data research

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Published By: Amazon Web Services and Intel     Published Date: Oct 07, 2015
With the large amounts of data that are constantly being utilized in today’s highly digitized world, there is a necessity for top of the line infrastructure to maintain everything with precision. Download this case study to see how high performance cloud computing can enhance collaboration and see how it will allow for research to be shared easily and the ability to scale up when needed.
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high performance cloud computing, collaboration, data
    
Amazon Web Services and Intel
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Today, businesses pour Big Data into data lakes to help them answer the big questions: Which product to take to market? How to reduce fraud? How to retain more customers? People need to get these answers faster than ever before to reduce “time to answer” from months to minutes. The data is coming in fast and the answers must come just as fast. The answer is self-service data preparation and analytics tools, but with that comes an expectation that the right data is going to be there. Only by using a data catalog can you find the right data quickly to get the expected insight and business value. Download this white paper to learn more!
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. Download this white paper to find out how you can improve your data preparation for business analytics.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Business users want the power of analytics—but analytics can only be as good as the data. To perform data discovery and exploration, use analytics to define desired business outcomes, and derive insights to help attain those outcomes, users need good, relevant data. Executives, managers, and other professionals are reaching for self-service technologies so they can be less reliant on IT and move into advanced analytics formerly limited to data scientists and statisticians. However, the biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Our recent primary research project solicited feedback from respondents to determine familiarity with, and plans for use of Hadoop ecosystem components. Download this white paper to learn more.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
For many years, traditional businesses have had a systematic set of processes and practices for deploying, operating and disposing of tangible assets and some forms of intangible asset. Through significant growth in our inquiry discussions with clients, and in observing increased attention from industry regulators, Gartner now sees the recognition that information is an asset becoming increasingly pervasive. At the same time, CDOs and other data and analytics leaders must take into account both internally generated datasets and exogenous sources, such as data from partners, open data and content from data brokers and analytics marketplaces, as they come to terms with the ever-increasing quantity and complexity of information assets. This task is clearly impossible if the organization lacks a clear view of what data is available, how to access it, its fitness for purpose in the contexts in which it is needed, and who is responsible for it.
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Waterline Data & Research Partners
Published By: Waterline Data & Research Partners     Published Date: Nov 07, 2016
Enterprise architects and their peers need to read this report to navigate the vendor landscape of the important and emerging big data technology.
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Waterline Data & Research Partners
Published By: Mimecast     Published Date: Mar 13, 2017
The EU General Data Protection Regulation (GDPR) is arriving soon. You may think your organization is immune from its impact, but if you do business with any customers in the EU, think again. You’ll need to rethink and possibly re-do your organizational processes around compliance and oversight. It may seem like a daunting task. The Forrester Research Brief “You Need an Action Plan for the GDPR” helps your security, regulatory and privacy teams grasp five must-have changes necessary to comply with the GDPR.
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Mimecast
Published By: IBM     Published Date: May 03, 2016
In our 33-criteria evaluation of customer analytics vendors, we identified the 11 most significant software providers in this category - adobe, agilone, alteryx, angoss, FICo, IBM, Manthan, Pitney Bowes, saP, sas, and teradata - and researched, analyzed, and scored them. this evaluation details our findings about how well each vendor fulfills our criteria and where each vendor stands in relation to each other. this report will help customer insights (CI) professionals and marketers make the right choice for their customer analytics needs.
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ibm, forrester, customer analytics, customer insights
    
IBM
Published By: IBM     Published Date: Jan 19, 2017
With organizations keeping larger and larger quantities of data the question will come up that given dropping storage costs, does uncontrolled data growth even matter? It does matter, and in this IBM publication featuring Gartner research you will learn about this ever growing problem and ways for information managers to address solutions.
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ibm, analytics, data, ecm, big data, information governance
    
IBM
Published By: Druva     Published Date: Sep 27, 2017
Wondering what data protection will look like in 2017? In this report, ESG provides new research on what is driving the need for better data protection and recovery in organizations of all sizes with major trends pointing to the cloud as the ultimate game changer.
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data protection, data recovery, cloud
    
Druva
Published By: Group M_IBM Q119     Published Date: Jan 04, 2019
Digital transformation is poised to change the supply chain more profoundly than any other functional area and more dramatically than at any point in its history in terms of driving efficiency and resiliency to disruption. In the context of the challenges facing supply chains, both now and in the future, it becomes clear that the old ways of working will not suffice and that even best-in-class performance today is unlikely to be good enough in the future. It is the view of IDC that the supply chain must become a "thinking" supply chain, one that is intimately connected to all data sources, enabled with comprehensive and fast analytics, openly collaborative through cloud-based commerce networks, conscious of cyberthreats, and cognitively interwoven. According to IDC supply chain research, technology is emerging as a prime driver of change, particularly artificial intelligence, blockchain, and the Internet of Things (IoT).
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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
In our 29-criteria evaluation of machine learning data catalogs (MLDCs) providers, we identified the 12 most significant ones — Alation, Cambridge Semantics, Cloudera, Collibra, Hortonworks, IBM, Infogix, Informatica, Oracle, Reltio, Unifi Software, and Waterline Data — and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise architecture (EA) professionals make the right choice.
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Group M_IBM Q2'19
Published By: Domino Data Lab     Published Date: Feb 08, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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Domino Data Lab
Published By: Domino Data Lab     Published Date: May 23, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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Domino Data Lab
Published By: Selligent Marketing Cloud     Published Date: Mar 07, 2018
What does it take to be relevant today? In the era of hyper-connectivity, consumers have become entitled, demanding more control over their experiences and expecting that marketers use data and insights to create a seamless, relevant brand experience. Research shows that communications containing relevant information and offers are the best drivers of brand loyalty and conversions
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insight marketing, customer engagement, omnichannel, multichannel, automation, loyalty, crm, marketing
    
Selligent Marketing Cloud
Published By: Oracle     Published Date: Sep 24, 2019
Don’t drown in your company’s data, use it to gain a competitive advantage. This research report explores how as a finance leader you need to become a data leader, disrupting your own thinking, then connecting your organisation’s data to outpace change. This is a toolkit for transformation in the age of hyperconnectivity.
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Oracle
Published By: Oracle     Published Date: Sep 24, 2019
The digital age will transform your supply chain. With hyperconnected, intelligent data at the heart of your logistics you’ll drive your company’s success. Our latest research reveals how today’s supply chain leaders create innovation, consistency and value by employing connected data. It addresses key organisational challenges that you need to be aware of. These include how to disrupt the culture of not just your team, but your whole company, to transform it into a data leader. This is a guidebook for the future of responsive, hyperconnected supply chains. Discover how to become a supply chain leader now.
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Oracle
Published By: Oracle     Published Date: Sep 24, 2019
HR has never been more in the spotlight. So it’s never been more important to not just store your data, but use it to transform ways of working. This trend-led research report explores how HR leaders need to become data leaders, using connected data to drive innovation, productivity and growth. Discover insights from those leading the way, and delve into the links between transforming culture and significant growth. Read our report for HR leaders now.
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Oracle
Published By: Flexential     Published Date: Jul 17, 2019
By 2020, there will be 50 billion devices producing 600Zb of data, driving enterprises to assess their IT infrastructures to keep pace with unprecedented data processing demands. Is your business ready to handle this data explosion? In this on-demand webinar, you’ll hear Tim Parker, Vice President of Network Services at Flexential, and Craig Matsumoto, Senior Analyst of Data Center Services at 451 Research, discuss the evolution of data – and how enterprises are processing and creating it. Download now to hear Parker and Matsumoto examine how edge computing solutions are meeting the latency demands of an increasingly digitized world.
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Flexential
Published By: Informatica     Published Date: Oct 28, 2011
In this White Paper, Bloor Research director Philip Howard discusses how Data Replication can help you deliver active data warehousing for analytics.
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Informatica
Published By: BlueArc Corporation     Published Date: Apr 24, 2008
The crisis of mass power consumption in the corporate data center has come to a head. Power required to run data centers in the U.S. is estimated to be as much as that produced by five power plants in a year. Energy expenditures and requirements have doubled in the last five years, and computer disposal is the fastest growing type of waste in the world, according to top Stanford researchers and Greenpeace.
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bluearc, green, green computing, energy, energy efficient
    
BlueArc Corporation
Published By: SAS     Published Date: Apr 16, 2015
Big data has made quite an impression on organizations embarking on data journeys, hoping to glean valuable insights ranging from process optimization to customer-facing improvements. This research paper explores proven best practices that can help organizations overcome obstacles to deliver on big data potential.
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SAS
Published By: MicroStrategy     Published Date: Jan 23, 2019
By 2025, the total amount of data produced will grow to 175 zettabytes, according to IDC’s Data Age 2025 Report. How will enterprises evolve over the next year to manage and make the most of this unprecedented growth? Download 10 Enterprise Analytics Trends to Watch in 2019 to learn how leading organizations will win with embedded and augmented analytics, HyperIntelligence, collaboration, enterprise AI strategies, and more. This eBook details the transformational technologies data-driven organizations will need to leverage to get and stay ahead in 2019 and beyond, with contributions from thought leaders, including: Mike Gualtieri of Forrester Research Ray Wang and Doug Henschen of Constellation Research Mark Smith and David Menninger of Ventana Research Chandana Gopal of IDC Ronald van Loon, and more. Is your organization ready for the data-driven future? Read 10 Enterprise Analytics Trends to Watch in 2019 today to find out.
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MicroStrategy
Published By: MicroStrategy     Published Date: Apr 11, 2019
By 2025, the total amount of data produced will grow to 175 zettabytes, according to IDC’s Data Age 2025 Report. How will enterprises evolve over the next year to manage and make the most of this unprecedented growth? Download 10 Enterprise Analytics Trends to Watch in 2019 to learn how leading organizations will win with embedded and augmented analytics, HyperIntelligence, collaboration, enterprise AI strategies, and more. This eBook details the transformational technologies data-driven organizations will need to leverage to get and stay ahead in 2019 and beyond, with contributions from thought leaders, including: Mike Gualtieri of Forrester Research Ray Wang and Doug Henschen of Constellation Research Mark Smith and David Menninger of Ventana Research Chandana Gopal of IDC Ronald van Loon, and more. Is your organization ready for the data-driven future? Read 10 Enterprise Analytics Trends to Watch in 2019 today to find out.
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MicroStrategy
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