Why Data Lifecycle Management Is the Key to Long-Term Growth

Any organization that is focused on growth must also be highly engaged with its data program. Leaders must consider optimization, risk mitigation, storage and compliance, and comprehensive use of data at every stage to generate business value. This data-centric growth strategy does more than give a company and its constituents a sense of purpose; it also supports innovation, profitability, and long-term survival. Without growth, operations stagnate. 

Data lifecycle management is an overarching approach to handling data to ensure its seamless flow and functionality while maintaining confidentiality, integrity, and availability.  

When companies are developing a data lifecycle management strategy to match their growth goals, we see two common oversights: 

1. Enterprises very rarely define their processes. Instead, they piece together solutions and approaches as needed, resulting in ad hoc, amorphous systems that are difficult to learn or optimize.

2. Data lifecycle management is only considered by enterprise-scale businesses handling enormous amounts of data. Yet, this approach to data management is often the springboard for small and mid-size companies on their path to expansion. 

Data lifecycle management is for everyone—and integrating it supports a data culture focused on growth. 

How thoughtful data management supports growth

As data complexity and volume grow, data management can feel like an uphill battle in which businesses must juggle and prioritize inefficiencies, risks, and costs. That can create paralysis even among the most proactive leadership teams. Yet, the possibilities created by scalable, efficient, and secure data practices throughout its lifecycle are worth the effort to create a data lifecycle management strategy. 

Data lifecycle management offers numerous benefits that propel business growth: 

Improved data quality

A data lifecycle management strategy effectively monitors and audits data across storage, usage, archival, and destruction, creating a unified, consistent approach and ensuring quality as it moves across teams and systems. High-quality data increases the accuracy and utility of analytics, resulting in better business decisions. Simultaneously, it saves businesses hours and dollars that would otherwise be allocated to correcting faulty data and related disruptions. 

More utility

When silos are broken down with an integrated approach to management, businesses can gain more insight from data—even what’s been archived. It’s not just active data that holds intelligence; historical and dormant data do too. A comprehensive approach, the corresponding tools, and more robust automations allow businesses to leverage data across its lifecycle for AI training, sales forecasting, user activity, and more. 

Greater automation

The advanced analytics tools and techniques associated with data lifecycle management facilitate machine learning, predictive modeling, and enhanced automation. These capabilities save time and resources while helping organizations continually identify opportunities for innovation, optimization, and growth. 

Reduced costs 

Your IT teams don’t need to dedicate their time to data cleansing, normalization, archiving, and other tedious management tasks. Their time is better spent on high-level tasks that require human intelligence. Putting effort into data lifecycle management frees up valuable resources and reduces operational overhead. 

Five steps to upgrade your strategy

To enable cross-functional collaboration and modernize systems, following these steps to define and implement a data lifecycle management strategy can smooth transformation challenges and offer the support needed to grow efficiently. 

1. Define a governance framework

Does your organization have robust policies and procedures for governing data management? When was the last time they were reviewed for utility and appropriate application across the data program? Make sure your governance framework matches your business in practice and the data management program you hope to implement. Include roles, responsibilities, and compliance requirements. 

2. Identify needs

Reviewing the types of data collected, sources, and the purposes for which it will be used is a crucial foundational step that informs how you update your data strategy. Conduct an inventory and classification exercise to categorize data based on its value, sensitivity, and regulatory requirements. 

3. Deploy supportive tools

Investing in a data management platform that holistically facilitates data storage, processing, analysis, and compliance monitoring can determine the success of your data lifecycle management program. Seek out a solution that stores data securely, maximizes the utility of both in-use and archived data, proactively preserves data integrity while maintaining access, and keeps storage lean and cost-effective by deleting obsolete data. 

4. Monitor and audit

Monitor data usage, access patterns, and compliance with well-defined and frequently updated management policies and tools that track changes and alert IT teams to anomalies before they become full-blown crises. Conducting occasional audits and reviews can also help address any compliance issues and ensure appropriate resources are allocated to tools, personnel, and other data management systems. 

5. Evaluate and improve

Create a culture of data stewardship and accountability by offering frequent training and communicating data policies as they evolve. Get team input on the effectiveness of data lifecycle management practices and tools. Incorporate lessons and refine and enhance your framework over time. 

The future of data

Your data is your competitive advantage, and it lives throughout your business. Instead of letting its scale overwhelm you, leverage a data lifecycle management strategy to eliminate anxiety and create opportunities. From creation to deletion, a systematic approach to handling data is essential in the modern business landscape. 

Data doesn’t just serve your organization’s immediate needs; it underpins a lasting legacy. Own’s mission is to help you create a data legacy rooted in insights, innovation, and integrity. That’s why we’ve created a suite of products that supports data practices businesses can use to build their futures. Our products include automation-centric solutions for storage, usage, archival, and destruction that drive security and greater utility of data—supporting growth now and in the future. 

Learn more about how Own can support your data lifecycle management efforts for Salesforce today.