Data Lifecycle Management: Navigating the Journey from Creation to Archiving

Your team will gather data from public resources or conduct surveys for first-hand feedback collection. Later, you want to sort the acquired data to identify trends, problems, and opportunities. However, mixed data objects must comply with a consistent formatting requirement to avoid inaccurate processing. So, you transform data, analyze, and report the findings. This post will describe and explain data lifecycle management, revealing the journey of datasets from creation to archiving or deletion.

What is Data Lifecycle Management?

Data lifecycle management (DLM) involves planning, quality enrichment, monitoring, and troubleshooting data operations. Enterprise data solutions and services

• include DLM features for secure storage, continuous processing, and efficient retrieval of business intelligence.

Each data source will offer you multimedia objects and textual content. If the data volume is small, manual inspection is feasible. Otherwise, companies must avoid manually sorting raw data, especially if the data volume is in the order of petabytes.

Data lifecycle managers and strategies develop multi-stage extract-transform-load (ETL) pipelines, converting raw and unstructured data assets to acquire holistic intelligence. They also decide what happens to data objects after they lose relevance or become obsolete.

Data Lifecycle Management: Stages from Creation to Archiving and Deletion

1| Data Creation or Acquisition

External platforms, like social networks, research journals, news portals, or government websites, can generate extensive data. Therefore, businesses employ data mining and web scraping methods to gather required data assets from those sources.

Data creation can occur in-house after ideas move between multiple stakeholders. For instance, employees and automated tools create new datasets in a corporate setting by providing suggestions, modifying records, conducting experiments, or rectifying errors.

This phase is the data creation stage, when data becomes available to the business for the first time. Irrespective of external or internal source type, the data will lack formatting consistency, requiring additional processing for quality improvement. Therefore, data lifecycle management solutions include dataset cleansing and validation features.

2| Secure Storage

Datasets can undergo corruption, or cybercriminals might threaten data integrity through ransomware attacks. So, storing and protecting data requires a reliable data management ecosystem.

Meanwhile, backup creation demands robust hardware. Thankfully, DLM specialists offer advanced encryption, cloud-based virtualization, and antimalware integrations to protect clients’ intelligence assets.

3| Processing and Usage

Stored data must serve a purpose, and every stakeholder must understand it. Otherwise, irrelevant data will hinder analytics efficiency by wasting data storage and bandwidth.

Goal-oriented data usage often involves extensive editing and analyses to deliver requested insights. You can leverage analytics tools to identify dataset patterns and extract practical insights for strategy optimization.

However, the company stakeholders must be careful when utilizing the firm’s personal or confidential data. Responsible data lifecycle management ensures your team complies with the privacy regulations and governance guidelines.

4| Sharing and Collaboration

Decades ago, a dataset was relevant to a particular department when businesses had not embraced a multidisciplinary team culture. Today, multiple departments require identical databases irrespective of distinct reporting needs.

So, ease of sharing and customizing is crucial across modern workflows. Leaders also want to monitor how data sharing happens, how it affects resource utilization, and whether new technologies are necessary to boost organizational productivity.

Across data lifecycle management goals, sharing enterprise intelligence assets through end-to-end encrypted communication channels has become mandatory due to governance considerations.

Therefore, companies can improve data integrity by restricting unrelated departments or less knowledgeable employees from accessing specific datasets. DLM systems might offer native employee authentication tools or ask users to utilize third-party technologies, like mobile-friendly authicator programs or physical security keys.

5| Archiving

Data assets do not stay relevant, given how fast the competitive markets evolve. What used to work a few years ago might become obsolete due to technological disruption or the ever-fluctuating preferences of today's young consumers.

Corporations require appropriate policies to determine the maximum duration to preserve historical intelligence assets. Simultaneously, brands must ensure their data lifecycle management respects a region's legal frameworks dictating PII-related data retention.

Consider eliminating trivial data points as soon as possible. Instead, focus on archiving critical records like financial transactions and supply chain intelligence that stay useful for decades.

6| Deletion

Principles of data lifecycle management also encompass data deletion or erasure procedures. However, stakeholders must confirm that a dataset has lost significance. Otherwise, you might lose potential business improvement ideas by premature erasure of databases due to perceived superannuation.

Leaders must specify how to check whether preserving old datasets has become a liability. Additionally, consult departments before deleting those datasets. Remember, what might be less valuable to one team might be indispensable to another business unit or field workers. 

Conclusion

Technologies that DLM professionals employ address data security, quality, and processing issues to streamline enterprise workflows. They have become popular with the privacy-first processing trend and new developments in the governance compliance space.

All the data lifecycle management stages, from creation to archiving, ensure companies utilize their IT ecosystem for business-relevant insight exploration. They help realize confidentiality, integrity, and availability (CIA) goals, empowering global brands to get detailed reports while respecting stakeholders’ privacy rights.

I am working as a digital marketing analyst at SG Analytics which is a global data analytics company that provides research and analytics services globally.