Redefining data protection

In a world where digital transformation determines winners and losers, businesses continue to create increasingly larger volumes of data, and by way of doing so, have evolved to the point where every organization is now a technology company. Further, it is a time where the most significant source of differentiation an organization has is the data that it keeps.

To fully unlock the value of data capital, attention must be paid to where data resides, how it is managed, and how it’s protected. Ultimately, modernizing IT to accelerate data-driven decisions and drive business outcomes is critical to an organization’s success.

Getting data protection correct in this context isn’t always straightforward or easy. There’s complexity associated with protecting data spread across the globe and on numerous platforms, including multiple clouds. Data platforms have become specialized to address specific workloads, further fragmenting the data landscape. Add in the weight of massive data growth – at a rate of 569% since 2016 – and the value of retaining more data for longer periods, plus the persistent concern about disruptive events, and this creates challenges that can only be solved with a data protection solution that spans the entire data footprint.

It is in this world that we must redefine data protection.

Protecting data isn’t only about keeping it safe, but also about ensuring it remains continuously available. Data protection technologies can be used in new ways to accelerate migrations, power DevOps activities, simplify compliance and regulatory requirements, and facilitate the preservation of data for long-term monetization. Data protection can no longer only be considered an insurance policy; it must evolve to be a leverageable service that can help drive business outcomes. Data protection must transition to data management.

Transforming Data into Value

The specific definition and application of “data management” depends on an organization’s goals for acquiring, validating, storing, protecting and processing their data. In many cases, data is captured without a pre-determined use case to leverage it. Access to the right data, at the right time, and being able to extract the right insight from it can be a strategic advantage for the enterprise.

With this objective in mind, how does an organization identify the “right” data management solution?

Effectively, data is at the center of the data management universe, and hence every aspect of a data management solution. Attention must be given to every possible data source, target, service level objective, location, use case, consumption model and business model. Let’s unpack each of these considerations, or more fittingly, requirements:

• Any Source: A solution must be able to capture data regardless of the application, hypervisor, database, filesystem, sensor or device that generates the data. Despite obvious differences in data formats and API’s, the goal of the data management solution is to be the universal conversationalist.
• Any Target: The solution also needs to aggregate, deduplicate and store the captured data to the protection storage target of choice. The target device could be an integrated storage appliance, a commodity hardware-based platform, cloud object storage or a combination of storage targets. Regardless of the choice storage targets, the user should have a common set of data services and a common mechanism for accessing the data from and across multiple target platforms.
• Any SLO: Placing data as the central focus means understanding the value of data and being able to support any Service Level Objective, which is often defined by Recovery Point Objective (RPO), and Recovery Time Objective (RTO). As the value of the data increases, the tolerance for data loss and data unavailability decreases, eventually arriving at a requirement for no data loss and instantaneous recovery.
• Any Location: Data can be generated and stored anywhere – from billions of devices and sensors in a variety of locations, to traditional enterprise and modern consumer applications residing on-premises and in cloud environments. An effective data management solution should be able to operate in in any environment – for both capturing and storing data – as well as to seamlessly span across environments, providing frictionless data mobility while ensuring the right data is available in the right location.
• Any Use Case: A data management solution should support use cases beyond traditional data protection. Instead of allowing secondary data to remain idle, only to be recalled in the event of data loss or data unavailability, the solution strives to extract value from data by making it available for additional use cases. A data management solution can quickly provision low-overhead copies for dev / test, simulation and analytics workloads. It also provides a single system of record for all the data to support compliance and GDPR use cases with visibility into the type and nature of the data for classification and analytical purposes.
• Any Access Method: An effective data management solution isn’t limited to traditional methods of data access and consumption. It can restore the entire data set (or a subset) to a different location or make it accessible in place by spinning up the entire application stack. It can also collaborate with the application to restore the appropriate granularity of data (table, emails, files, users, records) directly in the application dataset. Additionally, it provides rich API functionality for third-party integration and dynamically add capabilities not natively provided by the data management solution.
• Any Consumption Model: A data management solution should be delivered through multiple consumption models – an integrated multi-dimensional appliance, a software-only solution which can direct data streams to target protection storage (PBBA, commodity storage or cloud object storage), or as SaaS. Regardless of the method of consumption, the capabilities, data services and the management interface should remain unchanged. And, a common data format, independent from the underlying storage media, should enable frictionless data mobility across all these consumption models.
• Any Business Model : Capabilities of a data management solution should be acquired through an all-inclusive perpetual license or through an a la carte menu of individual capabilities. Similarly, the flexible, business model can be capacity based (either front-end or back-end) or CPU based when deploying a software-only solution.

Certainly, the list above symbolizes a significant number of factors for organizations to consider when modernizing IT. Organizations invest tremendous amounts of money and effort in creating data, and the availability of that data for use as a leverageable asset represents differentiated business value. Maximizing the return on that investment requires rethinking data protection, including transitioning to a data management solution.

By Sharad Rastogi

Published with permission from https://blog.dellemc.com/en-us/