data projects

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By: Dell EMC     Published Date: Feb 14, 2019
Artificial intelligence (AI) is a transformative technology that will change the way organizations interact and will add intelligence to many products and services through new insights currently hidden in vast pools of data. In 2017 alone, venture capitalists invested more than $11.7 billion in the top 100 Artificial Intelligence startups, according to CB Insights1, and the breadth of Artificial Intelligence applications continues to grow. While human-like intelligence will remain the stuff of novels and movies for the near future, most organizations can and should explore practical Artificial Intelligence projects. This technology has the potential to: • Improve productivity of internal applications • Increase revenue through enhanced customer interacton and improved customer acquisiiton • Reduce costs by optimizing operations • Enhance products and services with "smart" functionality such as vision and voice interaction and control This paper provided by Dell and Intel® gives executi
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     Dell EMC
By: Zynapse     Published Date: Jun 16, 2010
Data Governance has emerged as the point of convergence for people, technology and process in order to manage the crucial data (information) of an enterprise. This is a vital link in the overall ongoing data management process for it maintains the quality of data and makes it available to a wide range of decision making hierarchy across an organization
Tags : zynapse, erp projects, data information, data management, governance, mdm, master data management, odm
     Zynapse
By: HERE Technologies     Published Date: Jan 22, 2019
To improve safety and mobility across its 5,600km road network, the City of Toronto forged a partnered with HERE Technologies for the provision of traffic, incident, and historical traffic data. Access to this data allows the city authority to see exactly what’s happening on its roads and more easily and affectively run studies on improvement projects. This case study details how HERE Technologies enabled the City of Toronto’s transportation team to: Work smarter with comprehensive network coverage and accurate data to aid analysis Examine the impact of city projects without significant forward planning or expenditure Ensure travel volume models used to drive decision making are calibrated to represent real-world truths
Tags : public sector, urbanization, traffic management
     HERE Technologies
By: Digital Realty     Published Date: Feb 25, 2015
ARM had an immediate need for a flexible provider that was willing to collaborate on a “build-to-suit” energy efficient data center, but also want a long-term partner to consider for future projects in other parts of the world. Find out why they chose Digital Realty.
Tags : data center, digital realty, energy efficient, build-to-suit, research, energy efficiency, sustainability
     Digital Realty
By: Delphix     Published Date: May 03, 2016
Data security is a top concern these days. In a world of privacy regulation, intellectual property theft, and cybercrime, ensuring data security and protecting sensitive enterprise data is crucial. Only a data masking solution can secure vital data and enable outsourcing, third-party analysis, and cloud deployments. But more often than not, masking projects fail. Some of the best data masking tools bottleneck processes and once masked, data is hard to move and manage across the application development lifecycle.
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     Delphix
By: TIBCO Software     Published Date: Jan 17, 2019
Are you considering data virtualization for your organization today? In this paper you will learn 10 core truths about data virtualization and gain essential knowledge for overcoming analytic data bottlenecks and driving better outcomes.
Tags : virtualization, data, analytics, datasets, software, access, integration, projects, tools, scalability
     TIBCO Software
By: Collaborative Consulting     Published Date: Dec 23, 2013
There are some surprisingly straightforward reasons behind the glitches, delays, and cost-overruns that can bedevil data warehouse initiatives. ...The first is simply confusing expectations with requirements. But four other troublemakers can also lead to big problems for developers, IT departments, and organizations seeking to maximize the business value of information.
Tags : collaborative consulting, data warehouse, failed projects, business intelligence, business solution, meet expectations, big data, profile importance, cloud vendors, data quality, business goals, complicated architectures, avoid wasted expense, data management, data center
     Collaborative Consulting
By: SAS     Published Date: Oct 22, 2015
The Internet of Things (IoT) presents an opportunity to collect real-time information about every physical operation of a business. From the temperature of equipment to the performance of a fleet of wind turbines, IoT sensors can deliver this information in real time. There is tremendous opportunity for those businesses that can convert raw IoT data into business insights, and the key to doing so lies within effective data analytics. To research the current state of IoT analytics, Blue Hill Research conducted deep qualitative interviews with three organizations that invested significant time and resources into their own IoT analytics initiatives. By distilling key themes and lessons learned from peer organizations, Blue Hill Research offers our analysis so that business decision makers can ultimately make informed investment decisions about the future of their IoT analytics projects.
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     SAS
By: Rubrik EMEA     Published Date: Jan 04, 2019
Rubrik Cloud Data Management is a single, unified software platform to manage your physical, virtual, and cloud data. With Rubrik, enterprises can drastically simplify their data protection processes, automate workflows, and migrate data to the cloud. The results are powerful: faster recoveries, easier management, no forklift upgrades, and hard dollars saved for other projects. Organizations pursuing cloudfirst policies can use Rubrik to archive to the cloud, create cloud DR capabilities, or perform cloud-native backup. And with Rubrik’s Polaris SaaS platform, it has never been easier to unify data across data centers and clouds, simplifying operations and governance. DATA
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     Rubrik EMEA
By: MarkLogic     Published Date: Jun 09, 2017
Your high-stakes data projects don’t have to end – as analysts predict – in failure. Don’t just rely on legacy technology and outdated thinking - the key is to start your next data project armed with the right technology and mindset to succeed. This paper will give you insights and guidelines to help you learn how to leverage all of your data to reach your data integration objectives with less time and expense than you might imagine. Change is good, and in this paper we’ll give you real-world examples of organizations that embraced change and found success.
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     MarkLogic
By: Schneider Electric     Published Date: Oct 31, 2017
Most successful data center migration projects share similar practices, such as expertise, preparation, management, execution, communication and business alignment. The purpose of this research is not to provide an extensive examination of each one of these practices. Rather, it is to provide a pragmatic list of best practices. This list was derived from observations of numerous Gartner clients involved in data center migration projects over the last six years.
Tags : planning, contingency plan, testing, migration, preparation, project team, audit, closure
     Schneider Electric
By: Intel     Published Date: Jun 07, 2017
Using the Integrated Analytics Hub, data analytics projects have already accounted for an estimated quarterly savings on marketing digital-media expenditures of approximately USD 170,000. Download this white paper to find out more.
Tags : intel, analytics, data, data analytics, data science
     Intel
By: IBM     Published Date: Oct 19, 2015
This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
Tags : ibm, integration, data volume, business technology, information, it management, knowledge management, data management
     IBM
By: Liaison Technologies     Published Date: Nov 21, 2011
In this whitepaper, we describe how executive and IT management can get and maintain control of their company's data architecture to help meet business objectives. We describe the relationship between data integration projects and data architecture concepts and practices. We describe how the business planning and IT development processes that direct and leverage data integration projects depend on tooling. We describe breakthrough ways in which data architects, business analysts, programmers, and business users can collaborate to address their organization's pressing business challenges.
Tags : enterprise, soa, technology, liaison, business, data integration, data architecture, it management
     Liaison Technologies
By: BMC Software     Published Date: May 28, 2014
"In the paper, “Integrate Big Data into Your Business Processes and Enterprise Systems” you’ll learn how to drive maximum value with an enterprise approach to Big Data. Topics discussed include: • How to ensure that your Big Data projects will drive clearly defined business value • The operational challenges each Big Data initiative must address • The importance of using an enterprise approach for Hadoop batch processing
Tags : it management
     BMC Software
By: Oracle ODA     Published Date: Aug 15, 2016
Businesses understand more than ever that they depend on data for insight and competitive advantage. And when it comes to data, they have always wanted easy access and fast performance. But how is the situation different now? Today, organizations want those elements and more. They want IT to strip away the limitations of time with faster deployment of new databases and applications. They want IT to reduce the limitations of distance by giving remote and branch offices better and more reliable access. And in a global world where business never stops, they want IT to ensure data availability around the clock. If IT can deliver databases and applications faster, on a more automated and consistent basis, to more locations without having to commit onsite resources, IT will be free to focus on more strategic projects.
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     Oracle ODA
By: Veritas     Published Date: Jan 04, 2019
The digital business continues to evolve. Investments in data analytics projects lead the way while traditional, proprietary infrastructures are being disrupted by cloud, open source and hyperconverged paradigms. These changes are forcing IT leaders to contend with greater workload diversity in the midst of tightening budgets. And while the workload [or] IT landscape is changing, the need for reliable data protection remains as crucial as ever to protect against, data corruption, human error, and malicious threats such as ransomware. Learn how Veritas can help you navigate through these obstacles. Join us to hear experts from ESG and Veritas discuss how the right data protection solution today can prepare you for tomorrow's business demands. You will learn: The key trends that are driving change in the digital business The most common causes of data loss in tomorrow’s multi-cloud data centers How to protect an increasingly diverse environment with minimal operational overhead
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     Veritas
By: TIBCO Software APAC     Published Date: Aug 15, 2018
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms. Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
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     TIBCO Software APAC
By: IBM     Published Date: May 17, 2016
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.
Tags : ibm, big data, inline analytics, business analytics, roi
     IBM
By: IBM     Published Date: Jul 26, 2017
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base. High-priority big data and analytics projects often target customer-centric outcomes such as improving customer loyalty or improving up-selling. In fact, an IBM Institute for Business Value study found that nearly half of all organizations with active big data pilots or implementations identified customer-centric outcomes as a top objective (see Figure 1).1 However, big data and analytics can also help companies understand how changes to products or services will impact customers, as well as address aspects of security and intelligence, risk and financial management, and operational optimization.
Tags : customer analytics, data matching, big data, competitive advantage, customer loyalty
     IBM
By: IBM     Published Date: Jul 26, 2017
Business leaders are eager to harness the power of big data. However, as the opportunity increases, ensuring that source information is trustworthy and protected becomes exponentially more difficult. If not addressed directly, end users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organizations can boost confidence in big data. A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamle
Tags : mdm, big data, automation, organization
     IBM
By: Schneider Electric     Published Date: May 07, 2018
Modernizing your data center operations doesn't always mean expanding to a physically larger system
Tags : modernization, data center lifecycle services, refresh-ups, mprs, assessment services, old ups, aging infrastructure
     Schneider Electric
By: Delphix     Published Date: Feb 28, 2013
Agile Data Management eliminates the common barriers to successful agile development, reducing both the cost and the risk of short, parallel development projects. This paper discusses common challenges and the technical solutions for addressing them.
Tags : database virtualization, roi, data management, data management software, agile development, application development
     Delphix
By: CA Technologies EMEA     Published Date: Aug 03, 2017
The GDPR is set to have wide-ranging implications for the type of data which can be used in non-production environments. Organizations will need to understand exactly what data they have and who’s using it, and must be able to restrict its use to tasks for which consent has been given.
Tags : organization scope, data controllers, technology, gdpr, application regulation, corporate rules, contractual clauses, anonymize data, privacy projects
     CA Technologies EMEA
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