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Five Ways Big Data Analytics is Transforming Organizations

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Analytics Organizations

Analytics Organizations

Analytics Organizations- Organizations are no longer content waiting until tomorrow to know what happened two minutes ago, nor can they afford to wait. Many operational and mission-critical decisions rely on how fast and accurately they can analyze the growing amount of data streaming into the organization. As the architectural landscape becomes more complex, big data analytics professionals must find a way to wrestle insights from incompatible systems, despite the inconsistency of data. Adding more capacity, hardware and personnel is a poor workaround — it just adds a lot more cost and doesn’t necessarily translate into better, faster insights.

As organizations struggle to keep pace with the rapidly evolving technology, five trends are emerging within big data analytics that empower organizations to handle the volume, velocity, and location aspects of data so they can use it strategically.

1. GPUs to Handle Big Data Volume and Velocity

Big data analytics is pouring off real-time sources like sensors and devices, (cell phones, telematics data from cars, social media streams, server logs, and clickstreams). Much of this data requires immediate analysis, for valuable insights while the information is still relevant. Utility companies, for example, are gathering real-time insights from smart meters, to continuously balance the grid, prevent service interruptions, and reduce emissions.

Legacy systems, leveraging CPU-only architectures with low parallelism, struggle to keep pace with the volume and velocity of data. As a result, they force IT to keep adding more hardware, and hiring more data engineers to pre-aggregate or index the data, just to fit it in mainstream tools.  This slows down the whole data pipeline, and limits the type and amount of insights available from the data.

Graphics processing units (GPUs), in combination with traditional CPU architectures, are now accelerating a new breed of high-performance database engines and visual analytics systems. These GPU-based solutions enable massive parallel processing, and can complete a query in milliseconds that would take hours on a legacy platform.

2. Operational Agility

In today’s high-speed world, most analysts do not have the familiar luxury of getting up to have a cup of coffee while they wait for their query to finish or their dashboard to refresh. While that query-then-wait experience is just plain frustrating, it has real value impacts too: when the analytics experience is too slow, users explore less and find fewer insights.

The trend is toward providing users with an extreme analytics experience, one with zero discernible latency when interacting with the data. Users start to love doing analytics again, and the organization benefits by moving toward a continuous analysis mode, versus one with daily, weekly or monthly analysis cycles.

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Article Credit: Datanami

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