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Unstructured Data: Making Sense Of The Mass And The Chaos

Enormous data growth is coming. In fact, market intelligence firm IDC predicts data will increase 61% to 175 zettabytes by 2025. Organisations today are amassing vast amounts of data; everything from customer information to the burgeoning mountain of sensor and machine data from the adoption of the Internet of Things.

While a lot has been said about the potential of data, unstructured data accounts for 80 – 90% of the overall digital data footprint. This unstructured data, which exists in forms such as images, search data, videos and sensor data, is largely unsearchable and difficult to analyse–posing a significant challenge for businesses.

What’s more, much of this data exists in complex infrastructure and silos, such as data lakes, data warehouses, storage area networks and backup systems. At one point in time, these silos served a purpose. Now, they create massive headaches for IT teams, who have to manage these complex systems, and end users, by making it harder to pull information from across the organisation to extract insights.

The good news is, organisations at the cutting edge of their industries have grasped that what was once “cold” or “dark” data has an increasingly important role to play in helping them to maintain their market-leading positions. Examples of these workloads can be found in areas such as genomics, semiconductor design, data analytics, media and entertainment, oil and gas exploration and computer-aided engineering (CAE), among others. Clearly, being able to gain useful insights from unstructured data is crucial to producing an overall competitive advantage for organisations in a variety of sectors.

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Producing accurate analytics and training data-hungry artificial intelligence and machine learning algorithms requires an intensity, speed and volume that can often cripple legacy storage systems and media. Therefore, finding needles in the haystacks of unstructured data requires a long hard look at the existing storage foundation.

A modern data experience requires infrastructure that can bridge silos and meet demands for performance, agility and simplicity, without complexity or compromise. This calls for a data hub approach.

At the core of data hubs is a data-centric architecture designed for the very purpose of data sharing across the four many silo groups: data warehouses, data lakes, streaming analytics and AI clusters. This approach integrates their strengths onto a single, unified platform, eliminating bottlenecks across the applications that need data for better insight. Additionally, data hubs are simple and elastic, allowing applications to spin up and down resources as needed.

Pure customer Searcher Seismic is a good example of a local organisation that has successfully built a modern data hub. Searcher Seismic is an independent company that provides non-exclusive datasets to oil and gas companies, which was historically a long, people-intensive process. Having a data hub based on Pure Storage’s FlashBladeenabled Searcher Seismic to accelerate data ingestion by 20 to 30 times, which overcame a major hurdle for the company given the amount of data they were dealing with. As a result, Searcher Seismic was able to introduce a valuable service for clients, web-based visualisation, unlocking new revenue streams for the business.

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The tremendous growth of unstructured data is creating huge opportunities for organisations. Until recently, the potential to maximise unstructured data had been restricted by the limitations of legacy storage systems. Fortunately, with a modern data hub architecture built on a next-generation storage infrastructure, such as Pure FlashBlade, organisations can extract real-time insights at the speed of business while enjoying cloud scalability and operational simplicity.

source; ITbrief

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