Outdated Data Storage Methods Are Damaging Business Performance

Iron Mountain has analyzed business data management practices, which reveal that many enterprises lack the ability to maximize the value of the vast quantities of data they generate.

Committed to helping customers CLIMB HIGHER to transform their businesses and improve commercial performance, Iron Mountain has developed ‘How to understand the value of your data’, a step-by-step guide on how enterprises should assess, process and store data in order to best extract new value, build operational resilience and deliver business impact.

“Every day, a staggering 200 Exabytes of data is generated worldwide. That’s 1.1 Gigabytes per person, per hour,” said András Szakonyi, Senior Vice President, Europe, Middle East and Africa (EMEA) at Iron Mountain. “Enterprises account for 42.2% of that number, which is creating new challenges around data storage and management. These obstacles need to be overcome as organizations that make best use of their data to inform mission-critical decisions have the potential to gain a competitive advantage.”

Iron Mountain research reveals that COVID-induced investments in digital solutions have exploded, with most data decision-makers (86%) saying their deployment gave their organization a competitive edge. The findings from the study inspired the creation of the guidebook, which looks at the reality of data management in 2021 and provides practical guidance for organizations to ensure that they have the tools to succeed.

‘How to understand the value of your data’ details six steps that when combined can prevent valuable data ending up in a digital landfill:
● Take a proactive stance towards data management
● Develop a robust data tiering system
● Invest in data lifecycle automation tools
● Only pay for the storage space you actually need
● Make your data work smarter, not harder
● Establish best practices, organization-wide

The guide expands on each of these actions to create practical strategies for maximizing the value trapped in inefficiently managed data.

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