personnel in data management lifecycle management processes can help business entities reduce the costs of data management and guarantee employees that their data is collected, monitored, and maintained in the correct format.
Best practices for data management
Data management is the process by which businesses allocate resources to identify the methods that will guarantee only the correct data is being captured, stored, used, and managed across the enterprise.
These are some of the best practices for data lifecycle management.
Shared data management
Under this model, business and IT stakeholders agree on a standard data model. This ensures that the correct data is captured at the right time across the company, which helps to improve performance and add to revenue.
Develop a centralized repository for data lifecycle management
This will enable businesses to develop a centralized data management information repository, which contains all the required data and processes, and allows data to be imported easily.
This requires the role of data stewards in the business to control the data and oversee the ongoing processes. This will ensure that the correct data is collected, managed, analyzed, and used in the right way and ensure that the information is never misused.
For IT professionals to become more data-aware, they must embrace the right culture and ways of working. IT professionals need to consider their data as a critical asset and enable this to work to their benefit. This will help improve their ability to analyze data and determine which areas of their systems require the most attention.
This allows the business to gain a more holistic view of the business’s data and, therefore, make more informed decisions.
This allows IT professionals to ensure that they’re using the correct data types for the proper purposes. Data standardization helps drive efficiency and agility within the business and help them make more informed decisions.
Robust data governance
This ensures that all data is secured and organized. It will also help to ensure that systems and processes are in place to help protect this data and help them to comply with regulations, such as GDPR.
Is it time to get started with data lifecycle management?
Data lifecycle management is a vital aspect of every business today.
More and more businesses are realizing how crucial it is to keep up with the ever-changing data management requirements, which can prove to be quite challenging in today’s fast-paced technology world. In fact, one of the main challenges companies face in data management is that their data is often disorganized.
Through data lifecycle management, you can ensure data fragmentation and disorganization never complicate your business needs but instead allow you to control and effectively utilize your data for maximum output.
About the Writer
Tanhaz Kamaly is a Partnership Executive at Dialpad, a modern cloud-hosted business communications platform that turns conversations into the best opportunities, both for businesses and clients. He is well-versed and passionate about helping companies work in constantly evolving contexts, anywhere, anytime. Check out his LinkedIn profile.