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Published By: DATAVERSITY     Published Date: Nov 20, 2015
The competitive advantages realized from a dependable Business Intelligence and Analytics (BI/A) are well documented. Everything from reduced business costs and increased customer retention to better decision making and the ability to forecast opportunities have been observed outcomes in response to such programs. The implementation of such a program remains a necessity for any growing or mature enterprise. The establishment of a comprehensive BI/A program that includes traditional Descriptive Analytics along with next generation categories such as Predictive or Prescriptive Analytics is indispensable for business success.
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data, data management, analytics, business intelligence, data science
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Oct 04, 2016
This report evaluates each question posed in a recent survey and provides subsequent analysis in a detailed format that includes the most noteworthy statistics, direct comments from survey respondents, and the influence on the industry as a whole. It seeks to present readers with a thorough review of the state of Metadata Management as it exists today.
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DATAVERSITY
Published By: Couchbase     Published Date: Dec 04, 2014
Interactive applications have changed dramatically over the last 15 years. In the late ‘90s, large web companies emerged with dramatic increases in scale on many dimensions: · The number of concurrent users skyrocketed as applications increasingly became accessible · via the web (and later on mobile devices). · The amount of data collected and processed soared as it became easier and increasingly · valuable to capture all kinds of data. · The amount of unstructured or semi-structured data exploded and its use became integral · to the value and richness of applications. Dealing with these issues was more and more difficult using relational database technology. The key reason is that relational databases are essentially architected to run a single machine and use a rigid, schema-based approach to modeling data. Google, Amazon, Facebook, and LinkedIn were among the first companies to discover the serious limitations of relational database technology for supporting these new application requirements. Commercial alternatives didn’t exist, so they invented new data management approaches themselves. Their pioneering work generated tremendous interest because a growing number of companies faced similar problems. Open source NoSQL database projects formed to leverage the work of the pioneers, and commercial companies associated with these projects soon followed. Today, the use of NoSQL technology is rising rapidly among Internet companies and the enterprise. It’s increasingly considered a viable alternative to relational databases, especially as more organizations recognize that operating at scale is more effectively achieved running on clusters of standard, commodity servers, and a schema-less data model is often a better approach for handling the variety and type of data most often captured and processed today.
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database, nosql, data, data management, white paper, why nosql, couchbase
    
Couchbase
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: CA Technologies     Published Date: Feb 25, 2016
As combinations of both internal and externally-imposed business policies imply dependencies on managed data artifacts, organizations are increasingly instituting data governance programs to implement processes for ensuring compliance with business expectations. One fundamental aspect of data governance involves practical application of business rules to data assets based on data elements and their assigned values. Yet despite the intent of harmonizing data element definitions and resolution of data semantics and valid reference values, most organizations rarely have complete visibility into the metadata associated with enterprise data assets.
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CA Technologies
Published By: MapR Technologies     Published Date: Aug 01, 2018
How do you get a machine learning system to deliver value from big data? Turns out that 90% of the effort required for success in machine learning is not the algorithm or the model or the learning - it's the logistics. Ted Dunning and Ellen Friedman identify what matters in machine learning logistics, what challenges arise, especially in a production setting, and they introduce an innovative solution: the rendezvous architecture. This new design for model management is based on a streaming approach in a microservices style. Rendezvous addresses the need to preserve and share raw data, to do effective model-to-model comparisons and to have new models on standby, ready for a hot hand-off when a production model needs to be replaced.
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MapR Technologies
Published By: Access Sciences     Published Date: Sep 07, 2014
Few organizations have fully integrated the role of the Data Steward due to concerns about additional project complexity, time away from other responsibilities or insufficient value in return. The principles of the Agile methodology (whether or not Agile is followed for projects) can offer guidance in making the commitment to designating and empowering the Data Steward role. By placing insightful people in a position to connect innovators, respond to change and spur development aligned with business activities, organizations can expect to see a more efficient and effective use of their information assets.
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Access Sciences
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: Trillium Software     Published Date: Mar 29, 2016
We are living in a new age in which your business success depends on access to trusted data across more systems and more users faster than ever before. However, your business information is often incomplete, filled with errors, and beyond the reach of people who need it. Whether you’re responsible for technology or information strategy, you need to enable your business to have real-time access to reliable information. Otherwise, your company will be left behind. Download Trillium’s whitepaper, “How to Succeed in the New Age of Data Quality”, to learn how you can create a successful data quality strategy.
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Trillium Software
Published By: Experian     Published Date: May 17, 2016
Every year, Experian Data Quality conducts a study to look at the global trends in data quality. This year, research findings reveal how data practitioners are leveraging and managing data to generate actionable insight, and how proper data management is becoming an organization-wide imperative. This study polled more than 1,400 people across eight countries globally from a variety of roles and departments. Respondents were chosen based on their visibility into their orgazation's customer data management practices. Read through our research report to learn: - The changes in channel usage over the last 12 months - Expected changes in big data and data management initiatives - Multi-industry benchmarks, comparisons, and challenges in data quality - And more! Our annual global benchmark report takes a close look at the data quality and data management initiatives driving today's businesses. See where you line up and where you can improve.
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Experian
Published By: Reltio     Published Date: Feb 12, 2016
Reltio delivers reliable data, relevant insights and recommended actions so companies can be right faster. Reltio Cloud combines data-driven applications with modern data management for better planning, customer engagement and risk management. IT streamlines data management for a complete view across all sources and formats at scale, while sales, marketing and compliance teams use data-driven applications to predict, collaborate and respond to opportunities in real-time. Companies of all sizes, including leading Fortune 500 companies in healthcare and life sciences, distribution and retail rely on Reltio.
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Reltio
Published By: Alation     Published Date: Mar 15, 2016
curation (noun): The act of organizing and maintaining a collection (such as artworks, artifacts, or data). Data curation is emerging as a technique to support data governance, especially in data-driven organizations. As self-service data visualization tools have taken off, sharing the nuances and best practices of how to use data becomes ever more critical. Analysts at companies from eBay to Safeway and Square are scaling their data knowledge through curation techniques. What are the 4 steps to successful data curation? Find out here:
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data stewardship, self-service analytics, data curation, data governance
    
Alation
Published By: FairCom     Published Date: May 25, 2016
As companies embrace NoSQL as the “next big thing,” they are rightly cautious of abandoning their investment in SQL. The question a responsible developer or IT manager must investigate is “in which cases are each of these technologies, SQL and NoSQL, the appropriate solution?” For example, cloud provider BigStep offered this assessment: “NoSQL is not the best model for OLTP, ad hoc queries, complicated relationships among the data, and situations when stability and reliability outweigh the importance of speed.” While that statement may be true of many NoSQL databases, c-treeACE is the exception. Its unique, No+SQL architecture offers the advantages of SQL on top of a robust, high-performance NoSQL core engine. In this white paper, you'll read five ways c-treeACE breaks the NoSQL mold in terms of: • Data Integrity • Availability and Reliability • Complex Data Relationships • Flexible Queries • Performance
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FairCom
Published By: Dataiku     Published Date: Feb 01, 2018
A proof of concept (POC) is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. But what does running a POC mean in practice specifically for data science? POCs should prove not just that a solution solves one particular, specific problem, but that the solution in question will provide widespread value to the company: that it's capable of bringing a data-driven perspective to a range of the business's strategic objectives. Get the 7 steps to running an efficient POC in this white paper.
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Dataiku
Published By: Dataiku     Published Date: Feb 19, 2018
A proof of concept (POC) is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. But what does running a POC mean in practice specifically for data science? POCs should prove not just that a solution solves one particular, specific problem, but that the solution in question will provide widespread value to the company: that it's capable of bringing a data-driven perspective to a range of the business's strategic objectives. Get the 7 steps to running an efficient POC in this white paper.
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Dataiku
Published By: MarkLogic     Published Date: Jun 16, 2013
The primary issue discussed within this paper boils down to two disparate database reliability models: ACID vs BASE. The first (ACID) has been around for some 30+ years, is a proven industry standard for SQL-centric and other relational databases, and works remarkably well in the older, yet still extant, world of vertical scaling. The second (BASE) has only recently gained popularity over the past 10 years or so, especially with the rise of social networking, Big Data, NoSQL, and other leviathans in the new world of Data Management. BASE requirements rose out of a need for ever-expanding horizontally scaled distributed networks, with non-relational data stores, and the real-time availability constraints of web-based transaction processing. While there are now more crossovers and negotiations between the two models, they essentially represent two competing groups, with Brewer’s CAP Theorem acting as the referee in the middle forcing tough decisions on each team.
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data, data management, unstructured data, nosql, database, acid, base, database transactioning
    
MarkLogic
Published By: Semarchy     Published Date: Aug 18, 2016
David Loshin reexamines the way we ingest, manage, consume, and transform data into actionable information and intelligence. Read how this industry expert makes the case for data governance with an unconventional business-first focus. The conventional wisdom on data governance proposes hierarchies, operating models, and processes for data policy definition and implementation. Unfortunately, poorly-designed and minimally-planned data governance processes are ineffective because they are bureaucratic and overwhelming. This is especially true when processes are imposed by fiat, take a long time, and don't result in any short-term improvement in information value. But proper data governance is a critical success factor for master data management! In this paper, we examine the motivations for coupling data governance with master data management and consider how to evolve data policies and processes to position master data management for success.
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Semarchy
Published By: Hewlett Packard Enterprise     Published Date: Jan 31, 2019
Discover how HPE is responding to the massive growth in enterprise data with intelligent storage. Data helps enterprises find new ways to reach and serve customers to grow profitability, but only when it is available at the right place and the right time. The growing complexity of managing and securing data prevents businesses from gaining its full value. Hewlett Packard Enterprise delivers the world’s most intelligent storage for the hybrid cloud world by providing storage that is driven by artificial intelligence, built for the cloud, and delivered as a service.
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Hewlett Packard Enterprise
Published By: Sage EMEA     Published Date: Jan 29, 2019
Sage pays close attention to what your business demands For nearly 40 years, Sage has followed through on its founding commitment: Making sure we deliver what our customers need. That dedication has helped us become one of the world’s largest suppliers of enterprise management solutions, used by more than 3 million businesses of all types and sizes around the world. Sage helps drive today’s business builders with a new generation of solutions to manage everything from money to people wherever they’re needed, offering the freedom to work with mobility. Above all else, we’re driven to satisfy our customers and keep them coming back. That commitment has paid off. We beat SAP (19), Oracle (25) and Microsoft (34) with our Net Promoter Score (NPS)—a respected customer-satisfaction, customer-experience, and loyalty indicator widely used by Fortune 500 companies. The industry NPS average is 31.75, meaning we are 58% better than the average, and 88% above our closest competition. It shows t
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Sage EMEA
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: 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
Published By: Zendesk     Published Date: Jan 04, 2019
Seus clientes eles entendem mais de tecnologia do que nunca e preferem a abordagem "faça você mesmo" para resolver seus problemas e responder às suas próprias dúvidas. Anos de pesquisas feitas pelo ICMI confirmaram que os clientes preferem resolver seus problemas por conta própria e usando seus canais favoritos. Além disso, os clientes só procuram interações diretas quando esgotam todas as opções de autoatendimento. Essa informação se baseia em dados da American Express, que descobriu que 48% dos consumidores preferem falar com um representante do atendimento ao cliente para lidar com problemas complexos, mas apenas 16% preferem esse mesmo contato para resolver problemas simples. O objetivo desse documento é simples: queremos ajudá-lo a criar uma base de conhecimento, uma comunidade e um portal do cliente completos. E você pode fazer tudo isso usando a Central de Ajuda do Zendesk Guide.
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Zendesk
Published By: Zendesk     Published Date: Jan 04, 2019
Não importa qual o tipo ou o horário de operação da sua empresa: sua porta está sempre aberta. Atualmente, os clientes fazem perguntas a qualquer hora do dia ou da noite e esperam ser respondidos (desde que não demore muito). Isso representa um desafio para a maioria das empresas: como antecipar os canais que o cliente vai querer ou dos quais vai precisar? E como determinar se esses canais serão eficientes para a sua organização? A resposta simples é: sim, você precisa. E este guia vai explorar exatamente isso. Os clientes querem entrar em contato com as empresas de várias formas e esperam que cada interação com a marca seja consistente com as conversas que teve antes e as que terá depois. Aprenda como com a solução omnichannel do Zendesk.
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Zendesk
Published By: Zendesk     Published Date: Jan 04, 2019
Quando sua empresa começa um novo capítulo, seja mudando de mercado ou lançando novos produtos e recursos, o momento é ótimo para expandir as operações de atendimento ao cliente de maneira inteligente. Sabemos que, se disponível, os clientes preferem usar a base de conhecimento para se ajudarem. Um relatório do Gartner estima que os Diretores de informática podem reduzir os custos com suporte em 25% ou mais quando uma rotina adequada de gestão de conhecimento é implementada. Se você estava esperando o momento certo para mergulhar de cabeça no autoatendimento, este white paper provará que você e seus agentes já fazem tudo o que precisam para oferecer autoatendimento excelente todos os dias.
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Zendesk
Published By: Google Apigee     Published Date: Feb 05, 2019
Application programming interfaces, or APIs, are the way software talks to other software—so by definition, they’re a technology that connects systems. Many enterprise leaders think of them primarily in these terms. For example, 57 percent of respondents to Google’s enterprise digital maturity assessment tool, Apigee Compass, characterize APIs as systems integration technology.
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Google Apigee
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