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By: Ricoh     Published Date: Oct 02, 2018
Your business is changing. As a finance leader, you know that accounting is a labour-intensive, costly process where systems often don’t allow for expedient exception handling and many days are fraught with difficulty in matching invoices to other databases for reconciliation. Like most companies, you know where you want to go but may not have infrastructure or internal expertise to handle electronic fund transfers, credit card payments or cheque processing— all the pieces required to make your vision for an efficient, integrated operation a reality.
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     Ricoh
By: Group M_IBM Q418     Published Date: Oct 15, 2018
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data. Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
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     Group M_IBM Q418
By: Group M_IBM Q418     Published Date: Oct 23, 2018
The General Data Protection Regulation (GDPR) framework seeks to create a harmonized data protection framework across the European Union, and aims to give back EU citizens control of their personal data by imposing stricter requirements for those hosting and processing this data, anywhere in the world. IBM is committed to putting data responsibility first and providing solutions that are secure to the core for all customers. As such, IBM Cloud has fully adopted the EU Data Protection Code of Conduct for Cloud Service providers – meaning we agree to meet the entirety of its stringent requirements.
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     Group M_IBM Q418
By: Vision Solutions     Published Date: Feb 18, 2008
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.
Tags : vision, high availability, ibm, aix, cdp, core union
     Vision Solutions
By: SAS     Published Date: Aug 28, 2018
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. To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f
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     SAS
By: SAP     Published Date: Feb 03, 2017
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.
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     SAP
By: Cisco EMEA Tier 3 ABM     Published Date: Nov 13, 2017
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.
Tags : big data, analytics, virtualization, cloudera, ibm, sas, sap, splunk
     Cisco EMEA Tier 3 ABM
By: SAP     Published Date: May 18, 2014
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.
Tags : sap, big data, real time data, in memory technology, data warehousing, analytics, big data analytics, data management
     SAP
By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
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.
Tags : database usage, database management, server usage, data protection
     Hewlett Packard Enterprise
By: Hewlett Packard Enterprise     Published Date: Aug 02, 2017
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.
Tags : cost reduction, oracle database, it operation, online transaction, online analytics
     Hewlett Packard Enterprise
By: Cisco     Published Date: Jun 21, 2016
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.
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     Cisco
By: Amazon Web Services     Published Date: Sep 05, 2018
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making. Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
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     Amazon Web Services
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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     Oracle CX
By: IBM APAC     Published Date: Apr 27, 2018
While relying on x86 servers and Oracle databases to support their stock trading systems, processing rapidly increasing number of transactions fast became a huge challenge for Wanlian Securities. They shifted to IBM FlashSystem that helped them cut average response time for their Oracle Databases from 10 to less than 0.4 milliseconds and improved CPU usage by 15%. Download this case study now.
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     IBM APAC
By: Dome9     Published Date: Apr 25, 2018
As of May 2017, according to a report from The Depository Trust & Clearing Corporation (DTCC), which provides financial transaction and data processing services for the global financial industry, cloud computing has reached a tipping point1. Today, financial services companies can benefit from the capabilities and cost efficiencies of the cloud. In October of 2016, the Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of Currency (OCC) and the Federal Reserve Board (FRB) jointly announced enhanced cyber risk management standards for financial institutions in an Advanced Notice of Proposed Rulemaking (ANPR)2. These proposed standards for enhanced cybersecurity are aimed at protecting the entire financial system, not just the institution. To meet these new standards, financial institutions will require the right cloud-based network security platform for comprehensive security management, verifiable compliance and governance and active protection of customer data
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     Dome9
By: Oracle     Published Date: Aug 09, 2018
The purpose of IT backup and recovery systems is to avoid data loss and recover quickly, thereby minimizing downtime costs. Traditional storage-centric data protection architectures such as Purpose Built Backup Appliances (PBBAs), and the conventional backup and restore processing supporting them, are prone to failure on recovery. This is because the processes, both automated and manual, are too numerous, too complex, and too difficult to test adequately. In turn this leads to unacceptable levels of failure for today’s mission critical applications, and a poor foundation for digital transformation initiatives. Governments are taking notice. Heightened regulatory compliance requirements have implications for data recovery processes and are an unwelcome but timely catalyst for companies to get their recovery houses in order. Onerous malware, such as ransomware and other cyber attacks increase the imperative for organizations to have highly granular recovery mechanisms in place that allow
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     Oracle
By: Dell EMC     Published Date: Nov 09, 2015
This business-oriented white paper summarizes the wide-ranging benefits of the Hadoop platform, highlights common data processing use cases and explores examples of specific use cases in vertical industries. The information presented here draws on the collective experiences of three leaders in the use of Hadoop technologies—Dell and its partners Cloudera and Intel.
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     Dell EMC
By: Dell EMC     Published Date: Oct 08, 2015
Big data can be observed, in a real sense, by computers processing it and often by humans reviewing visualizations created from it. In the past, humans had to reduce the data, often using techniques of statistical sampling, to be able to make sense of it. Now, new big data processing techniques will help us make sense of it without traditional reduction.
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     Dell EMC
By: SAS     Published Date: Apr 16, 2015
Former Intel CEO Andy Grove once coined the phrase, “Technology happens.” As true as Grove’s pat aphorism has become, it’s not always good news. Twenty years ago, no one ever got fired for buying IBM. In the heyday of customer relationship management (CRM), companies bought first and asked questions later. Nowadays, executives are being enlightened by the promise of big data technologies and the role data plays in the fact-based enterprise. Leaders in business and IT alike are waking up to the reality that – despite the hype around platforms and processing speeds – their companies have failed to established sustained processes and skills around data.
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     SAS
By: EMC Converged Platforms     Published Date: Oct 22, 2015
Old Dutch Foods, known for its broad selection of snack foods in the midwest United States and Canada, was struggling to get the right products to the right places at the right time. Its data center included outdated physical servers, and batch processing meant that inventory would not be updated until the end of the day as opposed to real time. In addition, recovering from power outages and disk failures could frequently take up to two weeks. To modernize its data center, Old Dutch Foods invested in EMC Converged Infrastructure. The fast and easy deployment of two VCE VBlock® systems running JD Edwards, MS Exchange, mobile device apps, and operation of a backup site with replicated applications and data. This enhanced the IT department's responsiveness to the business, allowed them to shift to real-time inventory, and reduced CapEx and OpEx costs. Operations were simplified by reducing person-hours needed for infrastructure maintenance by 75 percent.
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     EMC Converged Platforms
By: Adobe     Published Date: Feb 20, 2014
San Diego County District Attorney’s Office accelerates Juvenile Court proceedings using Adobe® Acrobat® Pro in Microsoft SharePoint environment.
Tags : adobe, adobe acrobat pro, file management, software, data management, electronic documentation, paperless processing, pdf documents
     Adobe
By: Adobe     Published Date: Feb 20, 2014
Gain more efficient ways of working with documents and collaborating with others on them.
Tags : adobe, adobe acrobat pro, microsoft applications, collaboration, merging documents, editing documents, pdf to office format, file formatting
     Adobe
By: Oracle     Published Date: Feb 28, 2018
Getting the most from your data, driving innovation, processing orders faster, and reducing operating costs are all essential. And Oracle Exadata is the database platform to deliver these improvements. Read five top reasons for running your business on Oracle Exadata, and find out why other organisations say it was such a good choice for them.
Tags : business, oracle, exadata, organisation, operation
     Oracle
By: This program is brought to you by Oracle and Intel     Published Date: Mar 15, 2018
Getting the most from your data, driving innovation, processing orders faster, and reducing operating costs are all essential. And Oracle Exadata is the database platform to deliver these improvements. Read five top reasons for running your business on Oracle Exadata, and find out why other organisations say it was such a good choice for them.
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     This program is brought to you by Oracle and Intel
By: Pure Storage     Published Date: Jan 12, 2018
Data is growing at amazing rates and will continue this rapid rate of growth. New techniques in data processing and analytics including AI, machine and deep learning allow specially designed applications to not only analyze data but learn from the analysis and make predictions. Computer systems consisting of multi-core CPUs or GPUs using parallel processing and extremely fast networks are required to process the data. However, legacy storage solutions are based on architectures that are decades old, un-scalable and not well suited for the massive concurrency required by machine learning. Legacy storage is becoming a bottleneck in processing big data and a new storage technology is needed to meet data analytics performance needs.
Tags : reporting, artificial intelligence, insights, organization, institution, recognition
     Pure Storage
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