data analytics

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Published By: QlikTech UK     Published Date: Nov 24, 2015
Organizations are, or will soon be, producing petabytes – thousands of terabytes or millions of gigabytes – of valuable data. But the ability to transform these massive data sets into actionable insights requires business intelligence (BI) and analytics tools that can uncover the hidden relationships among varied sources of information, provide rich visualizations of trends and don’t require end-users to be trained data scientists.
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healthcare
    
QlikTech UK
Published By: Oracle     Published Date: Jan 08, 2018
In the age of the customer, businesses realize the need to take their big data insights further than they have before, in order to win, serve, and retain their customers. Today’s modern company has more data than ever before and is now looking to derive insights from the data that will help propel it forward. As firms move data analytics to the cloud, there is a new set of challenges and barriers to overcome, but with the help of insights-platforms-as-a-service, companies will be able to innovate with data and drive business forward.
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Oracle
Published By: Aberdeen     Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
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aberdeen, michael lock, data-driven decisions, business intelligence, public sector, analytics, federal, state
    
Aberdeen
Published 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.
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reporting, artificial intelligence, insights, organization, institution, recognition
    
Pure Storage
Published By: Pure Storage     Published Date: Jan 12, 2018
Deep learning opens up new worlds of possibility in artificial intelligence, enabled by advances in computational capacity, the explosion in data, and the advent of deep neural networks. But data is evolving quickly and legacy storage systems are not keeping up. Advanced AI applications require a modern all-fl ash storage infrastructure that is built specifically to work with high-powered analytics.
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data, deep learning, automated, intelligence, pure storage
    
Pure Storage
Published By: CA Technologies     Published Date: Jul 20, 2017
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.
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CA Technologies
Published By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
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.
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database, applications, data availability, cognitive applications
    
Group M_IBM Q1'18
Published By: Group M_IBM Q1'18     Published Date: Dec 19, 2017
For increasing numbers of organizations, the new reality for development, deployment and delivery of applications and services is hybrid cloud. Few, if any, organizations are going to move all their strategic workloads to the cloud, but virtually every enterprise is embracing cloud for a wide variety of requirements. To accelerate innovation, improve the IT delivery economic model and reduce risk, organizations need to combine data and experience in a cognitive model that yields deeper and more meaningful insights for smarter decisionmaking. Whether the user needs a data set maintained in house for customer analytics or access to a cloud-based data store for assessing marketing program results — or any other business need — a high-performance, highly available, mixed-load database platform is required.
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cloud, database, hybrid cloud, database platform
    
Group M_IBM Q1'18
Published By: IBM     Published Date: Oct 17, 2017
The data quality tools market continues to show strong revenue growth, driven by cost, process optimization and digital business initiatives. Applying data quality tools to existing and emerging business scenarios will enable data and analytics leaders to deliver greater business value.
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IBM
Published By: IBM     Published Date: Nov 08, 2017
IBM DB2 with BLU Acceleration helps tackle the challenges presented by big data. It delivers analytics at the speed of thought, always-available transactions, future-proof versatility, disaster recovery and streamlined ease-of-use to unlock the value of data.
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ibm, cloud, cloud computing, database, ibm db2
    
IBM
Published By: IBM     Published Date: Nov 08, 2017
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, ibm, deployment models
    
IBM
Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
IBM DB2 with BLU Acceleration helps tackle the challenges presented by big data. It delivers analytics at the speed of thought, always-available transactions, future-proof versatility, disaster recovery and streamlined ease-of-use to unlock the value of data.
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db2. blu accelaration, big data, data analytics, disaster recovery
    
Group M_IBM Q1'18
Published By: Group M_IBM Q1'18     Published Date: Jan 23, 2018
In this paper, you'll learn how organizations are adopting increasingly sophisticated analytics methods, that analytics usage trends are placing new demands on rigid data warehouses, and what's needed is hybrid data warehouse architecture that supports all deployment models.
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data warehouse, analytics, hybrid data warehouse, development model
    
Group M_IBM Q1'18
Published By: Group M_IBM Q1'18     Published Date: Feb 15, 2018
See how you can turn data into actionable insights with predictive analytics. Take our brief assessment to learn which analytical capabilities will enable you to find the greatest value in your data and make confident, accurate business decisions.
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analytics assessment, business decisions, predictive analytics, analytics
    
Group M_IBM Q1'18
Published By: Juniper Networks     Published Date: Feb 05, 2018
Innovative data-driven strategies are enabling organizations to connect with customers and increase operational efficiency as never before. These new initiatives are built on a multitude of applications, such as big-data analytics, supply chain, and factory automation. On average, organizations are now 53% digital as they create new ways of operating and growing their businesses, according to the Computerworld 2017 Forecast Study. As part of this transformation, enterprises rely increasingly on multivendor, multicloud environments that mix on-premise, private, and public cloud services and workloads. This shift is causing enterprises to increase network capacity; 55% of enterprises in the Computerworld study expect to add network bandwidth in the next 12 months.
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security, automation, savings, technology, cloud
    
Juniper Networks
Published By: Oracle     Published Date: Oct 20, 2017
Modern technology initiatives are driving IT infrastructure in a new direction. Big data, social business, mobile applications, the cloud, and real-time analytics all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Customers increasingly drive the speed of business, and organizations need to engage with customers on their terms. The need to manage sensitive information with high levels of security as well as capture, analyze, and act upon massive volumes of data every hour of every day has become critical. These challenges will dramatically change the way that IT systems are designed, funded, and run compared to the past few decades. Databases and Java have become the de facto language in which modern, cloud-ready applications are written. The massive explosion in the volume, variety, and velocity of data increases the need for secure and effective analytics so that organizations can make bette
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty. The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors: Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression. Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
Modern technology initiatives are driving IT infrastructure in a new direction. Big data, social business, mobile applications, the cloud, and real-time analytics all require forward-thinking solutions and enough compute power to deliver the performance required in a rapidly evolving digital marketplace. Customers increasingly drive the speed of business, and organizations need to engage with customers on their terms. The need to manage sensitive information with high levels of security as well as capture, analyze, and act upon massive volumes of data every hour of every day has become critical. These challenges will dramatically change the way that IT systems are designed, funded, and run compared to the past few decades. Databases and Java have become the de facto language in which modern, cloud-ready applications are written. The massive explosion in the volume, variety, and velocity of data increases the need for secure and effective analytics so that organizations can make bette
Tags : 
    
Oracle
Published By: Oracle     Published Date: Oct 20, 2017
Oracle’s new cloud platform, included a new line of servers for cloud and scale-out applications: Oracle’s SPARC S7-2 and S7-2L servers. These servers are based on the breakthrough SPARC S7 processor and extend the outstanding features and capabilities of the SPARC T7 and M7 systems into scale-out form factors. With the combination of Oracle’s breakthrough Software in Silicon features and the efficiency of the SPARC S7 processor we can offer the most secure and economical enterprise clouds with the fastest infrastructure for data analytics. Here at Oracle we recognize our customers’ needs for increasing the security of their data, therefore we have taken security as one of the core values on the SPARC Servers. The new SPARC S7 processor leverages the revolutionary Security in Silicon features introduced on the SPARC T7 and M7 systems. Silicon Secured Memory is a unique hardware implementation that prevents unauthorized access to application data in memory and can prevent hacking explo
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
On Thursday June 30th, we announced Oracle’s new cloud platform, including a new line of servers for cloud and scale-out applications: Oracle’s SPARC S7-2 and S7-2L servers. These servers are based on the breakthrough SPARC S7 processor and extend the outstanding features and capabilities of the SPARC T7 and M7 systems into scale-out form factors. With the combination of Oracle’s breakthrough Software in Silicon features and the efficiency of the SPARC S7 processor we can offer the most secure and economical enterprise clouds with the fastest infrastructure for data analytics. Here at Oracle we recognize our customers’ needs for increasing the security of their data, therefore we have taken security as one of the core values on the SPARC Servers. The new SPARC S7 processor leverages the revolutionary Security in Silicon features introduced on the SPARC T7 and M7 systems. Silicon Secured Memory is a unique hardware implementation that prevents unauthorized access to application data in
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Oracle
Published By: Oracle     Published Date: Oct 20, 2017
On Thursday June 30th, we announced Oracle’s new cloud platform, including a new line of servers for cloud and scale-out applications: Oracle’s SPARC S7-2 and S7-2L servers. These servers are based on the breakthrough SPARC S7 processor and extend the outstanding features and capabilities of the SPARC T7 and M7 systems into scale-out form factors. With the combination of Oracle’s breakthrough Software in Silicon features and the efficiency of the SPARC S7 processor we can offer the most secure and economical enterprise clouds with the fastest infrastructure for data analytics. Here at Oracle we recognize our customers’ needs for increasing the security of their data, therefore we have taken security as one of the core values on the SPARC Servers. The new SPARC S7 processor leverages the revolutionary Security in Silicon features introduced on the SPARC T7 and M7 systems. Silicon Secured Memory is a unique hardware implementation that prevents unauthorized access to application data in
Tags : 
    
Oracle
Published By: Oracle     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
Published By: Oracle     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
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Oracle
Published By: Snowflake     Published Date: Jan 25, 2018
To thrive in today’s world of data, knowing how to manage and derive value from of semi-structured data like JSON is crucial to delivering valuable insight to your organization. One of the key differentiators in Snowflake is the ability to natively ingest semi-structured data such as JSON, store it efficiently and then access it quickly using simple extensions to standard SQL. This eBook will give you a modern approach to produce analytics from JSON data using SQL, easily and affordably.
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Snowflake
Published By: Mindfire     Published Date: May 07, 2010
In this report, results from well over 650 real-life cross-media marketing campaigns across 27 vertical markets are analyzed and compared to industry benchmarks for response rates of static direct mail campaigns, to provide a solid base of actual performance data and information.
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mindfire, response rates, personalized cross-media, marketing campaign, personalization, personalized urls, purls, performance data
    
Mindfire
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