More data means more opportunities to discover powerful, actionable insights around customers, internal processes, and the broad market. Unfortunately, legacy IT architectures and approaches can block progressive analytics efforts.
That leaves a lot of room for improvement, and the learning and investment curve for starting in analytics can be steep. However, tending to three core steps will prove immensely helpful on this journey:
• Establishing an organizational foundation
• Mapping the data pipeline
• Transitioning analytics proofs of concept into production
OpenShift-Kubernetes offers an excellent automated application deployment
framework for container-based workloads. Services such as traffic management
(load balancing within a cluster and across clusters/regions), service discovery,
monitoring/analytics, and security are a critical component of an application
deployment framework. Enterprises require a scalable, battle-tested, and robust
services fabric to deploy business-critical workloads in production environments.
This whitepaper provides an overview of the requirements for such application
services and explains how Avi Networks provides a proven services fabric to
deploy container based workloads in production environments using OpenShift-
Published By: Attunity
Published Date: Nov 15, 2018
Change data capture (CDC) technology can modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems.
To realize these benefits, enterprises need to understand how this critical technology works, why it’s needed, and what their Fortune 500 peers have learned from their CDC implementations. This book serves as a practical guide for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC.
Read this book to understand:
? The rise of data lake, streaming and cloud platforms
? How CDC works and enables these architectures
? Case studies of leading-edge enterprises
? Planning and implementation approaches
This whitepaper discovers how by implementing cloud computing across 6 fundamental workloads can transfer the way whole groups employees do their jobs, enabling them to speed new development and uncover new sources of revenue.
Global organizations such as Nestle?, Shell and others retain their leadership by solving their toughest challenges with advanced optimization. Manufacturers are seeking ways to apply advanced analytics and optimization through the entire planning and production lifecycle. This can help you achieve:
Improved profitability, reliability and revenue by 10% or more
Deploy a new planning solution in as little as 3 months
Compute ready-to-implement plans for complex tasks within minutes
Reduce your dependence on IT and rapidly evolve solutions
Leverage industry-leading analytics and optimization and innovative modeling methodologies
Monetise your existing analytic, ERP and other solutions, accelerate growth and reduce costs
Learn How Three Manufacturers Are Using Optimization to Revolutionize Manufacturing
The Path to Predictive Analytics and Machine Learning This Ebook will be your guide to building and deploying scalable, production-ready machine-learning applications. Inside, you will find several machine learning use cases, code samples to help you get started, and recommended data processing architectures.
Your goal is clear-produce high-quality goods while optimizing resources at every step of production. And in today's uncertain economy, cost-control efforts may never have been more important. Unscheduled downtime because of equipment failure can have a serious impact on your organization's bottom line. Download this white paper from IBM, and learn the basics of predictive maintenance, the benefits it provides manufacturing operations and the underlying technologies that make it possible. Predictive analytics helps you in a number of ways: identify when equipment is likely to fail or need maintenance and take action to maximize uptime and reduce future warranty claims costs; optimize allocated labor resources and spare part inventories, helping eliminate undue maintenance, prevent downtime and reduce inventory costs; and determine why certain production runs fail more often than others, identify the cause and analyze whether those runs warrant a recall.
In this white paper, Forrester discusses the 70 criteria evaluation of customer analytics vendors and discuss 6 of the most significant software providers, one including IBM, and scored them. Read on to learn how each vendor fulfills each key point.
The solution to operationalizing analytic s involves the effective combination of a Decision Management approach with a robust, modern analytic technology platform. This paper discusses both how to use a focus on decisions to ensure the right problem gets solved and what such an analytic technology platform looks like.
Knowing the potential benefits of cloud computing, IBM implemented this technology for six workloads—development and test, analytics, storage, collaboration, desktop and production application. And the results have yielded significant financial and operational improvements. IBM shares some key insights through this report, learned after a successful move to cloud, including different ways in which each workload benefited. It also reveals how adopting cloud enabled significant improvements in efficiency, innovation, service and support levels. Download: Success in the cloud: Why workload matters.
Watch to learn how an enterprise-grade, multi-tenant solution can help you deploy Spark in a production environment to take advantage of
· Faster time-to-results for big data analytics
· Simplified deployment and management
· Increased utilization of hardware resources"
Your goal is clear—produce high-quality goods while optimizing resources at every step of production. And in today's uncertain economy, cost-control efforts may never have been more important. Unscheduled downtime because of equipment failure can have a serious impact on your organization's bottom line. Download this white paper from IBM, and learn the basics of predictive maintenance, the benefits it provides manufacturing operations and the underlying technologies that make it possible. Predictive analytics helps you in a number of ways: identify when equipment is likely to fail or need maintenance and take action to maximize up time and reduce future warranty claims costs; optimize allocated labor resources and spare part inventories, helping eliminate undue maintenance, prevent downtime and reduce inventory costs; and determine why certain production runs fail more often than others, identify the cause and analyze whether those runs warrant a recall.
To operate effectively in different markets, Finnish manufacturer Meka Pro needed to be able to adjust its pricing and manufacturing strategies to each locality, but had no insight into their own data. Read this case study to learn how Meka Pro used IBM® Cognos® software modules to make faster, better business decisions and improve profitability.
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
Companies are trying to improve efficiencies and performance of many real-time operational business practices, including customer experiences, inventory & purchasing, manufacturing yield, BAM and BPM. This white paper explores strategies and capabilities that best-in-class companies are employing to improve operational performance.
To survive and thrive in an era of accelerating digital
disruption, organizations require accessible data,
actionable insights, continuous innovation, and
disruptive business models. It’s no longer enough to
prioritize and implement analytics – leaders are being
challenged to stop doing analytics just for analytics’
sake and focus on defined business outcomes.
In addition, these leaders are being challenged to
bring predictive capabilities and even prescriptive
recommended actions into production at scale. As AI
and accelerated growth and transformation become
top of mind, many enterprises are realizing that their
current segmented analytics approach isn’t built to last,
and that real transformation will require proper endto-
end data management, data security, and a data
processing platform company-wide. The year 2019 will
be a turning point for many organizations that realize
being data-driven doesn’t guarantee future success.
Published By: Datastax
Published Date: Aug 15, 2018
Built on a production-certified version of Apache Spark™ and with integrated search and graph capabilities, DSE Analytics provides highly available, production-ready analytics that enables enterprises to securely build instantly responsive, contextual, always-on applications and generate ad-hoc reports. Read this white paper to learn about the specific features and capabilities of DSE Analytics, and why DSE Analytics is designed for the Right-Now Enterprise.