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Leveraging the Cloud to Better Mine Data in the Extreme Data Economy

In this special guest feature, Nima Negahban, CTO and Co-founder of Kinetica, observes that the demand for cloud solutions for data analytics is skyrocketing, and advanced analytics software now exists that supports both private and public cloud. No longer is it about businesses making decisions informed by data; in today’s Extreme Data Economy, data is the business. Nima is the original developer and software architect of the Kinetica platform. Leveraging his unique insight into data processing, he established the core vision and goal of the Kinetica platform. Nima leads Kinetica’s technical strategy and roadmap development while also managing the engineering team. He holds a B.S. in Computer Science from the University of Maryland.

Data represents a veritable goldmine for enterprises today. When analyzed effectively, it molds business strategies, informs decisions, drives investments and enables hyper growth. While many enterprises are collecting vast amounts of data today, very few have cracked the code on how to maximize their analysis of it in order to produce business-critical insights and power their company. After all, what’s the use of sitting on a bunch of gold without investing it? The same principle applies to data, especially with the transition into an Extreme Data Economy, which necessitates that companies act with unprecedented agility in order to reap real, actionable results from their data.

The increasing scale, speed and complexity of data means that new computing architectures are needed to accelerate data analytics and bring insights to businesses. Augmenting AI and machine learning with traditional analytics further enhances the depth of insight. This insight eventually turns into decisions, a product or even a service that can be monetized.

To get the most value from data, businesses need to leverage newer computing architectures that enable augmented analytics and other advanced analytical capabilities like location intelligence. In the past, getting access to these modern architectures would take a very long time and involved a lengthy process: place an order through procurement, wait months for hardware to arrive, then install and setup, and finally access the environment.

With the advent of the cloud, combined with arrival of the general-purpose GPU and the massively parallel compute environment, the speed to insight and speed to getting value from data has dramatically changed. NVIDIA GPUs are ubiquitous today and available in all environments, be it the public cloud across Amazon Web Services, Microsoft Azure or the Google Cloud Platform, or servers built by Dell, IBM and others. Marketplaces like the NVIDIA GPU Cloud make it easier to use GPU-accelerated software in any cloud environment.

Enterprises which integrate advanced analytics and machine learning and the cloud into their data analytics and insights programs are reaping the most from their data. For private clouds, containerized environments and the use of orchestration technologies like Kubernetes can simplify operations. Public clouds provide elasticity and are good for workloads when the resources needed may not be known beforehand, and when the different instance types and configurations are constantly growing, including those that support GPUs.

Over time, as applications include AI and machine learning, a serverless method of deployment will be the norm and the cloud will play a critical role in this. Whether public or private cloud, an endpoint API will point applications to different models for different use cases and calculations. For example, pricing will be computed in real-time by using a pricing model that is deployed in a serverless mode to inference on an on-demand basis.

With all these new approaches, business today have many options available to handle the explosion of extreme data in the digital age. They will need to leverage streaming data analysis, machine learning and visual foresight in order to be operationally successful with the cloud, allowing them to see a return on investment faster.

Access to data anytime, anywhere can make businesses practically omniscient, but only if they analyze it quickly, efficiently and with great sophistication. With GPU processing power now nestled in the cloud, enterprises can have an always-on, always-there data analytics system at their disposal that promotes stronger, near instantaneous insights. And use of Kubernetes and other container and orchestration approaches help with easy migration from cloud to cloud or to build hybrid cloud environments.

The demand for cloud solutions for data analytics is skyrocketing, and advanced analytics software now exists that supports both private and public cloud.  No longer is it about businesses making decisions informed by data; in today’s Extreme Data Economy, data is the business. Successful companies, no matter their industry, are becoming data companies first and foremost. Those that embrace cloud computing and a cloud-native mentality, along with creating a strong advanced analytics program for powering data analytics and management, will be the companies that successfully scale and achieve new heights.

 

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