data volume

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Published By: Arcserve     Published Date: May 29, 2015
Today, data volumes are growing exponentially and organizations of every size are struggling to manage what has become a very expensive and complex problem. It causes real issues such as: • Overprovisioning their backup infrastructure to anticipate rapid future growth. • Legacy systems can’t cope and backups take too long or are incomplete. • Companies miss recovery point objectives and recovery time targets. • Backups overload infrastructure and network bandwidth. • Not embracing new technologies, such as cloud backup, because there is too much data to transfer over wide area networks.
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backup infrastructure, legacy systems, overload infrastructure, cloud
    
Arcserve
Published By: SRC,LLC     Published Date: Jun 01, 2009
Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. Learn more today!
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src, data transformation, classification, business value, geographic business intelligence, geo-bi, etl, extract, transform, load, spatial analysis, operational data stores, ods, spatial data, etl, cdi, point of sale, retail floor space management, rfsm, alteryx
    
SRC,LLC
Published By: IBM     Published Date: Jul 06, 2017
Effectively using and managing information has become critical to driving growth in areas such as pursuing new business opportunities, attracting and retaining customers, and streamlining operations. In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before. These needs create a daunting array of workload challenges and place tremendous demands on your underlying IT infrastructure and database systems. In many cases, these systems are no longer up to the task—so it’s time to make a decision. Do you use more staff to keep up with the fixes, patches, add-ons and continual tuning required to make your existing systems meet performance goals, or move to a new database solution so you can assign your staff to new, innovative projects that move your business forward?
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database, growth, big data, it infrastructure, information management
    
IBM
Published By: Dell     Published Date: Sep 26, 2017
Government leaders have the unprecedented opportunity to accelerate the path to IT modernization. Download this whitepaper to learn more about the IT trends driving digital transformations and the considerations to be made in determining a successful strategy toward effective and compliant IT modernization. With the right approach, organizations can create an efficient and future-ready launching pad to agilely, cost-effectively and securely meet the needs of constituents, employees and internal customers; accommodate increasing data volumes; and comply with regulatory requirements.
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Dell
Published By: Mimecast     Published Date: Jul 15, 2019
The Mimecast Supervision solution enables compliance personnel to systematically review and discover targeted data among the volume of communications organizations face today. Integrated with the industry leading Mimecast Cloud Archive, users can facilitate an auditable, managed supervision review process, flexible to meet the needs of the business while utilizing a scaleable, immutable SEC 17A-4 validated and tamper proof archive with guaranteed 7 second SLA search capabilities. To reduce the number of false positives in sampling data, targeted detection rules can focus on specific senders/recipients and to accelerate the process. In addition, queues can be configured with an upper limit upon which to be populated with email. This helps limit the amount of email a reviewer must go through while still identifying risk. Today’s supervision demands require reviewers to be highly productive.
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Mimecast
Published By: Altiscale     Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
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big data, analytics, nexgen, hadoop, apache
    
Altiscale
Published By: Commvault     Published Date: Jul 06, 2016
ENDPOINT DATA. It’s often one of the most forgotten aspects of an enterprise data protection strategy. Yet, content on laptops, desktops and mobile devices is among a company’s most valuable data even while it’s potentially at the greatest risk. According to IDC there will be some 1.3 billion mobile workers by 2015. However, only half of enterprises today are using some type of endpoint backup. That means that the volume of endpoint data that is in jeopardy is nothing short of significant. Download to read the buyer's checklist on endpoint data protection!
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commvault, endpoint data protection, endpoint data, data protection, backup, encryption, data loss prevention, mobile app
    
Commvault
Published By: Commvault     Published Date: Jul 06, 2016
ENDPOINT DATA. It’s often one of the most forgotten aspects of an enterprise data protection strategy. Yet, content on laptops, desktops and mobile devices is among a company’s most valuable data even while it’s potentially at the greatest risk. According to IDC there will be some 1.3 billion mobile workers by 2015.1 However, only half of enterprises today are using some type of endpoint backup.2 That means that the volume of endpoint data that is in jeopardy is nothing short of significant.
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encryption, data loss prevention, mobile app
    
Commvault
Published By: NetApp     Published Date: Nov 13, 2017
To help member hospitals and clinics easily, securely, and affordably protect expanding data volumes, Engage switched to NetApp® AltaVault backup.
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NetApp
Published By: Symantec     Published Date: Jul 11, 2017
Cloud Access Security Brokers (CASBs) serve as a critical control point to ensure the secure and compliant use of cloud apps and services. Cloud service providers typically maintain a shared responsibility policy for security—they guarantee the integrity of their service infrastructure, but the customer is responsible for securing actual app usage. In addition to the growing cloud security challenges organizations face to safeguard data and protect against threats in the cloud, total volume of cloud app adoption is accelerating, with most of it being done by business units and employees without approval or security oversight from the IT organization. As a result, CASB functionality has become so critical that by 2020 it is projected that 80% of enterprises will use a CASB solution. (Gartner)
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cloud, cloud service providers, cloud security, cloud applications
    
Symantec
Published 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
Published By: Group M_IBM Q3'19     Published Date: Jun 27, 2019
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.
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Group M_IBM Q3'19
Published By: IBM     Published Date: Jul 05, 2012
Esri, a U.S. software company, had growing data volumes as they added new applications. System response slowed down and their existing infrastructure had no room for extension. Read the white paper and see how with the IBM Migration Factory helped Esri consolidate 16 physical servers to two, automate storage management and drastically cut costs.
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ibm, technology, sap, sofrware, technology, migration factory, storage
    
IBM
Published By: Forte Wares     Published Date: Jun 24, 2014
The key to making big data initiatives a success lies within making the produced data more digestible and usable in decision making, rather than making it just ‘more,’ resulting in the creation of an environment wherein information is used to generate real impact. Put another way, the survival of Big Data is more about making the right data (not just higher volume) available to the right people (not just higher variety) at the right time (not just higher velocity).
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Forte Wares
Published By: IBM     Published Date: Jul 19, 2016
Data movement and management is a major pain point for organizations operating HPC environments. Whether you are deploying a single cluster, or managing a diverse research facility, you should be taking a data centric approach. As data volumes grow and the cost of compute drops, managing data consumes more of the HPC budget and computational time. The need for Data Centric HPC architectures grows dramatically as research teams pool their resources to purchase more resources and improve overall utilization. Learn more in this white paper about the key considerations when expanding from traditional compute-centric to data-centric HPC.
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ibm, analytics, hpc, big data
    
IBM
Published By: Intel     Published Date: Apr 16, 2019
The data center is coming under immense pressure. The boom in connected devices means increasing volumes of data – and all that needs processing. One way for CSPs to accelerate customer workloads is by using FPGAs, which are easier to use than ever before. Download Intel's latest eGuide, ‘FPGA-as-a-Service: A Guide for CSPs' to discover: • How to add FPGAs to the data center • The structure of the Intel® Acceleration Stack for FPGAs • Adding off-the-shelf accelerator functions • How FPGAs can accelerate many cloud services, such as database as a service and analytics as a service
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Intel
Published By: SAP     Published Date: Feb 03, 2017
In the digital economy, data is becoming more interconnected every day. The volume of highly-connected data is growing rapidly, while also becoming a highly-valued corporate asset. By exploring relationships among people, processes and things, new business opportunities emerge, helping grow your business's competitive advantage.
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SAP
Published By: SAS     Published Date: Apr 20, 2017
Hype and hope — Big Data has generated a lot of both. Thanks to an abundance of enterprise information systems, networks, applications and devices that churn out huge volumes of information, government agencies are awash in Big Data. Add to this data growth the emerging trend of the Internet of Things (IoT) — the network of people, data, things and processes that is increasingly linked through automated connections and sensors — and the future of Big Data can seem quite daunting.
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SAS
Published By: Layered Tech, Inc.     Published Date: Dec 16, 2010
Layered Tech's engineers created a customized package of virtual private data centers (VPDCs), managed services and disaster recovery solutions that support KANA's clients, large and small. Layered Tech tailored the architecture to meet the highest enterprise security requirements, as well as ensuring that each KANA client can deploy applications that scale to ongoing volume fluctuations.
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layered tech, kana, virtual private data center, enterprise security, vpdc, disaster recovery, customer interaction
    
Layered Tech, Inc.
Published By: Dell PC Lifecycle     Published Date: Mar 09, 2018
Compression algorithms reduce the number of bits needed to represent a set of data—the higher the compression ratio, the more space this particular data reduction technique saves. During our OLTP test, the Unity array achieved a compression ratio of 3.2-to-1 on the database volumes, whereas the 3PAR array averaged a 1.3-to-1 ratio. In our data mart loading test, the 3PAR achieved a ratio of 1.4-to-1 on the database volumes, whereas the Unity array got 1.3 to 1.
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Dell PC Lifecycle
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: Feb 24, 2015
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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big data, ibm, claims operations, customer service
    
IBM
Published By: IBM     Published Date: Aug 06, 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, insurance, data, big data, analytics, solutions
    
IBM
Published By: IBM     Published Date: Aug 08, 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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big data, analytics, insurance, customer service, solutions
    
IBM
Published By: IBM     Published Date: Aug 08, 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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big data, analytics, insurance, customer service, solutions, business intelligence, market research, data management/analytics
    
IBM
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