When it comes to data, many modern businesses are hoarders. The average company keeps more than nine copies of any given piece of information. So, the idea of “data” being a singular unit, is no longer accurate. Data is rather a set of similar, but often distinct copies of the same information, each with its own unique purpose in the business; whether that be for archives, disaster recovery, DevOps or various others.
Data is often the best solution when something goes wrong – but only if the right information can be found and accessed quickly. This is increasingly becoming a challenge for businesses today, due to the overly-complicated nature of data management systems. Over the past two years more than 75% of businesses have been unable to surface the right data, according to a 2017 survey by Forrester.
Databases vs data lakes: Which should you be using?
Break down the silos
Data management and complexity haven’t always gone hand in hand. As modern businesses scale, operational silos are often created, which have diverse needs and complex IT architecture.
A classic example: an organisation’s legal team needs data, but they aren’t familiar with the backup team or perhaps they don’t trust that the right data can be obtained in way that is permissible for legal use. As legal needs to be incredibly careful how it obtains data, the team decide to retain their own backup copy. This example can be multiplied across the whole organisation, resulting in a plethora of data copies.
Data copies are siloed not just by department or use case, but also by platform. The range of backup solutions run across various technology architectures, including: legacy on-premises backup for physical servers and databases, hybrid solutions which operate on-premises but push data into selected archives (cloud or local), hosted backup (from a managed services provider or MSP), backup-as-a-service, and, for some, backups from edge computing such as IoT systems. The Forrester survey found that 79% of organisations have at least three backup solutions, and more than 26% have five or more solutions.
Data silos and legacy systems are preventing data insights
Given the rate of data growth today, this siloed approach isn’t a practical or manageable option. “Exponential data growth” is identified as a challenge by more than half (56%) of IT professionals. Compliance and regulations, such as GDPR, magnify the challenge by requiring companies to exercise greater control over their data. An industry dependency on SaaS solutions such as Salesforce, G Suite and Office 365, create additional silos and make it harder still to consistently protect data.
For true digital transformation, data silos must be torn down and replaced by a more accessible data lake, which in turn, will empower more effective decision-making, more complete insights, and better-informed automation. In today’s world, data protection and management need a holistic and integrated solution. In the case of data copies; less is definitely more.
The solution is in the cloud
The solution to this fragmented landscape lies in the cloud. Beyond the obvious business benefits of the cloud (cost, agility and scalability), the cloud augments an enterprise’s architecture to support different use cases, removing barriers and making it viable to use a single dataset which supports each unique requirement. Due to the inherent centralised and collaborative nature of the cloud, it is easy for various departments to address different use cases with the same data.
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Cloud-based operations and backup services offer an immediate solution for data recovery needs. The cloud removes the need for offsite locations and allows operational continuity without the need for specialised resources or considerable investment. There is much more to data management than backup and disaster recovery alone, of course. DevOps can utilise the latest copy of data, through cloud backup, to support A/B testing and improve development speed. The application of this data is almost limitless, when analysed by machine learning and AI applications: for example, a medical device company can learn from its archive of surgical data to improve precision times of devices.
Change in architecture and business processes can be slow, and the vision for a proper data lake is unlikely to be implemented across the entire business in one push. Disaster recovery, forensics and DevOps are already accustomed to working directly with data management and therefore provide a natural starting point for leading the transition. A cloud-based data lake model holds the key to unlocking the untapped value of business data — the onus is now on business leaders to set their organisations off on the right path to achieve this.
