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Published By: DATAVERSITY     Published Date: Nov 05, 2014
Ask any CEO if they want to better leverage their data assets to drive growth, revenues, and productivity, their answer will most likely be “yes, of course.” Ask many of them what that means or how they will do it and their answers will be as disparate as most enterprise’s data strategies. To successfully control, utilize, analyze, and store the vast amounts of data flowing through organization’s today, an enterprise-wide approach is necessary. The Chief Data Officer (CDO) is the newest member of the executive suite in many organizations worldwide. Their task is to develop and implement the strategies needed to harness the value of an enterprise’s data, while working alongside the CEO, CIO, CTO, and other executives. They are the vital “data” bridge between business and IT. This paper is sponsored by: Paxata and CA Technologies
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chief data officer, cdo, data, data management, research paper, dataversity
    
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: DATAVERSITY     Published Date: May 25, 2014
Deconstructing NoSQL: Analysis of a 2013 Survey on the Use, Production, and Assessment of NoSQL Technologies in the Enterprise This report examines the non-relational database environment from the viewpoints of those within the industry–whether current or future adopters, consultants, developers, business analysts, vendors, or others. This paper is sponsored by: MarkLogic, Cloudant and Neo4j.
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research paper, analysis, nosql, database, nosql database, white paper, nosql white paper
    
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: 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: Semantic Arts     Published Date: Aug 01, 2013
This White Paper explains how Semantic Technology can help organizations leverage their legacy investments into new solutions through the use of a Semantic Layer so they can improve IT productivity by reducing complexity, thereby reducing total cost of ownership.
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white paper, semantic technology, data, data management
    
Semantic Arts
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: AnalytixDS     Published Date: May 09, 2016
This paper seeks to provide insight into how AnalytiX Data Services can address many of the data related issues that come with combining disparate source data as is often the case in mergers and acquisitions. It will cover some of the best features and functionality offered by AnalytiX Data Services’ flagship product AnalytiX Mapping Manager, which have specific capabilities that are of high value in this scenario.
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AnalytixDS
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: WhereScape     Published Date: Mar 16, 2016
Industry expert Wayne Eckerson provides an overview of the emerging data warehouse automation market and outlines the value of using automation tools for developing data warehouses, data marts, analytical environments and big data platforms. Eckerson details WhereScape’s architecture—which enables a data-driven approach to automation. Eckerson also discusses how agility and automation together encourage iterative development and closer collaboration between business and IT.
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WhereScape
Published By: WhereScape     Published Date: Oct 20, 2017
Put IT on Automatic: Cloud Data Warehousing Has Arrived by Eric Kavanagh of The Bloor Group Download this white paper to better understand the value the cloud offers IT teams developing data infrastructure, and how automation can be used to not only accelerate time to value, but to tackle quality control, compliance and developer productivity. Sponsored by WhereScape. To learn more about WhereScape automation, visitwww.wherescape.com"
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WhereScape
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: 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: BackOffice     Published Date: Apr 22, 2018
The success of a business is increasingly influenced by how effectively it utilizes data within strategic decision making & operations. But when a business views its critical data simply as a byproduct of business processes, and doesn’t value it as a business asset, it increases the risk of not being able to achieve its desired outcomes.
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BackOffice
Published By: Data Blueprint     Published Date: Apr 02, 2014
Organizations maintain data-based assets in hopes of successfully employing them in support of strategy. In an attempt to provide valued products and/or services, a customer relationship management (CRM) strategy should attempt to improve what is known about the wants and needs of existing customers. An organization may desire to transfer its inventory to its suppliers and to only play the role of transaction broker. A third strategy might be to use data to obtain significant efficiencies from productions/operations, ensuring a low cost advantage.
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data, data management, data value, return on investment, white paper
    
Data Blueprint
Published By: Red Hat     Published Date: Sep 09, 2018
This assessment shows that enterprises adopt Red Hat Fuse because they believe in a community-based open source approach to integration for modernizing their integration infrastructure that delivers strong ROI. For these organizations, Fuse was part of a larger digital transformation initiative and was also used to modernize integration. IDC interviewed organizations using Fuse to integrate important business applications across their heterogeneous IT environments. These Red Hat customers reported that Fuse has enabled them to complete substantially more integrations at a higher quality level, thereby supporting their efforts to deliver timely and functional applications and digital services. Efficiencies in application integration with Fuse have generated significant value for study participants, which IDC quantifies at an average value of $75,453 per application integrated per year ($985,600 per organization). They have attained this value by: » Enabling more efficient and effectiv
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Red Hat
Published By: SAP     Published Date: Aug 14, 2018
Today, ERP is a cornerstone of many organisations’ tech infrastructure. Yet many of the solutions in play are legacy products, decades old and designed for an entirely different business landscape. With industry leaders expressing concern that out-of-date ERP solutions have now become an impediment to competitive business, IDC set out to uncover what exactly modern companies need from their ERP systems, and why SAP S/4Hana seems so well placed to deliver.
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SAP
Published By: Hewlett Packard Enterprise     Published Date: Jul 18, 2018
"In today’s Idea Economy, businesses need to turn ideas into services faster. Every new business and established enterprise is at risk of missing a market opportunity and being disrupted by a new idea or business model. It has never been easier, or more cru cial, to turn ideas into new products, services, or applications —and quickly drive them to market. But IT needs an infrastructure that enables them to partner with the business to speed the delivery of services."
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Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: Jul 19, 2018
"Enterprises depend on data to improve customer interaction, accelerate product development and run the back office. ?Is infrastructure complexity holding your applications back? ? Do your applications have instant access to data?"
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Hewlett Packard Enterprise
Published By: Zendesk     Published Date: Jun 29, 2018
Zendesk Support helps you improve relationships with your customers and provide them with a seamless experience. Improve customer satisfaction, increase agent productivity, scale your support operation, and better manage your team. Watch the demo and take advantage of your support efforts to benefit your entire company.
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Zendesk
Published By: Zendesk     Published Date: Jun 29, 2018
The start of a new chapter of your business, whether you're moving upmarket or adding products and features, is a great time to scale your customer service operations in a smart way. We know customers prefer self-service, via a knowledge base, if one is available. A Gartner report estimates that CIOs can reduce customer support costs by 25% or more when proper knowledge management discipline is in place. If you've been on the sidelines waiting to take the self-service leap, this white paper will prove that you and your agents already partake in the activities needed to offer great self-service, every single day.
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Zendesk
Published By: Red Hat     Published Date: Aug 22, 2018
In the emerging digital enterprise, there’s a good chance some application development will be taking place outside the information technology department. It’s not that the role of IT is in any way being diminished – in fact, IT managers are getting busier than ever, overseeing the technology strategies of their enterprises. Rather, the pieces are in place for business users to build and configure the essential business applications they need, on a self-service basis, with minimal or no involvement of their IT departments. As the world moves deeper into an era of ongoing disruption from digital players – be they startups, or teams within established enterprises – technology has become an essential part of every job, from the boardroom to the boiler room. Accordingly, the discipline of IT is no longer confined to the data center or development shop. Many business managers and professionals are building, launching or downloading their own applications to achieve productivity and respond
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Red Hat
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