Published By: Vertica
Published Date: Oct 30, 2009
Independent research firm Knowledge Integrity Inc. examine two high performance computing technologies that are transitioning into the mainstream: high performance massively parallel analytical database management systems (ADBMS) and distributed parallel programming paradigms, such as MapReduce, (Hadoop, Pig, and HDFS, etc.). By providing an overview of both concepts and looking at how the two approaches can be used together, they conclude that combining a high performance batch programming and execution model with an high performance analytical database provides significant business benefits for a number of different types of applications.
A comprehensive solution for leveraging data in today's financial industry. Most organizations realize that the key to success lies in how well they manage data—and the banking industry is no exception. From customer statistics to strategic plans to employee communications, financial institutions are constantly juggling endless types of information.
Published By: HP VMware
Published Date: Mar 03, 2008
There are probably as many approaches to data protection and disaster recovery as there are types of disasters that might befall your datacenter. Figuring out which approach is best for your datacenter—from a technical, operational and financial outlook—is enough to keep a responsible manager like you up at night. Download this paper to learn about the proven, flexible solution that prepares your data to survive and recover from almost any calamity, from HP and VMware.
From cars to factories to cities, many governments are already collecting information from citizens and connected devices that send and receive data over the internet of things (IoT). While analysts expect the IoT to soar to tens of billions of devices by 2020, no one knows how many or what new types of intelligent devices will emerge. But we do know that traditional approaches to data management and analytics may not be sufficient for sustaining value in this new, connected world
Every day, people in your organization are struggling to make the right decisions - working with customers, partners, and each other - because they simply can't use all available data. There's too much of it for humans to internalize, understand, and apply in real time. That's why organizations that automate and optimize decisions informed by predictive analytics have a significant advantage over competitors. Read this IBM white paper and discover how decision management can help you effectively leverage your business data to support faster, more-accurate real-time decisions in virtually every area of your business. You can learn the following: the common types of business decisions and how decision management affects them; why most decisions today aren't based on any real business insight; benefits of decision management-what it is and how to adopt it; and the role of predictive analytics in effective decision management.
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.
While many organizations are guarding the front door with yesterday’s signature-based antivirus (AV) solutions, today’s unknown malware walks out the back door with all their data. What’s the answer?
This white paper, “The Rise of Machine Learning in Cybersecurity,” explains machine learning (ML) technology — what it is, how it works and why it offers better protection against the sophisticated attacks that bypass standard security measures. You’ll also learn about CrowdStrike’s exclusive ML technology and how, as part of the Falcon platform’s next-gen AV solution, it dramatically increases your ability to detect attacks that use unknown malware.
Download this white paper to learn:
• How different types of ML are applied in various industries and why it’s such an effective tool against unknown malware
• Why ML technologies differ and what factors can increase the accuracy and effectiveness of ML
• How CrowdStrike’s ML-based technology works as part of the Falcon platform’s next-gene
Learn how fileless techniques work and why they present such a complex challenge.
The arms race between cybersecurity vendors and determined adversaries has never been more heated. As soon as a new security tool is released, threat actors strive to develop a way around it. One advanced threat technique that is experiencing success is the use of fileless attacks, where no executable file is written to disk.
The 2017 Verizon Data Breach Investigations Report found that 51 percent of cyberattacks are malware-free, so there’s no indication that these attacks will be subsiding anytime soon. Read this white paper to get the important information you need to successfully defend your company against stealthy fileless attacks.
Download this white paper to learn:
• The detailed anatomy of a fileless intrusion, including the initial compromise, gaining command and control, escalating privileges and establishing persistence
• How fileless attacks exploit trusted systems — the types of processe
Published By: Ephesoft
Published Date: Jan 18, 2018
Insurance companies are intensely data and document driven. Learn how intelligent document capture solutions can help all types of insurance offerings improve customer service, process claims faster and create streamlined, efficient and profitable business practices.
Today's confidentiality and privacy requirements drive organizations of all sizes and industries to secure sensitive data in email. Often particular types of data need to be encrypted, such as credit card numbers, intellectual property, or client information. Organizations also need to protect confidential emails for particular groups, such as executive management, human resources or legal departments.
Many organizations are turning to policy-based encryption to meet their encryption needs because it automatically encrypts data using content filtering rules that identify types of content or email for particular groups. Encryption is applied when the rules are triggered. With policy-based encryption, organizations avoid relying on individual users to secure important content.
Published By: Jobvite
Published Date: Aug 25, 2016
This explosion in the amount of information is similar to the boom the marketing world saw in the early 2000’s with the introduction of customer data. Now that recruiters finally have access to the same types of information as marketers, it’s becoming more clear that recruiting is marketing. Whereas the marketing world immediately saw the value in this data and latched onto technologies that could leverage the information, the recruiting world is just catching up. The proliferation of CRMs has revolutionized marketing and business development. Creating and maintaining relationships has always been part of a sound business strategy, but that nebulous process has now been operationalized through software like Salesforce. There is a reason the CRM software market has boomed to over $20 billion...it works. Personalizing the process to maintain constant contact and build relationships has been shown to be extremely effective in converting leads to sales.
Organizations that invest in proprietary applications and data for competitive advantage in their industries only succeed by making those investments available to employees, customers, and partners that drive revenue and opportunity. Securing those investments requires a fresh perspective as the types of devices accessing the cloud datacenter are changing rapidly and new workloads, such as VDI desktops, are appearing more regularly alongside server workloads. These changes alter the potential attacks and potential threats to datacenters whose security primarily stands firm at the perimeter, however, within the data center the security is weak.
Gaining the trust of online customers is vital for the success of any company that transfers sensitive data over the Web. When shopping online, consumers are concerned about identity theft and are therefore wary of providing untrusted sources with their personal information, especially their credit card details. Other types of online businesses require different but equally sensitive information. People are reluctant to provide their national insurance numbers, passwords, or other confidential personal information, or sometimes even just name, address, and phone number. Perhaps the information will be intercepted in transit, they fear, or perhaps the destination itself is manned by imposters with ill intent. The result is an abandoned transaction.
This paper will discuss the new IT landscape as it relates to the new integration, and argue that the need for a comprehensive integration strategy has never been more urgent. This strategy must not only take new data types into account, but also address the more fundamental issue of agility – because whatever the integration needs of an enterprise may be at the moment, they are certain to change rapidly.
Published By: Intralinks
Published Date: Mar 12, 2014
The implications of data loss are significant. Organizations that ignore the law affecting collaboration and information sharing are at serious risk of litigation, fines and brand damage. The paradigm shift from organizationally-defined to user-defined information governance is making it that much more difficult to maintain control of business activity and data.
This informative white paper by legal firm Field Fisher Waterhouse evaluates the legal risks of uncontrolled collaboration and information sharing and what to do about them, while providing in-depth insights into:
• Landmark incidents that have influenced data protection laws
• How to navigate different jurisdictional privacy frameworks
• Top 4 types of legal risk to protect against
• Top 5 recommendations for implementing good governance
Being able to monitor and respond to patient inquiries quickly and effectively is critical to creating a positive clinical experience and delivering successful products. But compiling and monitoring this data to address customer concerns in a timely way is a challenge when you have disparate sources and systems, global teams, and multiple patients.
Read how a top 10 global pharmaceutical company worked with Slalom and AWS to design and implement a unified and globally distributed event and inquiry data reporting system. By combining three types of requests into one solution, the company has improved the customer experience and increased call center and data input operational efficiency by 50%.
Learn how to
Increase access to relevant data to help inform future or ongoing clinical trials
Adapt your existing system development processes to an agile approach
Engage with Slalom and AWS throughout the lifecycle of a healthcare engagement
Compliance and governance trends ensure you’re responsibly using your data and that it’s trustable and traceable. Increasingly, your enterprise must know how and by whom its data is handled and what happens to it. Can your enterprise meet these new demands without slowing down the benefits you enjoy from new types and sources of data?
In spite of the growth of virtual business activities performed via the World Wide Web, every business transaction or operation is performed at a physical place. And as handheld GPS devices drive a growing awareness of the concept of "location," people are increasingly looking for operational efficiencies, revenue growth, or more effective management as a result of geographic data services and location-based intelligence. In this white paper, David Loshin, president of Knowledge Integrity, Inc., introduces geographic data services (such as geocoding and proximity matching) and discusses how they are employed in both operational and analytical business applications. The paper also reviews analytical techniques applied across many types of organizations and examines a number of industry-specific usage scenarios.
Published By: Unitrends
Published Date: Aug 18, 2015
When it comes to data protection, what matters most? You want the freedom to choose the types of data, applications, and computing platforms you protect. You want to seal off that data from security threats. You need space to house that data—the right space now with room to grow. You want to support older solutions as you evolve your technology environment to modern strategies, at a pace that suits your business. And you need the ability to reach out for that data at a moment’s notice without overstressing your network. In short, you need it all.
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. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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.
Published By: Tripp Lite
Published Date: Sep 30, 2015
This white paper:
• Explains the staggering growth of digital data volume and the increasing demand for faster access
• Examines the different types of data transmission
• Outlines the two potential solutions for connecting 10Gb equipment with higher-speed equipment
Internet use is trending towards bandwidth-intensive content and an increasing number of attached “things”. At the same time, mobile telecom networks and data networks are converging into a cloud computing architecture. To support needs today and tomorrow, computing power and storage is being inserted out on the network edge in order to lower data transport time and increase availability. Edge computing brings bandwidth-intensive content and latency-sensitive applications closer to the user or data source. This white paper explains the drivers of edge computing and explores the various types of edge computing available.
In the broadening data center cost-saving and energy efficiency discussion, data center physical infrastructure preventive maintenance (PM) is sometimes neglected as an important tool for controlling TCO and downtime. PM is performed specifically to prevent faults from occurring. IT and facilities managers can improve systems uptime through a better understanding of PM best practices. This white paper describes the types of PM services that can help safeguard the uptime of data centers and IT equipment rooms. Various PM methodologies and approaches are discussed. Recommended practices are suggested.
Many of the mysteries of equipment failure, downtime, software and data corruption, are the result of a problematic supply of power. There is also a common problem with describing power problems in a standard way. This white paper will describe the most common types of power disturbances, what can cause them, what they can do to your critical equipment, and how to safeguard your equipment, using the IEEE standards for describing power quality problems.