2021 CIO Guide to Modern Data Protection

Many organizations may need to rethink their mindset on data backup and recovery. Advanced, automation-driven data management solutions that provide unified coverage across infrastructures and formats can turn data from a cost and risk to be controlled into a participatory player in a more comprehensive operational strategy.

The sudden pivot to remote work of the customer experience in 2020 created a spike in the volume and type of data facing organizations. Many businesses turned to the cloud to scale up data capacity and agility. However, data backup solutions were often not upgraded in parallel: despite its critical importance, data backup has been one area where businesses have often had a “set it and forget it” philosophy. This tactic can backfire, leaving organizations vulnerable to failures, cybersecurity breaches, and rising data storage and maintenance cost. Along with enhanced protection, today’s backup solutions now contain significantly more capabilities, enable better data utilization, and provide stronger ROIs than antiquated systems of the past. Businesses revisiting their data strategies need to ensure modern backup, recovery, and management is a fundamental part of the picture.

Data Protection Drives Resiliency

Risk mitigation is a key tenet for business continuity and resilience. While organizations typically retained their overarching modernization goals in 2020, strategies to meet those goals tended to focus more on resilience than growth: Frost & Sullivan research1 shows that the top two priorities for businesses in 2020 were improving efficiency and enabling a better customer experience.

The changes brought forth by events in 2020 also resulted in a significant uptick in the volume and diversity of data being generated and streaming into organizations. The move to remote work meant new systems had to be in place that could accommodate thousands—even hundreds of thousands—of employees now logging in from new equipment or personal devices. In-person interactions, whether with customers or value chain partners, had to be virtualized, which required new tools, apps, and features and resulted in even more data. To capture, process, and utilize waves of new and different information, organizations turned to the cloud.

The cloud is seen as a leading strategy to ensure data hygiene and resiliency, but it also creates problems by obscuring organization visibility. On the one hand, 79% of IT professionals interviewed by Frost & Sullivan planned to improve their disaster recovery (DR) capabilities by moving to the cloud2. On the other hand, cloud usage can be ad-hoc rather than closely tied to an organization’s strategic growth objectives, making it difficult for IT teams to identify and mitigate gaps in backup and recovery. As cloud usage expands, many organizations find themselves juggling multiple cloud providers: an average of three, per Frost & Sullivan research.

Cybersecurity attacks also rose precipitously in 2020. For example, detected and blocked attempts at ransomware rose 715% in the 12 months ending in June 2020 as compared to the 12 months prior3. The combination of growing security risks, rapid cloud adoption, and the lack of prioritizing data backup and recovery creates a tenuous situation that may result in unforeseen gaps, security vulnerabilities, and lost productivity.

Protecting and backing up data is among the most elemental responsibilities of IT, with the “3-2-1 rule” (three copies of the data in two different formats, plus one copy off-site) a common guideline that illustrates the need for spreading risk; hence, most organizations employ this guideline at some level. Engaging backup and disaster recovery suppliers is increasing as well, with 46% of companies anticipated to use a BaaS provider by 2023, up from 29% in 20204. Despite this trend, current processes can be antiquated, and older systems are rife with risk: their reliance on manual tasks make them prone to human error, leading to efficacy gaps that leave data vulnerable.

Data storage costs are spiraling upwards as well, whether on-premises (on-prem) or related to unplanned cloud capacity growth. While the amount of data generated in 2020 was exceptional, the expansion of data generation has been ongoing for years and is not likely to abate. Systems may not have been built to withstand rapid increases in data or to handle information on the cloud, in multi- or hybrid cloud scenarios, and across different platforms and even formats.

The improvements that new and advanced solutions have over archaic ones are stark: for one, the cost and coverage picture is much better. Modern solutions that employ artificial intelligence (AI)-driven automation need less manpower to run, and responsive cloud storage keeps data costs in check—by some estimates, these solutions can be as much as 50% less5 costly in terms of total cost of ownership than their older counterparts. Data management platforms can also increase IT efficiency by 30% and respond 73% faster to problems.

In terms of performance, advanced archival solutions furnish much higher levels of data protection, reporting near-perfect RPO and RTO. Scalable cloud storage means comprehensive coverage across all infrastructures, platforms, and formats that are accessed and managed through a unified vantage point. It also means faster duplication and redundancy. The cloud’s computing power is what allows AI-based automation to not only be more effective and efficient, but to learn and improve over time. Cross-platform capabilities go beyond providing improved organizational visibility to also include data mobility across platforms, further improving storage, access, and utilization.

Within the realm of data protection, advanced solutions provide better security, value, and the ability to grow and evolve with an organization. In more sophisticated scenarios, modern backup solutions can even help organizations reach into and leverage stored data to augment the growing tools that require vast amounts of information. Organizations are realizing that they need ways to utilize all data more intelligently.

From Automation to Autonomy: Managed Disaster Recovery (DR) takes Data Protection and Resiliency to the Next Level

Protection is non-negotiable, yet modern providers can do more than protect data: they can manage data intelligence and build data trust. Effective data management draws on automation to streamline critical functions such as backup scheduling, replication management, and file restoration orchestration. As noted earlier, a major benefit of automation is reduced human intervention. This lessens the risk of mistakes that can lead to anything from a missed update to a compliance infraction. It also removes a potential threat vector: an estimated 30% of data breaches have internal collaborators, whether intentional or not. Advanced systems are also more efficient. They are built to automatically find the quickest, most direct backup and recovery methods, reduce recovery times, increase data availability, and to simplify process management. Over time, AI-based solutions can determine the best place to store and retrieve data based on usage patterns.

While all data needs to be secure, some needs to be more secure: market performance trends used by an investment firm, for example, may not have the same security demands as a client’s account information. Data is also accessed at different times and frequencies. Intelligent automation helps ensure that the right protection and access are optimally executed from the start. It can also learn how security needs evolve over time, based on changes in, for example, usage, policies, or regulations. Imagine the benefits that such a system would have brought to an organization implementing GDPR compliance or ensuring secure, effective data integration as part of an acquisition.

Intelligent automation can track, identify, record, and analyze issues to suggest event resolution strategies. When combined with real-time monitoring, it can accelerate response time and reduces outages and related downtime. Timely notification helps address any issues that the data management system itself cannot execute automatically. Over time, however, the system moves from automation to autonomy, detecting changes in the environment to recognize issues early on, even to the point of predicting a potential failure before it occurs. Advanced tools learn the likelihood of actions leading to certain events and how schedules, policies, and workloads should be managed to ensure complete recoverability. Advanced solutions add significant value through management, above and beyond critical backup and recovery services. Building intelligence through advanced orchestration and management creates greater trust in data, providing an avenue for strategies that more valuable than basic DR.

Beyond Management: Wielding Data’s Potential

Perhaps the most exciting aspect of modern data backup, recovery, and management is unleashing the potential of underutilized data. Industry-leading data protection providers go beyond data backup and disaster recovery to include expanded and responsive storage, AIdriven management, and advanced analytics on a single platform. This is elevating backup and DR from playing a role in business continuity assurance to being fundamental to an organization’s comprehensive data strategy.

Businesses should think beyond their data stores as simply a place to run to if and when disaster strikes. Rather than a cellar that contains copies of missing or corrupt information, comprehensive backup stores can be the ingredients of an entirely separate “test kitchen” for DevOps and DevTest teams.

Backup data can automate the environment requirement for update and patch testing and troubleshooting. It can deliver a protected production space where new apps can be run in a sandbox-type scenario. It can also be a near-real time resource for advanced analytics, reducing the need for multiple-data replication across the company.

This is critical for external products as customer experience and ratings are often at the top of the list of organizational priorities. A Frost & Sullivan survey published in February, 20207 showed that customer satisfaction rates are the top metric organizations use to measure digital transformation success (44% of respondents), even more so than revenue increases (38%) or cost savings from automation (37%). Misfires on customer-facing upgrades or new apps can have lasting and detrimental effects on an organization.

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2021 CIO Guide to Modern Data Protection


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