Mainframes continue to provide high business value by combining efficient transaction processing with high-volume access to critical enterprise data. Business organizations are linking mobile devices to mainframe processing and data to support digital applications and drive business transformation. In this rapidly growing scenario, the importance of providing excellent end-user experience becomes critical for business success.This analyst announcement note covers how CA Technologies is addressing the need for providing high availability and a fast response time by optimizing mainframe performance with new machine learning and analytics capabilities.
Published By: Workday
Published Date: Mar 02, 2018
Before Workday, Panera Bread’s payroll processes were manual, inefficient, and error-prone,
and payroll nightmares and compliance risks were a regular occurrence. Complex systems and costly
integrations made it impossible for the company to keep up with its rapid growth or gain valuable
insights into global labor expenses. See the infographic to learn why unifying HR, payroll, time tracking,
and absence management in a single system allows Panera to use one consistent, flexible, and scalable
system across the U.S. and Canada.
As organizations develop next-generation applications for the digital era, many are using cognitive computing ushered in by IBM Watson® technology. Cognitive applications can learn and react to customer preferences, and then use that information to support capabilities such as confidence-weighted outcomes with data transparency, systematic learning and natural language processing.
To make the most of these next-generation applications, you need a next-generation database. It must handle a massive volume of data while delivering high performance to support real-time analytics. At the same time, it must provide data availability for demanding applications, scalability for growth and flexibility for responding to changes.
In this report, Forrester reveals how order management systems (OMS) solve the omni-channel deficiencies of traditional systems, allowing retailers to orchestrate complex order processing scenarios - from the point of capture, through the supply chain, and to the point of fulfillment. Read the report today.
Oracle debuted its Blockchain Cloud Service in October, and now one of Oracle’s early-stage partners, AuraBlocks, has already created a financial service on the platform.
AuraBlocks is using blockchain to help its customer Biz2Credit verify the identity of borrowers. Biz2Credit provides loans to small- and medium-sized businesses.
Published By: Workday
Published Date: Jan 16, 2018
Financial transformation by definition is not something you can bolt on—it requires a willingness to question long-held assumptions and envision where you want to go and a total technology rethink. In the next blog, we’ll take a closer look at how one, unified, cloud-based system can create the perfect environment for finance to handle transaction processing and compliance and control while delivering the answers the business needs.
Published By: IBM APAC
Published Date: Mar 19, 2018
Finnish telecom giant DNA’s vision is to have the most satisfied customers. They achieve this with Flash storage by accelerating daily reports on customer preferences and making agile business decisions accordingly.
Read how they use IBM Flash Storage to cut its report processing by 66%, enabling it to provide the insights it needs to deliver the most relevant and valuable experiences to its subscribers.
Published By: Snowflake
Published Date: Jan 25, 2018
Compared with implementing and managing Hadoop (a traditional on-premises data warehouse) a data warehouse built for the cloud can deliver a multitude of unique benefits. The question is, can enterprises get the processing potential of Hadoop and the best of traditional data warehousing, and still benefit from related emerging technologies?
Read this eBook to see how modern cloud data warehousing presents a dramatically simpler but more power approach than both Hadoop and traditional on-premises or “cloud-washed” data warehouse solutions.
Published By: Veeam '18
Published Date: Mar 13, 2018
Disaster recovery (DR) planning has a reputation for being difficult and time consuming. Setting up alternate processing sites, procuring hardware, establishing data replication, and failover testing have been incredibly expensive undertakings. To top it all off, the need for 24x7x365 business application availability threatens to make disaster recovery planning an exercise in futility.
Written by: IDC Abner Germanow, Jonathan Edwards, Lee Doyle IDC believes the convergence of communications and mainstream IT architectures will drive significant innovation in business processes over the next decade.
Continuous member service is an important deliverable for credit unions, and. the continued growth in assets and members means that the impact of downtime is affecting a larger base and is therefore potentially much more costly. Learn how new data protection and recovery technologies are making a huge impact on downtime for credit unions that depend on AIX-hosted applications.
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated
drive as you read the morning news. We are getting very close to that kind of
scenario, and companies like Ford expect to have production vehicles in the latter part
Driverless cars are just one popular example of machine learning. It’s also used in
countless applications such as predicting fraud, identifying terrorists, recommending
the right products to customers at the right time, and correctly identifying medical
symptoms to prescribe appropriate treatments.
The concept of machine learning has been around for decades. What’s new is that
it can now be applied to huge quantities of data. Cheaper data storage, distributed
processing, more powerful computers and new analytical opportunities have dramatically
increased interest in machine learning systems. Other reasons for the increased
momentum include: maturing capabilities with methods and algorithms refactored to
run in memory; the
There is a lot of excitement in the market about artificial intelligence (AI), machine learning
(ML), and natural language processing (NLP). Although many of these technologies have been
available for decades, new advancements in compute power along with new algorithmic
developments are making these technologies more attractive to early adopter companies. These
organizations are embracing advanced analytics technologies for a number of reasons including
improving operational efficiencies, better understanding behaviors, and gaining competitive
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for
the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and
discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses.
SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
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.
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
In-memory technology—in which entire datasets are pre-loaded into a computer’s random access memory, alleviating the need for shuttling data between memory and disk storage every time a query is initiated—has actually been around for a number of years. However, with the onset of big data, as well as an insatiable thirst for analytics, the industry is taking a second look at this promising approach to speeding up data processing.
In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
What if you could reduce the cost of running Oracle databases and improve database performance at the same time? What would it mean to your enterprise and your IT operations?
Oracle databases play a critical role in many enterprises. They’re the engines that drive critical online transaction (OLTP) and online analytical (OLAP) processing applications, the lifeblood of the business. These databases also create a unique challenge for IT leaders charged with improving productivity and driving new revenue opportunities while simultaneously reducing costs.
The Cisco® Hyperlocation Solution is the industry’s first Wi-Fi network-based location system that can help businesses and users pinpoint a user’s location to within one to three meters, depending on the deployment. Combining innovative RF antenna and module design, faster and more frequent data processing, and a powerful platform for customer engagement, it can help businesses create more personalized and profitable customer experiences.
Published By: OpenText
Published Date: Mar 02, 2017
Watch the video to learn how Procure-to-Pay (P2P) solutions automate B2B processes to help you gain better visibility into transaction lifecycles, improve efficiency, and increase the speed and accuracy of order, shipping, and invoice processing.
Published By: Oracle CX
Published Date: Oct 20, 2017
With the growing size and importance of information stored in today’s
databases, accessing and using the right information at the right time has
become increasingly critical. Real-time access and analysis of operational
data is key to making faster and better business decisions, providing
enterprises with unique competitive advantages. Running analytics on
operational data has been difficult because operational data is stored in row
format, which is best for online transaction processing (OLTP) databases,
while storing data in column format is much better for analytics processing.
Therefore, companies normally have both an operational database with data
in row format and a separate data warehouse with data in column format,
which leads to reliance on “stale data” for business decisions. With Oracle’s
Database In-Memory and Oracle servers based on the SPARC S7 and
SPARC M7 processors companies can now store data in memory in both
row and data formats, and run analytics on their operatio
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