The traditional approach to data management has always been a scale up approach – when you run out of capacity you buy more, writes Hemant Harie, MD at Gabsten Technologies. The trouble is that data is growing exponentially.
In fact, according to Statista, data centre storage capacity worldwide will exceed 2000 exabytes in 2020. As the pace of data growth accelerates, the frequency with which organisations will run out of storage capacity will likewise accelerate. Storage tools evolve over time and organisations may not always purchase the same solution as previously, which results in an architecture that is a ‘mix and match’ of a variety of different technologies.
This is not only complex and costly to maintain, it also makes effective data analytics all but impossible due to the number of siloes of information. Adopting a more modern approach to data management is critical in addressing these challenges and mitigating the risks associated with fragmented infrastructure.
The challenges of a traditional approach
Traditional scale up architecture creates a number of issues as it grows over years and decades. Storage platforms are always evolving, and the result over the course of a decade or two is a multitude of different systems running in parallel. Organisations typically end up having to manage and maintain far more systems than they originally planned for, and all of these systems need to be protected, managed and maintained.
Multiple systems all performing more or less the same task adds complexity and thus cost, since each system needs a specific resource and skill set to manage and maintain it. There is also no single view or universal visibility of how data is moving and how it is managed, making analytics ineffective and compliance impossible. Given the rate that data is expanding at today, this approach is neither a sustainable nor an economical path to continue upon.
The evolution of data management
Data infrastructure strategy directly impacts and controls data management. It is no longer sufficient to focus on production data and then plug backup into it. In today’s world where data is a de facto currency, backup needs to be considered as part of an overall data management strategy. Moreover, backup should support infrastructure as it changes and include additional elements such as disaster recovery, replication, archiving, de-duplication and network management. Because data touches everything, it is critical to consider its effect on all areas and how this needs to be managed.
Coping with complexity
As data centres become increasingly large and complex, businesses are finding it increasingly difficult to manage their own architectures. In 2020 and beyond, modernisation should focus on streamlining infrastructure and leveraging the growing array of elements offered as a service. Managed services not only reduce the capital outlay associated with hardware and licensing purchases, they also reduce maintenance requirements and the resources required for this task. The result is reduced complexity with the same or improved functionality.
One way of modernising data infrastructure and data management is to move towards hyperconverged infrastructure, which reduces the physical footprint of data storage while simplifying management. However, organisations need to bear in mind that if the production environment is migrated to hyperconverged infrastructure, the backup solution should likewise be moved over. Data management therefore needs to develop in tandem with the infrastructure or else complexity and cost once again increases and effectiveness decreases.
The vision for 2020 and beyond
This is the year where data management should become a priority for organisations. With all of the security challenges in the global IT space, from ransomware to denial of service attacks, it is more important than ever to ensure data is secure.
It is critical that organisations regularly evaluate their data management processes, because both infrastructure and business requirements are constantly changing. Moreover, the data management strategy should ensure that growth projections are accurately planned for and that data growth is aligned with these projections.
This allows businesses to identify and correct anomalies as and when they occur and avoid outgrowing their storage capability. Effective modernised data management reduces complexity and cost while delivering a single pane of glass view into all of your data for management under a single umbrella. This in turn facilitates data analytics, which requires a cohesive view of all data in order to be effective.
A modernised approach to data management should enable improved management, planning and budgeting by making all of these elements more predictable. The ultimate goal is to become smarter, simpler and easier to manage as data itself continues to grow and become more complex in 2020 and beyond.