data volume

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Published By: MalwareBytes EMEA     Published Date: May 10, 2019
Enterprises, beware. Threat actors are continuing to eye businesses for high returns on investment in Q1 2019, breaching infrastructure, exfiltrating or holding data hostage, and abusing weak credentials for continued, targeted monitoring. From a steadfast increase of pervasive Trojans, such as Emotet, to a resurgence of ransomware lodged against corporate targets, cybercriminals are going after organizations with a vengeance. Yet every cloud has a silver lining, and for all the additional effort thrown at businesses, consumer threats are now on the decline. Ransomware against consumers has slowed down to a trickle and cryptomining, at a fever pitch against consumers this time last year, has all but died. Interestingly, this has resulted in an overall decline in the volume of malware detections from Q4 2018 to Q1 2019. While threat actors made themselves busy with challenging new victims, they ensnared targets in the old ways, using tried-and-true malspam and social engineering tactic
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MalwareBytes EMEA
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: Group M_IBM Q2'19     Published Date: Apr 03, 2019
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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Group M_IBM Q2'19
Published By: Nice Systems     Published Date: Feb 26, 2019
NICE has made a significant investment into AI and ML techniques that are embedded into its core workforce management solution, NICE WFM. Recent advancements include learning models that find hidden patterns in the historical data used to generate forecasts for volume and work time. NICE WFM also has an AI tool that determines, from a series of more than 40 models, which single model will produce the best results for each work type being forecasted. NICE has also included machine learning in its scheduling processes which are discussed at length in the white paper.
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Nice Systems
Published By: Automation Anywhere     Published Date: Feb 21, 2019
Automation Anywhere’s flagship product is Automation Anywhere Enterprise – a RPA platform offering a variety of tools to help organisations develop, operate and manage RPA bots that automate data entry, data gathering and other repetitive, routine tasks usually carried out as part of high-volume, repetitive work (for example, service fulfilment work in call centres, shared-service centres, and back-office processing environments). Automation Anywhere Enterprise bots can add value both in unattended (server-based, lights-out operation) and attended (desktop-based, interactive) deployment configurations. In this report, MWD Advisors digs deeper into the features and capabilities of Automation Anywhere’s product portfolio, analysing its fast-growth trajectory and highlighting large-scale implementations.
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Automation Anywhere
Published By: HP Inc.     Published Date: Feb 14, 2019
How to overcome complexity and be ready for future growth. The modern production digital print market brought a world of opportunities for micro-runs and variable data printing into the hands of Print Service Providers (PSPs). With print available in any quantity to anyone, print is being democratized, resulting in ever-greater numbers of increasingly smaller jobs. The old print model of high-volume static print is being replaced with a myriad of micro-runs and micro-jobs. Automation that allows PSP owners to master the ensuing complexity and respond faster than ever before is enabling them to refocus on market development and on delivering value.
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HP Inc.
Published By: HERE Technologies     Published Date: Jan 22, 2019
To improve safety and mobility across its 5,600km road network, the City of Toronto forged a partnered with HERE Technologies for the provision of traffic, incident, and historical traffic data. Access to this data allows the city authority to see exactly what’s happening on its roads and more easily and affectively run studies on improvement projects. This case study details how HERE Technologies enabled the City of Toronto’s transportation team to: Work smarter with comprehensive network coverage and accurate data to aid analysis Examine the impact of city projects without significant forward planning or expenditure Ensure travel volume models used to drive decision making are calibrated to represent real-world truths
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public sector, urbanization, traffic management
    
HERE Technologies
Published By: SAS     Published Date: Jan 04, 2019
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment. Newer examples of operational analytics include support for logistics, customer call centers, fraud detection, and recommendation engines to name just a few. Embedding analytics is certainly not new but has been gaining more attention recently as data volumes and the freq
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SAS
Published By: Datastax     Published Date: Dec 27, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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Datastax
Published By: IBM Watson Health     Published Date: Dec 10, 2018
In this case study, large health systems implement IBM Watson Health to surface improvement opportunities. Using this tool, they were able to cut costs, reduce patients’ length of stay, acquire actionable data, address number of readmissions and improve management of COPD and sepsis.
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hospitals, health systems, healthcare, analytics, data, insights, case study, performance improvement, healthcare insights, cost, length of stay, financial performance data, readmissions, quality performance, benchmarks, dashboards, patient volume, savings
    
IBM Watson Health
Published By: HERE Technologies     Published Date: Dec 05, 2018
Car data consumption is critically important to auto manufacturers. As more vehicles are produced with built-in infotainment systems, the cost of supplying them with live information and real-time updates via a data connection grows ever greater. Auto manufacturers need to be able to enhance the driving experience with smart traffic, navigation, and entertainment services, while reducing costs and data volumes. HERE is the world’s leading provider of traffic data to the automotive industry. This eBook shows how HERE optimizes traffic solutions and it outlines how data use modelling, standardization, and good data management can help reduce usage.
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traffic data, auto, location data
    
HERE Technologies
Published By: Resource     Published Date: Dec 04, 2018
What’s a common characteristic of the best talent? They all have jobs. In today’s marketplace, to get the best talent you have to convince them your opportunity is better than what they currently have. The good news: it can be done - and you can win consistently with a deliberate outbound process. The bad news: it requires an intentional approach which is challenging without the right tools and data. Your success depends on your ability to build a repeatable process to identify and recruit a steady volume of high quality applicants. The way to accelerate and scale your outbound process is to benchmark and refine it regularly using data. Below we’ll walk through the steps to building a data-driven recruiting process, based on.. you guessed it.. data.
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Resource
Published By: AWS     Published Date: Nov 28, 2018
Financial institutions run on data: collecting it, analyzing it, delivering meaningful insights, and taking action in real time. As data volumes increase, organizations demand a scalable analytics platform that can meet the needs of data scientists and business users alike. However, managing an on-premises analytics environment for a large and diverse user base can become time-consuming, costly, and unwieldy. Tableau Server on Amazon Web Services (AWS) is helping major Financial Services organizations shift data visualization and analytics workloads to the cloud. The result is fewer hours spent on manual work and more time to ask deeper questions and launch new data analyses, with easily-scalable support for large numbers of users. In this webinar, you’ll hear how one major asset management company made the shift to cloud data visualization with Tableau Server on AWS. Discover lessons learned, best practices tailored to Financial Services organizations, and starting tactics for scalable analytics on the cloud.
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AWS
Published By: Wasabi     Published Date: Nov 13, 2018
As cams and digital video become more critical to law enforcement, their success will be in large part dependent on the ability to efficiently store and access that data. In this case study we look at Novus Insight, a leading integrator of managed video storage for law enforcement agencies, and their decision to use Wasabi Hot Cloud Storage. Novus is tasked with helping law enforcement manage the massive volumes of digital video evidence that body and dash cams are creating. Not a simple proposition for these budget and resource constrained public agencies. After surveying the available options Novus found going with Wasabi an easy choice. Wasabi’s revolutionary storage technology allows for an unprecedented combination of speed, affordability and security. It not only brings Novus the instantly scalable cloud solution they need, it dramatically improves performance and economics at the same time. To learn more about how Novus and Wasabi are helping enable better policing through bette
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Wasabi
Published By: DataStax     Published Date: Nov 02, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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DataStax
Published By: Datastax     Published Date: Nov 02, 2018
Today’s data volume, variety, and velocity has made relational database nearly obsolete for handling certain types of workloads. But it’s also put incredible strain on regular NoSQL databases. The key is to find one that can deliver the infinite scale and high availability required to support high volume, web-scale applications in clustered environments. This white paper details the capabilities and uses case of an Active Everywhere database
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Datastax
Published By: Talend     Published Date: Nov 02, 2018
Siloed data sources, duplicate entries, data breach risk—how can you scale data quality for ingestion and transformation at big data volumes? Data and analytics capabilities are firmly at the top of CEOs’ investment priorities. Whether you need to make the case for data quality to your c-level or you are responsible for implementing it, the Definitive Guide to Data Quality can help. Download the Definitive Guide to learn how to: Stop bad data before it enters your system Create systems and workflow to manage clean data ingestion and transformation at scale Make the case for the right data quality tools for business insight
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Talend
Published By: AWS     Published Date: Oct 26, 2018
Today’s organisations are tasked with analysing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organisations are finding that in order to deliver analytic insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. A data lake is an architectural approach that allows you to store enormous amounts of data in a central location, so it’s readily available to be categorised, processed, analysed, and consumed by diverse groups within an organisation? Since data—structured and unstructured—can be stored as-is, there’s no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
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data, lake, amazon, web, services, aws
    
AWS
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: AWS     Published Date: Oct 12, 2018
Safeguarding your data is more important than ever. In today’s data-driven business landscape, companies are using their data to innovate, inform product improvements, and personalize services for their customers. The sheer volume of data collected for these purposes keeps growing, but the solutions available to organizations for processing and analyzing it become more efficient and intuitive every day. Reaching the right customers at the right time with the right offers has never been easier. With this newfound agility, however, comes new opportunities for vulnerability. With so much riding on the integrity of your data and the services that make it secure and available, it’s crucial to have a plan in place for unexpected events that can wipe out your physical IT environment or otherwise compromise data access. The potential for natural disasters, malicious software attacks, and other unforeseen events necessitates that companies implement a robust disaster recovery (DR) strategy to
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AWS
Published By: Mimecast     Published Date: Oct 11, 2018
Information management is getting harder. Organizations face increasing data volumes, more stringent legal and regulatory record-keeping requirements, stricter privacy rules, increasing threat of breaches and decreasing employee productivity. Companies are also finding that their old-fashioned, legacy archive strategies are increasingly ineffective. This is driving many organizations to rethink their approach, developing more modern Information Governance strategies.
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Mimecast
Published By: Mimecast     Published Date: Oct 11, 2018
Information management is getting harder. Organizations face increasing data volumes, more stringent legal and regulatory record-keeping requirements, stricter privacy rules, increasing threat of breaches and decreasing employee productivity. Companies are also finding that their old-fashioned, legacy archive strategies are increasingly ineffective. This is driving many organizations to rethink their approach, developing more modern Information Governance strategies.
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Mimecast
Published By: AWS - ROI DNA     Published Date: Aug 09, 2018
In today's big data digital world, your organization produces large volumes of data with great velocity. Generating value from this data and guiding decision making require quick capture, analysis and action. Without strategies to turn data into insights, the data loses its value and insights become irrelevant. Real-time data inegration and analytics tools play a crucial role in harnessing your data so you can enable business and IT stakeholders to make evidence-based decisions
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AWS - ROI DNA
Published By: EverString     Published Date: Jul 27, 2018
You have lots of leads, but finding the right match is getting harder and harder. You know the good leads are out there. They might even be looking for you. The trouble is, those good leads are hidden among so many bad ones. Your sales and marketing teams know this. They’re facing a deluge of data, not all of it reliable, and they’re trying to decode that data using outdated point solutions and imperfect criteria. Which means a lot of time spent running campaigns, initiating conversations and booking meetings that go nowhere. If this were the dating world, we’d be talking about a calendar full of bad first dates. Everyone’s “swiping right,” but no one’s truly matching. No one’s getting a second date. No one’s committing. Something just isn’t working. We can’t help you if you’re dating. But if you’re trying to sell your company’s products and services—well, the game is about to change. Instead of moving through a volume of potential customers as quickly as possible to build pipeline, th
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EverString
Published By: Amazon Web Services     Published Date: Jul 25, 2018
What is a Data Lake? Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lakes are a new and increasingly popular way to store and analyze data that addresses many of these challenges. A Data Lakes allows an organization to store all of their data, structured and unstructured, in one, centralized repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. Download to find out more now.
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Amazon Web Services
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