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Published By: Stardog Union     Published Date: Jul 27, 2018
When enterprises consider the benefits of data analysis, what's often overlooked is the challenge of data variety, and that most successful outcomes are driven by it. Yet businesses are still struggling with how to query distributed, heterogeneous data using a unified data model. Fortunately, Knowledge Graphs provide a schema flexible solution based on modular, extensible data models that evolve over time to create a truly unified solution. How is this possible? Download and discover: • Why businesses should organize information using nodes and edges instead of rows, columns and tables • Why schema free and schema rigid solutions eventually prove to be impractical • The three categories of data diversity including semantic and structural variety
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Stardog Union
Published By: Bitwise     Published Date: Apr 30, 2018
Organizations that adopt an enterprise data lake model for real-time, self-service and advanced analytics require a fresh approach and outlook to develop a Data Governance strategy as Hadoop changes the way that organizations ingest and store data, as well as how business partners access and use data. This paper outlines pillars for Hadoop Data Governance and Security that provide a framework that can be applied to any company.
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Bitwise
Published By: Trillium Software     Published Date: Oct 26, 2015
Acting Quickly – Or Not at All The pace of business is accelerating. Enterprises must do more things, do them more quickly – and then adjust to market and competitive forces and do them differently. They must adapt in order to remain differentiated, and with that differentiation, hopefully build and sustain competitive advantage.
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Trillium Software
Published By: DATAVERSITY     Published Date: Jul 06, 2015
The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across-the-board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. The time-honored techniques for data modeling are being rewritten, reworked, and modified in a multitude of different ways, often wholly dependent on the NoSQL platform under development. The research report analyzes a 2015 DATAVERSITY® survey titled “Modeling NoSQL.” The survey examined a number of crucial issues within the NoSQL world today, with focus on data modeling in particular.
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DATAVERSITY
Published By: MarkLogic     Published Date: Jun 17, 2015
Modern enterprises face increasing pressure to deliver business value through technological innovation that leverages all available data. At the same time, those enterprises need to reduce expenses to stay competitive, deliver results faster to respond to market demands, use real-time analytics so users can make informed decisions, and develop new applications with enhanced developer productivity. All of these factors put big data at the top of the agenda. Unfortunately, the promise of big data has often failed to deliver. With the growing volumes of unstructured and multi-structured data flooding into our data centers, the relational databases that enterprises have relied on for the last 40-years are now too limiting and inflexible. New-generation NoSQL (“Not Only SQL”) databases have gained popularity because they are ideally suited to deal with the volume, velocity, and variety of data that businesses and governments handle today.
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data, data management, databse, marklogic, column store, wide column store, nosql
    
MarkLogic
Published By: TopQuadrant     Published Date: Mar 21, 2015
Data management is becoming more and more central to the business model of enterprises. The time when data was looked at as little more than the byproduct of automation is long gone, and today we see enterprises vigorously engaged in trying to unlock maximum value from their data, even to the extent of directly monetizing it. Yet, many of these efforts are hampered by immature data governance and management practices stemming from a legacy that did not pay much attention to data. Part of this problem is a failure to understand that there are different types of data, and each type of data has its own special characteristics, challenges and concerns. Reference data is a special type of data. It is essentially codes whose basic job is to turn other data into meaningful business information and to provide an informational context for the wider world in which the enterprise functions. This paper discusses the challenges associated with implementing a reference data management solution and the essential components of any vision for the governance and management of reference data. It covers the following topics in some detail: · What is reference data? · Why is reference data management important? · What are the challenges of reference data management? · What are some best practices for the governance and management of reference data? · What capabilities should you look for in a reference data solution?
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data management, data, reference data, reference data management, top quadrant, malcolm chisholm
    
TopQuadrant
Published By: TopQuadrant     Published Date: Jun 01, 2017
This paper presents a practitioner informed roadmap intended to assist enterprises in maturing their Enterprise Information Management (EIM) practices, with a specific focus on improving Reference Data Management (RDM). Reference data is found in every application used by an enterprise including back-end systems, front-end commerce applications, data exchange formats, and in outsourced, hosted systems, big data platforms, and data warehouses. It can easily be 20–50% of the tables in a data store. And the values are used throughout the transactional and mastered data sets to make the system internally consistent.
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TopQuadrant
Published By: CA Technologies     Published Date: Apr 24, 2013
Using ERwin Data Modeler & Microsoft SQL Azure to Move Data to the Cloud within the DaaS Lifecycle by Nuccio Piscopo Cloud computing is one of the major growth areas in the world of IT. This article provides an analysis of how to apply the DaaS (Database as a Service) lifecycle working with ERwin and the SQL Azure platform. It should help enterprises to obtain the benefits of DaaS and take advantage of its potential for improvement and transformation of data models in the Cloud. The use case introduced identifies key actions, requirements and practices that can support activities to help formulate a plan for successfully moving data to the Cloud.
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CA Technologies
Published By: AT&T     Published Date: Sep 11, 2014
The age of Big Data is upon us. Storage costs are going down, and data analytics is becoming more capable and more user-friendly. Even your auto mechanic will be storing a petabyte of data soon. Big Data will give businesses new insights and help improve operations. With these new tools come questions about how to use them. But your mechanic knows more about fixing a transmission than developing a Hadoop cluster, and similar concerns hold true for larger enterprises. Businesses everywhere are looking for guidance.
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AT&T
Published By: iCEDQ     Published Date: Feb 05, 2015
The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.
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data, data management, data warehousing, data quality, etl testing, malcolm chisholm
    
iCEDQ
Published By: Reltio     Published Date: Jan 20, 2017
If you invested in master data management (MDM), you are part of an elite association of those who have been able to afford the time, effort and resources to deploy what has characteristically been a tool, and discipline reserved for only the largest enterprises. Feedback from top industry analysts and companies that transitioned from legacy MDM to modern data management platforms, led to the compilation of a list of 10 warning signs you can use as a handy guide. If one or more of these signs get your attention, it warrants a serious conversation with your current provider about these issues, and how they compare to modern offerings available today.
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Reltio
Published By: Innovative Systems     Published Date: Oct 26, 2017
Even after investing significant time and resources implementing a data quality solution, many enterprises find that their data does not effectively support their goals. This white paper shows how to get the most out of your data quality solution by tailoring it to support your business goals.
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Innovative Systems
Published By: AtomRain     Published Date: Nov 07, 2017
The world is more connected than ever before, and data relationships only continue to multiply. Yet enterprises still operate largely with an incomplete perspective caused by segmented, non-contextual and disconnected data silos. Connected data is the key to surviving, growing and thriving. However, a transformation across the entire enterprise won’t happen overnight, and each step must be measurable from both a business and technical perspective. Organizations need expert guidance to move more swiftly and avoid costly technical pitfalls in the new paradigm. This paper examines the journey to what we call, “The Connected Enterprise”.
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AtomRain
Published By: Datawatch     Published Date: Apr 06, 2018
Enterprises are focusing on becoming ever more data-driven, meaning that it is simply unacceptable to allow data to go to waste. Yet, as the amount of data businesses collect and control continues to increase exponentially, many organizations are failing to derive enough business value from their data. Companies are feeling the pressure to extract maximum value from all of their data, both defensive and offensive. Defensive analytics are the “plumbing aspects” of data management that must be captured to mitigate risk and establish a basic understanding of business performance. Offensive analytics build on defensive analytics and support overarching business objectives, strategic initiatives and long-term goals using predictive models. In this whitepaper, you will learn how to address many challenges, including streamlining operational reporting, delivering insight and providing a single, unified platform for everyone.
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Datawatch
Published By: MapR Technologies     Published Date: Jul 26, 2013
Enterprises are faced with new requirements for data. We now have big data that is different from the structured, cleansed corporate data repositories of the past.Before, we had to plan out structured queries. In the Hadoop world, we don’t have to sort data according to a predetermined schema when we collect it. We can store data as it arrives and decide what to do with it later. Today, there are different ways to analyze data collected in Hadoop—but which one is the best way forward?
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white paper, hadoop, nosql, mapr, mapr technologies
    
MapR Technologies
Published By: Cisco EMEA Tier 3 ABM     Published Date: Jun 01, 2018
What if defenders could see the future? If they knew an attack was coming, they could stop it, or at least mitigate its impact and help ensure what they need to protect most is safe. The fact is, defenders can see what’s on the horizon. Many clues are out there—and obvious. The Cisco 2018 Annual Cybersecurity Report presents our latest security industry advances designed to help organizations and users defend against attacks. We also look at the techniques and strategies that adversaries use to break through those defenses and evade detection. The report also highlights major findings from the Cisco 2018 Security Capabilities Benchmark Study, which examines the security posture of enterprises and their perceptions of their preparedness to defend against attacks.
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Cisco EMEA Tier 3 ABM
Published By: Akamai Technologies     Published Date: Jun 14, 2018
"Traditional remote access technologies—like VPNs, proxies, and remote desktops—provide access in much the same way they did 20 years ago. However, new and growing business realities—like a growing mobile and distributed workforce—are forcing enterprises to take a different approach to address the complexity and security challenges that traditional access technologies present. Read 5 Reasons Enterprises Need a New Access Model to learn about the fundamental changes enterprises need to make when providing access to their private applications."
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vpn, proxies, security, security breach, technology
    
Akamai Technologies
Published By: Akamai Technologies     Published Date: Jun 14, 2018
"High-profile cyber attacks seem to occur almost daily in recent years. Clearly security threats are persistent and growing. While many organizations have adopted a defense-in-depth strategy — utilizing anti-virus protection, firewalls, intruder prevention systems, sandboxing, and secure web gateways — most IT departments still fail to explicitly protect the Domain Name System (DNS). This oversight leaves a massive gap in network defenses. But this infrastructure doesn’t have to be a vulnerability. Solutions that protect recursive DNS (rDNS) can serve as a simple and effective security control point for end users and devices on your network. Read this white paper to learn more about how rDNS is putting your enterprise at risk, why you need a security checkpoint at this infrastructural layer, how rDNS security solutio Read 5 Reasons Enterprises Need a New Access Model to learn about the fundamental changes enterprises need to make when providing access to their private applications.
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rdns, dns, anti-virus, security, network defense
    
Akamai Technologies
Published By: DigiCert     Published Date: Jun 19, 2018
Code signing is at a crossroads. In many enterprises, the traditional approach remains standard procedure: you purchase a code-signing certificate, download it, and deploy it locally for all your code-signing needs. When carefully managed, this approach can still be very effective in guarding against malware. But recent events have shown that many companies are finding it a challenge to manage their code signing certificate deployments—and failures in certificate management can have serious consequences. To discover how our Secure App Service solution can help you manage your codesigning efforts and provide enterprise-level support, all at a predictable cost, download this whitepaper today and find out more.
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DigiCert
Published By: Hewlett Packard Enterprise     Published Date: Jul 12, 2018
"Delivering business transformation is an incredibly complex task for IT. Keeping the lights on while supporting digital transformation initiatives requires a new era of hybrid IT so IT teams can address the needs of not just IT operations staff, but also application developers and LOB executives. Through in-depth interviews of IT operations and LOB staff, IDC shares how enterprises are handling the challenges and opportunities presented by hybrid IT. Find out how you can create value and competitive advantage through digital transformation."
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Hewlett Packard Enterprise
Published By: Butler Technologies     Published Date: Jul 02, 2018
As enterprises become more distributed and migrate applications to the cloud, they will need to diversify their network performance management solutions. In particular, the network operations team will need to complement its packet-based monitoring tools with active test monitoring solutions to enhance visibility into the cloud and help enterprises scale monitoring gracefully and cost-effectively.
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Butler Technologies
Published By: Turbonomic     Published Date: Jul 05, 2018
The hybrid cloud has been heralded as a promising IT operational model enabling enterprises to maintain security and control over the infrastructure on which their applications run. At the same time, it promises to maximize ROI from their local data center and leverage public cloud infrastructure for an occasional demand spike. Public clouds are relatively new in the IT landscape and their adoption has accelerated over the last few years with multiple vendors now offering solutions as well as improved on-ramps for workloads to ease the adoption of a hybrid cloud model. With these advances and the ability to choose between a local data center and multiple public cloud offerings, one fundamental question must still be answered: What, when and where to run workloads to assure performance while maximizing efficiency? In this whitepaper, we explore some of the players in Infrastructure-as-a-Service (IaaS) and hybrid cloud, the challenges surrounding effective implementation, and how to iden
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Turbonomic
Published By: Workday     Published Date: Mar 09, 2018
Workday has been recognized by global market intelligence firm, IDC, as a Leader in the IDC MarketScape for Midmarket ERP applications. This report is a guide highlighting the most important criteria for midsize companies to considers when selecting a system. Being named a Leader also underscores the value that Workday brings customers ranging from mid-sized business to the world’s largest enterprises.
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market intelligence, midsize companies, global intelligence
    
Workday
Published By: IBM     Published Date: May 30, 2018
AI is the future of SIEM - More than 50%of enterprises expect to use SIEM with AI by the end of 2018
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IBM
Published By: IBM     Published Date: Jul 09, 2018
As the information age matures, data has become the most powerful resource enterprises have at their disposal. Businesses have embraced digital transformation, often staking their reputations on insights extracted from collected data. While decision-makers hone in on hot topics like AI and the potential of data to drive businesses into the future, many underestimate the pitfalls of poor data governance. If business decision-makers can’t trust the data within their organization, how can stakeholders and customers know they are in good hands? Information that is not correctly distributed, or abandoned within an IT silo, can prove harmful to the integrity of business decisions. In search of instant analytical insights, businesses often prioritize data access and analysis over governance and quality. However, without ensuring the data is trustworthy, complete and consistent, leaders cannot be confident their decisions are rooted in facts and reality
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IBM
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