The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and the need to rework faulty products.
To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support.
At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
"Considering switching to a single system for finance, planning, and analytics? These leading insurance companies did just that—and they achieved amazing results.
This infographic shows how Workday helped them stay competitive, deliver a customer experience like no other, and ensure compliance as well as:
Save $400,000 annually with better transactional control
Reduce time spent on manual processes, such as quarterly reports
Spend more time analyzing data than gathering it
“EDR alone is simply not enough to empower security pros to detect, investigate, and respond to attacks at the pace they need to keep up with modern attackers. A broader detection and response approach is needed.”
Register now and receive this exclusive white paper. Dave Gruber, ESG Senior Analyst takes a look at how you can increase the efficiency and effectiveness of detection and response through XDR, along with:
• Strategic insight into the current state of threat detection and response, providing you with ESG’s comprehensive research and findings.
• Current challenges affecting today’s organizations, including the time and resources required and numerous gaps that EDR exposes.
• Valuable foresight into what’s next and how XDR—detection and response across email, endpoint, servers, cloud workloads, and network—can help solve these issues.
Published By: Datastax
Published Date: Sep 27, 2019
Smartphones, smart cities, smart homes, smart cars—IoT has triggered a data explosion, and not every enterprise is prepared to handle it.
Beyond collecting and analyzing the increasing volume of data, organizations must figure out how to manage the velocity of that data, as well as how to integrate it with multiple data sources. And that’s just scratching the surface of the IoT challenge. To extract business value out of this inpouring of data, and to take full advantage of IoT boosted by new 5G technology, IT organizations must consider five key technologies.
In this ebook, you’ll learn about these five technologies and their benefits. To continue to develop and scale your IoT-driven applications, your infrastructure needs to be able to handle sensor data at velocity, keep data close to the edge, maintain 100% uptime, and make it easy to extract business value. The insights you’ll discover in this ebook will not only help you prepare your organization for this reality; they’ll also
The SAP HANA platform provides a powerful unified foundation for storing, processing, and analyzing structured and unstructured data. It funs on a single, in-memory database, eliminating data redundancy and speeding up the time for information research and analysis.
The spatial analytics features of the SAP HANA platform can help you supercharge your business with location-specific data. By analyzing geospatial information, much of which is already present in your enterprise data, SAP HANA helps you pinpoint events, resolve boundaries locate customers and visualize routing. Spatial processing functionality is standard with your full-use SAP HANA licenses.
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
Published By: Cisco EMEA
Published Date: Mar 05, 2018
The Cisco® Incident Response team is led by elite security specialists who can uncover the source of threats by analyzing and synthesizing intelligence from multiple sources. These sought-after specialists consistently deliver resolution in a shorter timeframe, returning businesses like yours to normal. Fast.
To find out more about Cisco Incident Response Services download this whitepaper today.
Big Data is not just a big buzzword. Government agencies have been collecting large amounts of data for some time and analyzing the data collected to one degree or another. Big data is a term that describes high volume, variety and velocity of information that inundates an organization on a regular basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and better services.
The Cloud, once a radical idea in IT, is now mainstream. Whether it’s email, backup or file sharing, most consumers probably use a cloud service or two. Similarly, most IT professionals are familiar with cloud service providers such as Amazon, Google and Microsoft Azure, and many companies have moved at least some of their information technology processes into the cloud. In fact, the cloud has become so popular it’s easy to assume that running IT applications on-premises is not cost competitive with a cloud based service. In this report Evaluator Group will test the validity of that assumption with a TCO (Total Cost of Ownership) model analyzing a hyperconverged appliance solution from HPE and a comparable cloud service from Amazon Web Services (AWS).
This white paper can help you confirm that your small business or distributed enterprise needs to invest in an effective next-generation firewalls (NGFW) solution. For small businesses, the
NGFW should provide an affordable and manageable entrée to advanced threat protection. In branch offices and the distributed enterprise, NGFWs should provide a detection and enforcement point, analyzing real-time threats and network traffic at scale and benefiting from an integrated and holistic view of the network of which it is a part. In both use scenarios, the NGFW should help your organization defend against targeted and persistent malware attacks, including emerging threats.
“The biggest problem with big data is using it.”
Accessing, analyzing, and actioning with big data are among the key challenges facing every enterprise. In this detailed report from Forrester, you’ll see key drivers and challenges with Big Data, and some clear recommendations on how to proceed.
Get the white paper to see Forrester's key findings.
Healthcare and Life Sciences organizations are using data to generate knowledge that helps them provide better patient care, enhances biopharma research and development, and streamlines operations across the product innovation and care delivery continuum. Next-Gen business intelligence (BI) solutions can help organizations reduce time-to-insight by aggregating and analyzing structured and unstructured data sets in real or near-real time.
AWS and AWS Partner Network (APN) Partners offer technology solutions to help you gain data-driven insights to improve care, fuel innovation, and enhance business performance.
In this webinar, you’ll hear from APN Partners Deloitte and hc1.com about their solutions, built on AWS, that enable Next-Gen BI in Healthcare and Life Sciences.
Join this webinar to learn:
How Healthcare and Life Sciences organizations are using cloud-based analytics to fuel innovation in patient care and biopharmaceutical product development.
How AWS supports BI solutions f
Published By: Oracle CX
Published Date: Oct 19, 2017
In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions
of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by
the aggressive build-out for cloud computing. Big data and machine learning applications that perform
tasks such as fraud and intrusion detection, trend detection, and click-stream and social media
analysis all require forward-thinking solutions and enough compute power to deliver the performance
required in a rapidly evolving digital marketplace. Companies increasingly need to drive the speed of
business up, and organizations need to support their customers with real-time data. The task of
managing sensitive information while capturing, analyzing, and acting upon massive volumes of data
every hour of every day has become critical.
These challenges have dramatically changed the way that IT systems are architected, provisioned,
and run compared to the past few decades. Most companies
Published By: Oracle CX
Published Date: Oct 20, 2017
Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been
top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar
database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as
quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data
from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure
1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP
database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks
can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data.
In-memory databases have helped address p
The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and no need to rework faulty products. To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support. At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
Forward-thinking enterprises understand what it takes to be successful in this data-rich, increasingly automated economy. According to the Harvard Business Review Analytic Services research report The Rise of Intelligent Automation: TurningComplexity into Profit, sponsored by Oracle, at least 7 in 10 executives understand that predictive analytics (80%) and AI and machine learning (68%) are important for the future of the business.
Even as executives recognize the vital role data plays in their businesses, many are unable to take advantage of the value residing in their data. The old ways of collecting, managing, storing, and analyzing data are no longer effective, and are preventing businesses from extracting potential value. Many simply can’t execute on a data-driven vision.
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and Amazon Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes.
This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
Published By: Teradata
Published Date: Jul 07, 2015
As cyber security challenges continue to grow, new threats are expanding exponentially and with greater sophistication—rendering conventional cyber security defense tactics insufficient. Today’s cyber threats require predictive, multifaceted strategies for analyzing and gaining powerful insights into solutions for mitigating, and putting an end to, the havoc they wreak.
Published By: ServiceNow
Published Date: Oct 24, 2019
Learn how ServiceNow Event Management uses AIOps to dramatically improve business service availability and performance by mapping business services with accurate service context, intelligently analyzing events, integrating existing monitoring and event management tools, and reducing event volumes.
"In today’s IT infrastructure, data security can no longer be treated as an afterthought, because billions of dollars are lost each year to computer intrusions and data exposures. This issue is compounded by the aggressive build-out for cloud computing. Big data and machine learning applications that perform tasks such as fraud and intrusion detection, trend detection, and click-stream and social media analysis all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace.
Companies increasingly need to drive the speed of business up, and organizations need to support their customers with real-time data. The task of managing sensitive information while capturing, analyzing, and acting upon massive volumes of data every hour of every day has become critical.
Published By: Genesys
Published Date: Dec 11, 2013
Gartner recently released its Magic Quadrant for Contact Center Workforce Optimization, the annual report analyzing the workforce optimization industry. Gartner positions vendors based on Completeness of Vision and Ability to Execute.
Genesys improved its 2013 position moving from Niche Player to the Challengers Quadrant. This report provides you with insights you need to determine the Workforce Optimization Solution that is right for your organization. Read Now.
Published By: Genesys
Published Date: Jul 27, 2016
Customer service has been, and will continue to be, a central concern for most companies. Designing and delivering a positive customer experience relies heavily on the framework and capabilities of your call center or contact center, specifically the ability to support omnichannel interactions.
The IDC MarketScape examines 12 key players in the worldwide contact center infrastructure and software (CCIS) market, analyzing their current capabilities as well as longer term strategies that impact their ability to service customers and gain market share going forward.
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs.
Download “The Essential Guide to Infrastructure Machine Data” for:
The benefits of machine data for network, remote, web, cloud and server monitoring
IT infrastructure monitoring data sources to include in your machine data platform
Machine data best practices
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