data projects

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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: 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: 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: Experian     Published Date: Mar 30, 2017
Businesses today recognize the importance of the data they hold, but a general lack of trust in the quality of their data prevents them from achieving strategic business objectives. Nearly half of organizations globally say that a lack of trust in their data contributes to increased risk of non-compliance and regulatory penalties (52%) and a downturn in customer loyalty (51%). To be of value to organizations, data needs to be trustworthy. In this report, you will read about the findings from this unique study, including: · How data powers business opportunities · Why trusted data is essential for performance · Challenges that affect data quality · The current state of data management practices · Upcoming data-related projects in 2017
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Experian
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: Silwood Technology     Published Date: Nov 28, 2016
Business functions in large organizations are usually handled by software application packages. Some of the most well-known of these are from SAP, Oracle, Salesforce and Microsoft. These packages all store their data in a database. Often however it is necessary to use that data with other IT projects. In this instance being able to understand the metadata that defines these databases is critical. The challenge is that their metadata is complex, opaque and difficult to access. This paper describes how the top application packages store and use their own metadata. It explores the importance of understanding that metadata and examines the obstacles in getting at that metadata in a timely and effective manner.
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Silwood Technology
Published By: Silwood Technology     Published Date: Mar 21, 2017
Business functions in large organizations are usually handled by software application packages. Some of the most well-known of these are from SAP, Oracle, Salesforce and Microsoft. These packages all store their data in a database. Often however it is necessary to use that data with other IT projects. In this instance being able to understand the metadata that defines these databases is critical. The challenge is that their metadata is complex, opaque and difficult to access. This paper describes how the top application packages store and use their own metadata. It explores the importance of understanding that metadata and examines the obstacles in getting at that metadata in a timely and effective manner.
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Silwood Technology
Published By: T4G     Published Date: Mar 15, 2017
About to embark on an advanced analytics project? Or have already started and things aren’t going as planned? This white paper will share our approach to ensure you set up for success. We will discuss aspects of data strategy and data stewardship, and why they are so important. We will outline the benefits of having a solid data strategy and how to start the data strategy conversation within your organization. We will then outline how a project’s entry point (the initial impetus for the project) impacts the scope and approach for the project. And will show you how to avoid missteps and gaps that can lead to less than stellar results or wasted effort. The white paper will touch on the importance of understanding your business drivers and how to use them as your guide to get the most out of your data driven decision making projects. Our proven approach will help ensure a successful start on your advanced analytics journey.
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T4G
Published By: Innovative Systems     Published Date: Mar 29, 2017
Planning a data quality initiative? This paper presents some of the most effective tactics used to justify a data quality initiative, present a strong business case, and get approvals from senior executives, such as: • How to demonstrate to business leadership the costs of poor data quality • The benefits of working with stakeholders to create the business case • How to calculate and present the ROI of proposed projects
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Innovative Systems
Published By: TIBCO Software EMEA     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.
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virtualization, data, analytics, datasets, software, access, integration, projects, tools, scalability
    
TIBCO Software EMEA
Published By: Citrix Systems     Published Date: Feb 14, 2019
"Help your teams be even more productive. By giving them a secure, reliable connection with high-definition access to the apps and data they need, they'll be able to keep projects moving forward seamlessly — even across low bandwidth or high-latency networks. What's more, IT will be able to manage it all efficiently. Download our white paper. You'll discover how Citrix Workspace offers a high-definition (HDX) virtualized experience that rivals that of a local PC, and: - Decreases your demand for bandwidth - Increases your user density per hosting server - Supports voice, video, and display—even 3D professional graphics!"
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Citrix Systems
Published 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
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zynapse, erp projects, data information, data management, governance, mdm, master data management, odm, business intelligence, database development
    
Zynapse
Published 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
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public sector, urbanization, traffic management
    
HERE Technologies
Published 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.
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data center, digital realty, energy efficient, build-to-suit, business intelligence, research
    
Digital Realty
Published 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
Published 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.
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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
Published 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
Published 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
Published 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
Published 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
Published 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.
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planning, contingency plan, testing, migration, preparation, project team, audit, closure
    
Schneider Electric
Published 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.
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intel, analytics, data, data analytics, data science
    
Intel
Published 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.
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ibm, integration, data volume, business technology, information, it management, knowledge management, data management
    
IBM
Published 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.
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enterprise, soa, technology, liaison, business, data integration, data architecture, it management, analytical applications, business intelligence, data quality
    
Liaison Technologies
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