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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: Denodo     Published Date: Feb 07, 2019
With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources. Data virtualization transcends the limitations of traditional data integration techniques such as ETL by delivering a simplified, unified, and integrated view of trusted business data. Learn how you can: • Conquer siloed data in the enterprise • Integrate all data sources and types • Cope with regulatory requirements • Deliver big data solutions that work • Take the pain out of cloud adoption • Drive digital transformation
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Denodo
Published By: Tamr, Inc.     Published Date: Feb 08, 2019
Traditional data management practices, such as master data management (MDM), have been around for decades – as have the approaches vendors take in developing these capabilities. And they were well-equipped for the problem at hand: managing data at modest size and complexity. However, as enterprises mature and start to view their data assets as a source of competitive advantage, new methods to managing enterprise data become desirable. Enterprises now need approaches to data management that can solve critical issues around speed and scale in an increasingly complex data environment. This paper explores how data curation technology can be used to solve data mastering challenges at scale.
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Tamr, Inc.
Published By: DATAVERSITY     Published Date: Jun 14, 2013
This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. “Data Governance” is a term fraught with nuance, misunderstanding, myriad opinions, and fear. It is often enough to keep Data Stewards and senior executives awake late into the night. The modern enterprise needs reliable and sustainable control over its technological systems, business processes, and data assets. Such control is tantamount to competitive success in an ever-changing marketplace driven by the exponential growth of data, mobile computing, social networking, the need for real-time analytics and reporting mechanisms, and increasing regulatory compliance requirements. Data Governance can enhance and buttress (or resuscitate, if needed) the strategic and tactical business drivers every enterprise needs for market success. This paper is sponsored by: ASG, DGPO and DebTech International.
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data, data management, data governance, data steward, dataversity, research paper
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Nov 11, 2013
This report investigates the level of Information Architecture (IA) implementation and usage at the enterprise level. The primary support for the report is an analysis of a 2013 DATAVERSITY™ survey on Data and Information Architecture. This paper is sponsored by: HP, Vertica, Denodo, Embarcadero and CA Technologies.
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information architecture, data architecture, white paper, mdm, master data management, data, data management, enterprise information management, enterprise data management, data virtualization, metadata, data modeling, research paper, survey, data integration
    
DATAVERSITY
Published By: Alation     Published Date: Jan 06, 2017
90% of the time that is spent creating new reports is recreating information that already exists. Without a way to effectively share prior work and identify verified data sources, analysts and other data consumers lack shared context on how to apply data to analytic inquiries and business decision making. Time is wasted tracking down subject matter experts and trying to unearth tribal knowledge. Leading analytic organizations in retail, healthcare, financial services and technology are using data catalogs to help their analysts find, understand and use data appropriately. What are the 5 critical capabilities of a data catalog? Learn more here:
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Alation
Published By: Alation     Published Date: Jan 06, 2017
90% of the time that is spent creating new reports is recreating information that already exists. Without a way to effectively share prior work and identify verified data sources, analysts and other data consumers lack shared context on how to apply data to analytic inquiries and business decision making. Time is wasted tracking down subject matter experts and trying to unearth tribal knowledge. Leading analytic organizations in retail, healthcare, financial services and technology are using data catalogs to help their analysts find, understand and use data appropriately. What are the 5 critical capabilities of a data catalog? Learn more here:
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Alation
Published By: MapR Technologies     Published Date: Mar 29, 2016
Add Big Data Technologies to Get More Value from Your Stack Taking advantage of big data starts with understanding how to optimize and augment your existing infrastructure. Relational databases have endured for a reason – they fit well with the types of data that organizations use to run their business. These types of data in business applications such as ERP, CRM, EPM, etc., are not fundamentally changing, which suggests that relational databases will continue to play a foundational role in enterprise architectures for the foreseeable future. One area where emerging technologies can complement relational database technologies is big data. With the rapidly growing volumes of data, along with the many new sources of data, organizations look for ways to relieve pressure from their existing systems. That’s where Hadoop and NoSQL come in.
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MapR Technologies
Published By: Neo Technology     Published Date: Jun 28, 2015
The future of Master Data Management is deriving value from data relationships which reveal more data stories that become more and more important to competitive advantage as we enter into the future of data and business analytics. MDM will be about supplying consistent, meaningful views of master data and being able to unify data into one location, especially to optimize for query performance and data fit. Graph databases offer exactly that type of data/performance fit. Use data relationships to unlock real business value in MDM: - Graphs can easily model both hierarchical and non-hierarchical master data - The logical model IS the physical model making it easier for business users to visualize data relationships - Deliver insights in real-time from data relationships in your master data - Stay ahead of the business with faster development Download and read the white paper Your Master Data Is a Graph: Are You Ready? to learn why your master data is a graph and how graph databases like Neo4j are the best technologies for MDM.
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database, nosql, graph database, big data, master data management, mdm
    
Neo Technology
Published By: Neo Technology     Published Date: Feb 15, 2018
By itself, data offers finite value. But when connected, its value is infinite. Discover how enterprise organizations such as Airbnb, eBay and Telia used connected data and graph technology in order to create a sustainable competitive advantage. This white paper shows business leaders how to take advantage of data relationships with graph technology.
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Neo Technology
Published By: Reltio     Published Date: May 22, 2018
"Forrester's research uncovered a market in which Reltio [and other companies] lead the pack,” the Forrester Wave Master Data Management states. "Leaders demonstrated extensive and MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.” Reltio executes the vision for next-generation MDM by converging trusted data management with business insight solutions at scale and in the cloud. Machine learning and graph technology capabilities enable a contextual data model while also maintaining temporal and lineage changes of the master data.
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Reltio
Published By: Silwood Technology     Published Date: Mar 02, 2016
Ever since organisations started to implement packaged software solutions to solve business problems and streamline their processes there has been a need to access their data for the purposes of reporting and analytics, integration, governance, master data and more. Information Management projects such as these rely on data professionals being able to understand the underlying data models for these packages in order to be able to answer the critical question “Where’s the data?”. Without this knowledge it is impossible to ensure accuracy of data or timely delivery of projects. In addition the lack of discovery tools designed to meet this challenge has meant that performing this task has commonly been frustrating, time-consuming and fraught with risk. This white paper offers insight into why the traditional methods are not effective and how an innovative software product from Silwood Technology provides a faster and more effective approach to solving the problem.
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Silwood Technology
Published By: Looker     Published Date: Mar 15, 2016
Data centralization merges different data streams into a common source through unified variables. This process can provide context to overly-broad metrics and enable cross-platform analytics to guide better business decisions. Investments in analytics tools are now paying back a 13.01:1 return on investment (ROI), with increased returns when these tools integrate with three or more data sour- ces. While the perks of centralization are obvious in theory, the quantity and variety of data available in today’s landscape make this difficult to achieve. This report provides a roadmap for how to connect systems, data stores, and institutions (both technological and human). Learn: • How data centralization enables better analytics • How to redefine data as a vehicle for change • How the right BI tool eliminates the data analyst bottleneck • How to define single sources of truth for your organization • How to build a data-driven (not just data-rich) organization
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Looker
Published By: Syncsort     Published Date: Jan 04, 2018
The term Big Data doesn’t seem quite “big enough” anymore to properly describe the vast over-abundance of data available to organizations today. As the volume and variety of Big Data sources continue to grow, the level of trust in that data remains troublingly low. Read on and discover how a strong focus on data quality spanning the people, processes and technology of your organization will help keep your data lake pristine.
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Syncsort
Published By: R2C     Published Date: Jan 05, 2018
Consistent sharing of data across organizational boundaries is often hampered by a lack of transparency, visibility, and trust in the agreements made between parties who seek to share data assets. How does an organization with cultural barriers to sharing data assets engender trust in the process? Leveraging blockchain technology that “oraclizes” data sharing agreements may provide an answer.
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R2C
Published By: Attivio     Published Date: Mar 14, 2018
Did you ever consider all of the examples of machine learning in your personal life? Google’s page ranking system, photo tagging on Facebook, and customized product recommendations from Amazon are all driven by machine learning under the hood. How do these same techniques improve productivity for your business? Search is the new data and content curation. Improved relevance translates to faster search results and better business outcomes across the line. Download the Five-Minute Guide to Machine Learning to find out how self-learning technologies drive increasingly relevant answers and better context for cognitive search.
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Attivio
Published By: Converseon     Published Date: Apr 02, 2018
Separating signals from noisy social listening data has long been a problem for data scientists. Poor precision due to slag, sarcasm and implicit meaning has often made it too challenging to effectively model. Today, however, new approaches that leverage active machine learning are rapidly over taking aging rules-based techniques and opening up use of this data in new and important ways. This paper provides some detail on the evolution of text analysis including current best practices and how AI can be used by data scientists to use this data for meaningful analysis.
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Converseon
Published By: graphgrid     Published Date: Oct 19, 2018
Graph databases are about to catapult across the famous technology adoption chasm and land in start-ups, enterprises and government agencies across the globe. The adoption antibodies are subsiding as the power of natively connected data becomes fundamental to any organization looking for data-driven insights across operations, suppliers, and customers. Moore’s Law increases in storage capacity and processing power can no longer keep up with the pace of data expansion, yet how companies structure and analyze their data ultimately will impact their ability to compete. Unstructured, disconnected data is useless. Graph databases will rapidly jump from niche use cases to a transformative IT technology as they enable turning the data you collect into actionable insights. Data will become the single most differentiating asset for your organization.
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graphgrid
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: DATAVERSITY     Published Date: Jun 17, 2013
This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. “Data Governance” is a term fraught with nuance, misunderstanding, myriad opinions, and fear. It is often enough to keep Data Stewards and senior executives awake late into the night. The modern enterprise needs reliable and sustainable control over its technological systems, business processes, and data assets. Such control is tantamount to competitive success in an ever-changing marketplace driven by the exponential growth of data, mobile computing, social networking, the need for real-time analytics and reporting mechanisms, and increasing regulatory compliance requirements. Data Governance can enhance and buttress (or resuscitate,if needed) the strategic and tactical business drivers every enterprise needs for market success.
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research paper, data, data management, data governance, data steward
    
DATAVERSITY
Published By: Ted Hills     Published Date: Mar 29, 2016
NoSQL database management systems give us the opportunity to store our data according to more than one data storage model, but our entity-relationship data modeling notations are stuck in SQL land. Is there any need to model schema-less databases, and is it even possible? In this short white paper, Ted Hills examines these questions in light of a recent paper from MarkLogic on the hybrid data model. Ted Hills has been active in the Information Technology industry since 1975. At LexisNexis, Ted co-leads the work of establishing enterprise data architecture standards and governance processes, working with data models and business and data definitions for both structured and unstructured data. His book, NoSQL and SQL Data Modeling, was recently released by Technics Publications (http://technicspub.com).
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Ted Hills
Published By: Hewlett Packard Enterprise     Published Date: Jan 31, 2019
With the maturing of the all-flash array (AFA) market, the established market leaders in this space are turning their attention to other ways to differentiate themselves from their competition besides just product functionality. Consciously designing and driving a better customer experience (CX) is a strategy being pursued by many of these vendors.This white paper defines cloud-based predictive analytics and discusses evolving storage requirements that are driving their use and takes a look at how these platforms are being used to drive incremental value for public sector organizations in the areas of performance, availability, management, recovery, and information technology (IT) infrastructure planning.
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Hewlett Packard Enterprise
Published By: Sage EMEA     Published Date: Jan 29, 2019
Enterprises must continuously change to keep ahead of the competition, reduce silos, improve connectivity and respond rapidly to a changing world. Organisations also need to drive continuous innovation with technology that helps them adapt faster. So if you’re thinking of replacing your legacy ERP system, start by asking yourself these three essential questions:
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Sage EMEA
Published By: Sage EMEA     Published Date: Jan 29, 2019
Ready to work faster, simpler, and more flexibly? Now you can. With today’s technology advancements, there’s no need to settle for less when it comes to your enterprise management solution. And now, with the introduction of Sage Business Cloud, you can future-proof your business with the only management solution you’ll ever need. Sage Business Cloud enables you to come onto the platform at any stage of your business and allows you to grow over time by adding new products as you need them. These include core cloud products across accounting, financials, enterprise management, and HR, as well as a thriving ecosystem that draws from more than 200 ISVs. Request a business review and discover how the right enterprise management solution can help you manage growth, improve efficiency, make smarter decisions, and reduce IT cost and complexity.
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Sage EMEA
Published By: Emarsys     Published Date: Dec 20, 2018
As a modern marketer, you’re always looking for ways to improve your efficiency at work, especially in the form of software that makes some part of your job easier. You’ve likely heard a lot lately about AI in marketing and how it promises to do just that – and more, of course. In fact, it’s pretty hard to avoid the topic of AI and all the ways it is destined to change marketing as we know it. You may already know that companies who have adopted AI boast about the results: increased conversions, engagement, and a host of other metrics. With such decisive evidence, everybody must be aware of AI and clearly sees its business potential, right? Then you go to your Chief Marketing Officer to strike up an informal conversation where you can strategically recommend the technology, and it starts off great — your CMO is well aware of AI and what it could do for the business. You’re thinking, “What luck! I might not have to put up much of an argument.” Then your CMO says, “But you’re not a data
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Emarsys
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