Taking Analytics to the edge

In my previous article, I had written about HPE's EdgeLine servers for IoT analytics.

In 2017, we are seeing a steady wave of growth in data analytics that's happening on the edge and HPE is in the forefront of this wave - leveraging its strengths in hardware, software, services, and partnership to build powerful analytic capabilities.

With HPE EdgeLine, customers are able to move  analytics from the data center to the to the edge, providing rapid insights from remote sensors to solve critical challenges in multiple industries like energy, manufacturing, telecom,  and financial services.

Why IoT project fail?

Recently, Cisco reported that  ~75% of IoT projects fail. This is because IoT data has been managed in centralized, cloud-based systems. In traditional settings, data is moved from a connected 'thing' to a central system over a combination of cell-phone, Wi-Fi and enterprise IT network, to be managed, secured, and analyzed.

But with IoT devices generating huge volumes of data, and data being generated at multiple sites - even in remote areas with intermittent connectivity. This meant that analysis could not be done in a meaningful way as the data collection was taking time, and when the analysis was completed, results were computed, it was irrelevant.

Centralized cloud systems for IoT data analysis just does not scale nor can it perform at speeds needed.

HPE Solution - EdgeLine servers for Analytics on the Edge

With HPE EdgeLine servers, we now have a  solution that optimizes data for immediate analysis and decision making at the edge of the network and beyond.

For the first time ever, customers have the first holistic experience of the connected condition of things (machines, networks, apps, devices, people, etc.) through the combined power of HPE EdgeLine servers and Aruba wireless networks.

Analysis on the edge is just picking up momentum and it's just the beginning of good things to come.

Today, cloud is omnipresent, but for large scale IoT deployment, a new model of computing is needed emerge where constant cloud connectivity is not essential. Most data will be processed at or near its point of origin to provide real-time response and will be handled on-site in that  moment. Running analytics on edge will save costs, refine machine learning on massive data sets -  that can be acted on at the edge.

In June 2017 at HPE Discover, customers were delighted to get an in-depth view of this solution.

HPE's continued investments in data management and analytics will deliver a steady stream of innovation. Customers can safely invest in HPE technologies and win.

HPE along with Intel is future proofing investments in data and analytics for hyper distributed environments. HPE has taken a new approach to analytics to provide the flexibility of processing and analyzing data everywhere - from right at the edge where data is generated for immediate action and for future analysis in the cloud at a central data center.

Customers are using IoT data to gain insight through analytics, both at the center and the edge of the network to accelerate digital transformation. With HPE Edgeline, one can take an entirely new approach to analytics that provides the flexibility of processing and analyzing data everywhere—at the edge and in the cloud, so it can be leveraged in time and context as the business needs to use it.

This technology was developed in direct response to requests from customers that were struggling with complexity in their distributed IoT environments. Customers, analysts and partners have embraced intelligent IoT edge and are using it in conjunction with powerful cloud-based analytics.

Analytics on the edge is a game changing approach to analytics that solves major problems for for businesses looking to transform their operations in the age of IoT. The HPE Vertica Analytics Platform now runs at the IoT edge on the Edgeline EL4000. This combination gives enterprises generating massive amounts of data at remote sites a practical solution for analyzing and generating insights.

Customers like CERN, FlowServe,  etc are using Edge analytics to expand its monitoring of equipment conditions such as engine temperature, engine speed and run hours to improve maintenance costs. Telecom services companies are pushing the edge with analytics to deliver 4G LTE connectivity throughout the country, regardless of the location of the business.

Closing Thoughts 

Benefits of centralized deep compute make sense—for traditional data. But the volume and velocity of IoT data has challenged this status quo. IoT data is Big Data. And the more you move Big Data, the more risk, cost, and effort you'll have to assume in order to provide end-to-end care for that data.

Edge computing is rebalancing this equation, making it possible for organizations to get the best of all worlds: deep compute, rapid insights, lower risk, greater economy, and more trust and security.