Data creation is exponential. Over 147 ZB of data currently exists compared to only half that amount in 2020. Clearly businesses need to understand the data lifecycle so they can efficiently store and manage large volumes from creation to disposal.
The data lifecycle is the sequence of phases that data goes through. Throughout this process, you'll need to see to it that data is efficiently managed, protected, and used while maintaining compliance standards and working toward your organization's goals. Key phases include data creation, storage, processing, analysis, visualization, and archival and retention. Each plays a key role in data quality, accessibility, and security.
This post discusses the phases, challenges, and best practices in managing the data lifecycle.
Phase 1: Data Creation and Collection
The first phase of the data lifecycle is creating and collecting data. It involves generating and taking the initial data, capturing it from various sources, and bringing it into the organization. Data sources include APIs, databases, IoT devices, social media feeds, customer interactions, business and financial transactions, and manual entries. To maintain data quality while creating and collecting it, use the correct methods and tools, and recruit the right personnel.
Phase 2: Data Storage
After data has been created and collected, it needs to be stored. This phase enables quick access for current and future use while maintaining security and integrity. Tools and technologies used to store the data include the following:
- Cloud storage, such as Google Cloud Storage and Microsoft Azure.
- On-premises storage options such as hard drives, though these lack scalability and accessibility and are at greater risk of data loss.
- Databases or datasets that will eventually be uploaded to cloud storage.
Efficient storage protects sensitive information against unauthorized access, breaches, and misuse.
Phase 3: Data Processing and Organization
Data processing and organization helps prepare data for analysis.
- Data cleaning and transformation: Cleaning data is the identification and correction of errors and missing values. Data transformation is the conversion of raw or unstructured data into a suitable format or structure to meet specific analytical needs.
- Data integration and management: Data integration means combining data from disparate sources into a cohesive dataset to make it easier to analyze. Data management means collecting, organizing, and leveraging data so it can be used to make business decisions.
You can leverage data processing frameworks like ETL for grouping, sorting, and transforming unstructured data. Tools like Kafka Streams, Snowflake, and Apache Spark can also be helpful.
Phase 4: Data Analysis
Data analysis is the process of analyzing data to get valuable insights. This phase involves using analytical tools and techniques to identify patterns, trends, and correlations. Some of the more commonly used methods include statistical modeling, algorithms, artificial intelligence, and machine learning.
Analysts and data scientists incorporate various tools like Tableau and Power BI to enhance the process. Meaningful insights gathered during this phase enable businesses to make more informed decisions. For instance, using Natural Language Processing (NLP) to analyze social media sentiment and identify common themes in customer feedback allows organizations to create more targeted marketing campaigns.
Phase 5: Data Visualization and Reporting
Data visualization is the use of graphical elements to represent insights generated in the analysis phase, making patterns and trends more discernible. Conversely, reporting relies on textual or tabular formats to provide comprehensive information.
Various visualization techniques include the following:
- Charts and graphs
- Interactive and real-time dashboards
- Geospatial maps (e.g., heat maps)
To create effective reports, focus on the key metrics that matter most to your business, define your goals and objectives by identifying and determining the audience of your reports, establish the KPIs to measure success, and choose the right data visualizations by considering the type of data you have. Consider using visualization tools such as Power BI and Tableau.
Phase 6: Data Maintenance and Updating
Data maintenance involves cleansing data you've already captured or updating it to reflect changes in the real world. Simply put, it's about making sure data remains accurate, relevant, and current.
Carry out regular updates and data refreshes to reflect the latest changes in the business domain. It's particularly crucial in fast-paced sectors where decisions rely on the latest available data, such as in finance, healthcare, and e-commerce.
Working with large amounts of data can increase the potential for redundancy and duplication, which can harm data quality and needlessly fill up storage space. Data maintenance and updating removes duplicate entries to avoid confusion and errors and corrects inaccuracies.
Best practices for data maintenance include defining data standards, validating data at entry, and ensuring security through encryption, role-based access control, and multifactor authentication.
Phase 7: Data Archival and Retention
Data archival focuses on securely storing data that has permanent or long-term value. Meanwhile, data retention is retaining data for a set time to comply with legal, administrative, operational, and business requirements.
Data archival is important as it retains data useful for record-keeping, compliance, historical purposes, and future references. To implement data retention, consider the following strategies:
- Create retention policies and delete data promptly when the time comes.
- Adhere to all regulatory and organizational requirements.
- Communicate regularly and openly.
Choose archival storage solutions that provide secure, long-term storage, such as tape, and keep one backup copy in immutable storage. Additionally, you can make your data easily accessible for audits, legal inquiries, and historical analysis by maintaining a catalog of archived data, documenting metadata, and implementing search functionality for efficient retrieval. Some top tools include Google Vault and Bloomberg Vault.
Phase 8: Data Disposal and Destruction
The final phase of the life cycle is deleting unnecessary data. You must delete it in compliance with legal and regulatory requirements and data retention regulations. Before destroying data, the data retention policy period must be over.
To securely destroy data, you can physically destroy hardware containing sensitive data, use software tools for digital shredding, or use secure deletion protocols that overwrite data. Additionally, you must thoroughly document these processes to mitigate unauthorized access or recovery risks.
Challenges in the Data Lifecycle
Managing the data lifecycle comes with some challenges.
- Managing large volumes of data: Handling large volumes of data presents several challenges, such as storage and scalability. Consider investing in scalable storage that can handle growth without compromising performance such as Google Cloud Storage.
- Ensuring data quality and consistency: Data accuracy and consistency are critical since maintaining them prevents poor decisions and wasted resources. Controls must be enforced to ensure data remains reliable.
- Adapting to changing technologies: Outdated technology may lack the capabilities needed to support modern data lifecycle management practices. Organizations should gradually modernize legacy systems by adopting cloud-based solutions and leveraging APIs for seamless integration.
Best Practices in Data Lifecycle Management
Follow best practices to maximize the benefits.
- Implement a strong data governance framework. This is the practice of knowing where your data is, how it's being used, and whether or not it's adequately protected. Good data governance means data has integrity, is consistent, and is not being misused or mishandled.
- Leverage automation and AI. Automating the various stages of the data lifecycle can boost efficiency, reduce errors, and improve overall productivity.
- Foster a data-driven culture. Create an environment where data is readily accessible and used consistently to drive decision-making processes.
Acceldata's Approach to Data Lifecycle Management
Acceldata provides organizations with the visibility into their data lifecycle. With Acceldata Torch, you can get a single, unified view of your data and data lifecycle. Furthermore, Acceldata helps organizations monitor, investigate, and manage the reliability of their data pipelines and infrastructure using the cloud and AI-powered tools. By leveraging Acceldata, you ensure data quality, optimize performance, control costs, and prevent failures.
Request a demo or sign up for a free trial to see how AccelData can help meet your data management strategy needs.
This post was written by Kelechi Ugwu. Kelechi is a passionate and versatile Software Engineer and Technical Writer. As a technical writer, he has a love for crafting innovative digital solutions and communicating complex technical concepts in a clear and engaging manner. His writing style emphasizes clarity, consistency, and user-centricity, making it easier for developers and stakeholders to understand and adopt the technology.
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