The amount of data created globally is expected to grow to more than 180 zettabytes by 2025. For context, 1ZB is equal to a trilliant gigabyte. It should hardly be a surprise that organisations are under immense pressure to manage this ever-swelling sea of data to extract meaningful insights for competitive advantage as quickly as possible.
By Rowen Grierson, senior director at Nutanix
Take the healthcare sector as an example. Providers must be agile to respond to fast-changing demands for disease surveillance, biomedical research, and population health. They must also continue to collect and analyse disparate data to drive research, track the spread of diseases, and monitor medical supply chains while continually improving healthcare to deliver better patient outcomes.
Across industries, the rise of edge computing results in machines creating data at faster rates. This drives an equally high demand for storage growth. Invariably, companies must manage the complexity of scaling for the copious amounts of data generated while trying to mitigate the risk of perpetuating siloed storage environments.
These legacy solutions reduce the visibility organisations have of their data which minimises its potential for analysis and fresh insights. Ultimately, the benefits of generating this data to begin with are nullified by this inability to act.
Structured versus unstructured
Before developing strategies that consolidate data for competitive intelligence, decision-makers must understand the differences between structured and unstructured data. Yes, they have technical experts in managing the nuts and bolts of implementation and management. Still, they must be able to provide the direction needed to empower these specialists to extract value from data consolidation.
As the name suggests, structured data is information that can be neatly organised into a set structure. Think of spreadsheets with their tidy rows and columns. Relational databases such as those used in placing retail product orders, making hotel reservations, or setting up bank accounts are all examples of this. People can read and understand this data, and it does not require any specialised training to use.
However, because it is predefined, the data can only be used for its originally intended purposes. Furthermore, relational databases cannot quickly grow their storage capacity as this negatively impacts on the query and, in turn, application performance.
For its part, unstructured data is everything else that does not fit neatly into a row and column format. Think social media content, audio recordings, video footage, email, chat transcripts, and so on. Gartner estimates that unstructured data make up 80% of all enterprise data. Because unstructured data has no predefined model, traditional tools developed for structured data cannot process and analyse it.
Typically, this data is stored on flash drives, local servers, and data lakes. It, therefore, requires specialised, advanced tools and solutions to analyse it and extract value in meaningful ways to make insights actionable.
However, given its nature, this data is easy to collect, storage is massively scalable, and it provides tremendous flexibility in how it can be used. Unstructured data is where the gold nuggets are hidden, just waiting to be discovered.
Managing complexities
To address the challenges of managing structured and unstructured data, organisations need to consider a new operating model that delivers a more consistent platform spanning disparate databases and clouds. Part of this can also entail turning towards an on-premises database-as-a-service architecture. Combining virtualised and on-premises solutions are essential considering that some workloads remain on bare-metal servers due to their licensing constraints, sensitivity, application portability, or investments in existing infrastructure.
Implementing a database management solution that delivers cloud-like services capable of integrating with a foundational IT infrastructure can be a game-changer. This will empower the business to manage its databases through an API or user interface, making it more scalable while simplifying operations and saving staff time to process and analyse the data. It also allows for the introduction of an abstraction layer above existing heterogeneous database technology. This helps to alleviate worries about how disparate databases interact with the current infrastructure.
Such a database management solution can also simplify database operations to let a business spin up many common databases as required. This ensures the company can scale up computing and storage and adjust to shut it down quickly when no longer needed. Perhaps most importantly, this solution can centralise data with a single management plane across all business databases. For instance, a company can quickly get a backup snapshot of an Oracle database the same way it can get one for a MySQL database.
Consolidating storage and the associated data on a private cloud solution removes data siloes and allows the company to embrace a more service-oriented architecture. Doing so provides IT teams with a unified management plan that enables easy file, block, and object storage while delivering the analysis required to unlock business intelligence.