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data integration

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Published By: IBM     Published Date: May 27, 2014
Big data and analytics help insurance companies identify the next best action for customers. With the right solutions, companies can extract, integrate and analyze a large volume and variety of data, from call-center notes and voice recordings to web chats, telematics and social media
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ibm, big data, analytics, insurance, insurance industry, big data solutions, integration, risk assessment, policy rates, customer retention, claims data, transaction data
    
IBM
Published By: IBM     Published Date: May 27, 2014
If insurers want to succeed in today's digital world, they need to create experiences and business models that are orchestrated, symbiotic, contextual and cognitive.
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ibm, big data, insurance, digital insurer, business model, insurance industry, technology, integration, social business
    
IBM
Published By: IBM     Published Date: Nov 12, 2015
Hear from the University of Michigan Health System’s CMIO, Dr. Andrew Rosenberg, to learn how this institution is achieving their goal for internal/external operability in support of their enterprise analytics roadmap to support its clinical, research, education and administrative missions. Learn more about the specific challenge's that were solved, how they integrated systems of record with medical devices, and hear about their plans for future integration.
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ibm, big data, enterprise analytics, emr, research interfaces
    
IBM
Published By: SAS     Published Date: Mar 06, 2018
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.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
The Internet of Things (IoT) is rapidly emerging as a core transformational technology of the digital era. The ability to gather data from sensors embedded throughout an enterprise can drive insights and operational efficiencies from the supply chain to the customer. But IoT and Industrial IoT (IIoT) implementations require high degrees of IT/OT convergence - collaboration and integration between information technology and operational technology groups - to succeed. These two groups, however, often have different goals, performance metrics, and perspectives on both the collaboration and the outcome. This SAS/HPE-sponsored paper helps readers get a better understanding of the relationship, either real or perceived, between these two groups. Futurum Research surveyed the state of the relationship between IT and OT teams as it pertains to the design, implementation, and creation of value through IoT technologies.
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SAS
Published By: IBM     Published Date: Jul 27, 2015
Read this issue to learn how to tap into the massive amounts of data and information across your organization.
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cloud-based data management, sustainability energy, future of energy, data analytics, utility analytics, grid optimization, advanced analytics integration, cloud-based services
    
IBM
Published By: GlobalSCAPE     Published Date: Jan 08, 2008
AON’s Human Capital division needed a better way to automate their data transfer processes. They wanted a simple yet flexible system that didn’t require customization to adapt to growing needs.  They wanted to consolidate their sensitive data transfers and ensure the security of this data and to provide integration with current workflow and key legacy applications. AON deployed the GlobalSCAPE EFT (Enhanced File Transfer) server.
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file transfer, file transmission, eft, mft, secure file transfer, data transfer, data transmission, globalscape
    
GlobalSCAPE
Published By: Numara Software     Published Date: Jul 16, 2009
Delivering world class customer service in today’s fast paced business environment is becoming more difficult as information and data requires instant access.
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footprints, numara, blackberry, mobile, service desk, on-the-go, slas, service level agreements, smartphone, off-line synchronization, critical data, projects, surveys, workflow, service desk, tracking, billing, accounts receivable, automated integration, automated tools
    
Numara Software
Published By: SAS     Published Date: Sep 08, 2010
This paper describes five business analytics styles used today and the building blocks required in implementing these styles. It is important to consider which of these styles is valid for your organization now and into the future.
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sas, reporting, data warehouses, business activity monitoring, data integration
    
SAS
Published By: SAS     Published Date: Sep 09, 2010
This white paper identifies the key issues that must be addressed and provides a clear strategy for solving them.
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sas, business analytics, productivity, operational application, data integration, reporting
    
SAS
Published By: Pentaho     Published Date: Jan 16, 2015
Download if you need to make decisions about the architecture of the systems you work on. Sponsored by Pentaho.
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embedded, big data, nosql, hadoop, customer analytics, data integration, data delivery, data center
    
Pentaho
Published By: Esker     Published Date: Dec 08, 2014
In today’s increasingly high-tech and efficiency driven business landscape, internal processes such as accounts payable (AP) are being looked to as prime candidates for modernization. Traditional, paper-based methods of vendor invoice processing are associated with higher costs, lower visibility and longer processing times — all barriers that ultimately impede business progress and the ability to gain a competitive advantage. The purpose of this white paper is to explore the specific challenges faced by companies using manual processing methods while shedding light on the key features and proven benefits of automation. Is it time to modernize your AP process? Read this white paper to get started!
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automation, operations, accounts payable, manual inefficiencies, ap workflow, cost, erp system, data recognition, applications, invoice processing, integration
    
Esker
Published By: Intel Corp.     Published Date: Aug 08, 2012
This report describes how improving the efficiency of data storage, deduplication solutions has enabled organizations to cost-justify the increased use of disk for backup and recovery. However, the changing demands on IT storage infrastructures have begun to strain the capabilities of initial deduplication products. To meet these demands, a new generation of deduplication solutions is emerging which scale easily, offer improved performance and availability and simplify management and integration within the IT storage infrastructure. HP refers to this new generation as "Deduplication 2.0.
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assessing, deduplication, 2.0, second, generation, storeonce, deduplication, appliance, efficiency, data, storage, backup, storage, bandwidth management, grid computing, infrastructure, internetworking hardware, server hardware, servers, small business networks
    
Intel Corp.
Published By: K2     Published Date: Jan 07, 2016
We’re experiencing a data explosion. By 2020 the data we create and copy annually will reach 44 trillion gigabytes.1 As this data disseminates into the workplace, companies add hardware and software systems to store, protect and manage it all. While data can help solve business problems, data is most helpful when it reaches those who can use it. Companies need to build Microsoft SharePoint apps that integrate with other line-of-business (LOB) systems so users can take full advantage of this data explosion. But the reality of creating apps that integrate data across organizational systems presents an intimidating challenge. To integrate multiple isolated data sources into an application that transforms data into useful information requires technical expertise, coding and ongoing maintenance. Integration also raises security and governance concerns. So it’s no surprise that only 47 percent of SharePoint users have connected their SharePoint apps with other systems.2 As a result, nearly
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microsoft sharepoint, data integration, security, sharepoint solutions, networking
    
K2
Published By: Eaton     Published Date: Mar 07, 2016
In their search for top-of-the-class uninterruptible power systems (UPSs) for their demanding data center environment, WUSD wanted a system that would keep everything backed up with enough runtime for a graceful shutdown, but was also easily scalable. Equally important, the district wanted seamless integration with their virtual environment. Find out how the Eaton 9390 UPS and the Eaton Intelligent Power Software (IPS) Suite made the grade.
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upss, uninterruptible power systems, data center environment, backup, shutdown, scalability, integration
    
Eaton
Published By: IBM     Published Date: May 28, 2014
Read the whitepaper to find out how one client improved business value of their data by implementing InfoSphere Optim processes and technologies.
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ibm, data lifecycle management, infosphere optim, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence, virtualize data, lifecycle management
    
IBM
Published By: IBM     Published Date: May 28, 2014
The right test data management solution accelerates time to value for business-critical applications and builds relationships and efficiencies across the organization. IBM InfoSphere Optim Test Data Management closes the gap between DBAs and application developers by providing all teams with accurate, appropriately masked and protected data for their work. Developers can confirm that new application functionalities perform as expected. QA staff can validate that the application performs as intended based on the test cases, and that integrations work properly. And business leaders can be more confident that competitive functionality will be delivered on time with less risk.
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ibm, ibm infosphere, ibm optim solutions, ibm test data, test data management, dba, database management, big data, applications, business critical applications, integrations, data center
    
IBM
Published By: IBM     Published Date: May 28, 2014
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
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ibm, data retention, information governance, archiving, historical data, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence
    
IBM
Published By: IBM     Published Date: Aug 05, 2014
The MDM of customer data solutions market segment grew healthily in 2012. New acquisitions and integrations of prior acquisitions by the Leaders have continued, and several visions for linking MDM and social data have emerged. This Magic Quadrant will help you find the right vendor for your needs.
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ibm, magic quadrant, master data, data, management, solutions, mdm, vendor
    
IBM
Published By: IBM     Published Date: Aug 05, 2014
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three V’s of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats. Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
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ibm, data, big data, information, healthcare, governance, technology
    
IBM
Published By: IBM     Published Date: Oct 01, 2014
Gaining a more complete, trusted view of customers is a strategic goal of most organizations. The challenge is that information about customers is typically managed and stored in many different applications, management systems and data silos. And this challenge is often compounded by a lack of consistency from one application to the next. The combination of IBM InfoSphere Data Explorer and IBM InfoSphere Master Data Management addresses these challenges by creating a single, combined, trusted 360-degree view of all data related to customers, accounts, products and other entities. The combined solution enables organizations to gain a deeper understanding of customer sentiment, increase customer loyalty and satisfaction, and get the right information to the right people to provide customers what they need to solve problems, cross-sell and up-sell.
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data integration, customer loyalty, customer service, big data
    
IBM
Published By: IBM     Published Date: Oct 01, 2014
Salesforce.com is an industry leading cloud CRM solution which helps organizations streamline and effectively manage sales processes, customers and opportunities. The effectiveness of these initiatives can be improved dramatically by providing Salesforce with a 3600 view of the Customer to overcome the limitation of fragmented data that Salesforce currently relies on. This paper discusses how InfoSphere capabilities can be used to create comprehensive and accurate 3600 views of your customers from internal and external sources. This data is integrated seamlessly within Salesforce.com to help your sales teams get a complete view of the customer to help find the right contacts, allocate resources efficiently and identify new opportunities. This helps your sales teams be more efficient, effective and ultimately improve your win rate and drive more revenue.
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salesforce, crm solution, data integration
    
IBM
Published By: IBM     Published Date: Oct 01, 2014
This white paper discusses how IBM InfoSphere can support the integration and governance of Big Data in healthcare. The white paper reviews three case studies including predictive analytics with Electronic Medical Records, time series data in a neonatal intensive care unit and predictive pathways for disease.
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big data, data integration, healthcare
    
IBM
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