Big Data

Beyond the hype: Designing a cloud for data

Share this post:

Beyond hype designing cloud for dataMany of the tech industry headlines I’ve read this week have discussed advancements in AI and deep learning, describing how cloud providers are enabling enterprises to unlock the value of the vast amounts of data now stored in the cloud. IDC estimates that by 2018, nearly 75 percent of developers will build AI functionality into their apps.

To keep up with rapid demand, data scientists and developers are constantly working to securely connect data across public, private and hybrid cloud environments so it can be continually ingested to power AI apps and deep learning models. As they navigate these challenges and break new ground, it’s important to distinguish between the hype and reality of a cloud that is designed for data.

At IBM, we know that the value in data is not just in collecting and securing it, but in extracting insights and knowledge for better decision making. Enterprises have moved beyond pure infrastructure-as-a-service and are using cloud for AI, analytics, blockchain and other new capabilities to help unlock these insights.

Data scientists and developers need not only the right cloud-based tools and technology, but also a broad range of database services to ensure they can consistently access and quickly turn structured and unstructured data into workable data sets regardless of where it resides. This helps reduce the tedious processing that data scientists have had to do in the past, giving them more time to analyze data and draw out insights that can help shape business decisions.

Additionally, having a high-performance network that is built and optimized for data-intensive workloads is critical. This requires a low latency, global network as well as infrastructure such as GPUs that can quickly process massive amounts of data in parallel.

Last, but certainly not least, security must be at the heart of the cloud to help enterprises protect their valuable data across public and hybrid deployments. This includes around-the-clock monitoring and alignment with key industry security standards, as well as advanced encryption, data protection and application security capabilities to name a few.

These features and characteristics are critical for data-intensive workloads across every industry. For example, we recently announced that Walgreens, one of the country’s largest drugstore chains, will deploy IBM retail analytics at more than 8,100 locations nationwide to help improve the efficiency of field service support. IBM Cloud will be used to determine the level of support that will likely be needed at each Walgreens location based on service request history. These data-driven insights can help to identify the most frequent service calls at a given location and bundle those requests into one service call to minimize repeated instances of system downtime.

IBM continues to help enterprises, data scientists and developers unlock the value of data. A few recent examples include:

  • An expansion of the Watson Data Platform, including data cataloging and data refining. The expansion is designed to make it easier for developers and data scientists to analyze and prepare enterprise data for AI applications, regardless of its structure or where it resides.
  • The launch of IBM Cloud Private, a new platform built on the open source, Kubernetes-based container architecture and containers to bring cloud-native environments behind the firewall, accelerate app develop and maintaining data security across cloud environments.
  • An IBM global footprint of nearly 60 locally owned and operated data centers in 19 countries helps meet data residency and data protection regulations. We recently announced a new support model and capabilities coming soon to IBM Cloud in Germany to give clients control over and transparency with where their data lives, who has access to it, and what they can do with this access.
  • The availability of high performance bare metal servers and advanced NVIDIA P100 GPUs, making it faster and more cost-effective to train deep learning models.

As we read the headlines and listen to cloud providers talk about advancements in AI and new data tools, remember that not every solution is one size fits all.

Learn more about why IBM Cloud is the right choice for so many businesses and read how IBM provides a cloud that is designed for data, enterprise strong and AI ready.

More Big Data stories

Feeding the planet as the climate changes with rapid analytics on IBM Cloud

The latest climate research indicates that the planet might be warming at an even faster rate than previously thought. As extreme weather events such as droughts become more common, it’s becoming more difficult and costly for farmers to irrigate their fields, making crop failures more likely. The current world population of 7.3 billion is expected […]

Continue reading

Data management and Kubernetes come together in IBM Cloud Private for Data

In addition to the short-term benefits of IBM Cloud Private for Data — a data management platform built on Kubernetes with support for Db2, MongoDB and Postgres databases — General Manager for IBM Analytics pointed to the long-term effects in an article at Container Journal this week. As IT organizations continue to take advantage of […]

Continue reading

The best technology grows and innovates over time

In 1993, as grunge and alternative music picked up momentum, the first beanie babies were released and Jurassic Park dominated the box office, computing was making big strides, too. The World Wide Web was born at CERN and Intel released the first Pentium microprocessor. IBM also released the world’s flagship enterprise messaging technology: MQ. Messaging […]

Continue reading