Ignoring data lifecycle management is putting your business at risk

Enterprises are dealing with increasing amounts of data, and managing it has become imperative to optimize its value and keep it secure. Data lifecycle management is essential to ensure it is managed effectively from creation, storage, use, sharing, and archive to the end of life when it is deleted. 

Data lifecycle management covers the processes, policies, and procedures to ensure data is effectively managed through its lifecycle. It describes how data flows through the enterprise and beyond. It determines where the data lives, whether on-premises or in the cloud. Or a hybrid of the two – outlining how it is harvested, prepared, governed, transported, and analyzed.  

This approach allows enterprises to streamline processes, gather data for specific purposes, get better insights from data in a secure environment, and efficiently share it. At the same time, it reduces risk by ensuring that all data is under control, avoiding inconsistencies, and adhering to a single source of truth.    

In a recent IDC Infobrief, more than half of respondents report that regulatory compliance is a primary factor in deciding how and where they store enterprise data.1 A clear picture of where data lives and how it moves enables enterprises to consistently protect this data and its privacy. 

This strategic initiative also makes data consistently available for insight and maintains its integrity. Without a coherent strategy, enterprises face heightened security risks, rocketing storage costs, and poor-quality data mining.  

Many enterprises have become data hoarders, however. Not all data is useful and can induce data bloat. Before embarking on a data lifecycle management initiative, enterprises need to look at what data they have and what needs holding on to. Identify files that are duplicated, for example, or unused. This will reduce costs and carbon footprint in terms of data storage.  

Modern data platforms can stop enterprises from drowning in a sea of data by integrating AI and ML to enable more efficient, accessible data. For example, AI-automated data entry and ingestion can dramatically improve data quality.   

A decentralized approach to data management  

Data mesh addresses the complexities of scaling data and analytics in a large organization, providing a distributed architecture for data management. It also helps to overcome the challenges of shadow data, which enterprise security policies do not recognize or cover. 

Unlike traditional data architectures such as data warehouses or lakes, where data is collected, stored, and processed in a single location, data mesh places data in defined groups owned and managed by the domain teams closest to them. The idea is that creating a more flexible infrastructure that opens up data to knowledge users accelerates insight, significantly increases productivity, and improves business outcomes.   

This self-service approach with a single point of control ensures high-quality data deliverables. Separate data sets enable independent teams to work on data sets, providing faster data insight while maintaining regulatory compliance.  

The correct data is central to successful business practices.  

Data lifecycle management is no longer a nice to have. It is imperative to keep up with continually changing data management requirements, security policies, and compliance. At the same time, providing a competitive edge, ensuring data is utilized for maximum business benefit.   

To find out more, sign up for the Orange Business Webinar here 

[1] IDC InfoBrief, sponsored by Orange Business, A Framework to Earn and Sustain Customer Trust, Mitigate Risk, and Drive Revenue Growth, #EUR150405923, June 2023

Data Management