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Published By: CA Technologies     Published Date: Oct 22, 2015
As the interest in managing information and enforcing corporate data management policies increases, data governance programs to manage data sets are becoming more and more vital to the business operation. However, in this rush for data governance programs, sometimes the true utility and importance of metadata can be missed. In this white paper, David Loshin of Knowledge Integrity, Inc. discusses the importance of data governance and the role of metadata management as a way to empower data governance and enforce data policies.
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white paper, metadata, data management, data modeling, david loshin, data governance, data governance strategy
    
CA Technologies
Published By: Dell Boomi     Published Date: May 29, 2013
Application and data integration doesn't have to be slow and expensive. New cloud-based integration platforms dramatically speed results and lower costs, as outlined in this new report by Bloor Research.
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Dell Boomi
Published By: Dell Boomi     Published Date: May 28, 2013
Application and data integration doesn't have to be slow and expensive. New cloud-based integration platforms dramatically speed results and lower costs, as outlined in this new report by Bloor Research.
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Dell Boomi
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: 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: Paxata     Published Date: Nov 29, 2016
Every organization looks for ways to reduce costs and run more efficiently. In fact, those are key drivers for the mainstream adoption of Hadoop and self-service BI tools. And while we can now collect and store more data than ever before, and we have enabled every information worker into a data-hungry analyst, not much consideration has been paid to the cost - including time and effort - of preparing data. Download this report to learn more about the hidden cost of data preparation.
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Paxata
Published By: Paxata     Published Date: Mar 21, 2017
Every organization looks for ways to reduce costs and run more efficiently. In fact, those are key drivers for the mainstream adoption of Hadoop and self-service BI tools. And while we can now collect and store more data than ever before, and we have enabled every information worker into a data-hungry analyst, not much consideration has been paid to the cost - including time and effort - of preparing data. Download this report to learn more about the hidden cost of data preparation.
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Paxata
Published By: Aerospike     Published Date: Jul 17, 2014
This whitepaper defines provides a brief technical overview of ACID support in Aerospike. It includes a definition of ACID (Atomicity, Consistency, Isolation, Durability), and an overview of the CAP Theorem, which postulates that only two of the three properties of consistency, availability, and partition tolerance can be guaranteed in a distributed system at a specific time. Although Aerospike is an AP system with a proven track record of 100% uptime, this paper describes Aerospike's unique approach to avoiding network partitioning to also ensure high consistency. In addition, the paper describes how Aerospike will give users the option to support a CP configuration with complete consistency in the presence of networking partitioning by reducing availability.
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whitepaper, data, data management, nosql, aerospike, acid
    
Aerospike
Published By: Pentaho     Published Date: Nov 03, 2016
While a whole ecosystem of tools has sprung up around Hadoop to handle and analyze data, many of them are specialized to just one part of a larger process. In order to fulfill the promise of Hadoop, organizations need to step back and take an end-to-end view of their analytic data pipelines.
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Pentaho
Published By: AT&T     Published Date: Sep 11, 2014
The age of Big Data is upon us. Storage costs are going down, and data analytics is becoming more capable and more user-friendly. Even your auto mechanic will be storing a petabyte of data soon. Big Data will give businesses new insights and help improve operations. With these new tools come questions about how to use them. But your mechanic knows more about fixing a transmission than developing a Hadoop cluster, and similar concerns hold true for larger enterprises. Businesses everywhere are looking for guidance.
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AT&T
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: Mar 21, 2017
AnalytiX DS Mapping Manager is an award winning platform that has enabled organizations worldwide to meet challenges, bring wide-spread collaboration, and put structure and governance in place, regardless of the size of their architecture, data, or user base. Scalable, adaptable, and value-added through its feature rich modules, it can make a difference as to whether you see BASEL III as a challenge or an opportunity.
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AnalytixDS
Published By: CapTech     Published Date: May 26, 2015
Big Data is the future of business. According to CloudTweaks.com, as much as 2.5 quintillion bytes of data are produced each day, with most of this data being captured by Big Data. With its ability to transfer all data sources all into one centralized place, Big Data provides opportunities, clearer visions, customer conversations and transactions. However, with the dazzling big promise of Big Data comes a potentially huge letdown. If this vast pool of information resources is not accessible or usable, it becomes useless. This paper examines strategies for building the most value into your Big Data system by enabling process controls to effectively mine, access and secure Big Data.
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big data, captech, data, data management, nosql
    
CapTech
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: 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: Experian     Published Date: Mar 12, 2018
Data is quickly becoming the currency of the emerging digital economy. As digital transformation efforts proliferate and become commonplace, data will take center stage as a critical driver of these initiatives. Organizations that are able to mobilize their data assets to power critical business initiatives will see a distinct advantage in the years to come. In fact, a majority of C-level executives (87%) believe that data has greatly disrupted their organization’s operations over the last 12 months. As the reliance on data deepens, the need for trustworthy and reliable data assets will become increasingly important. This year’s global study highlights several important issues and opportunities throughout the data management and data quality spaces. By discussing the latest advancements and challenges within our industry, we hope to empower all organizations to better leverage their data and to thrive in the digital economy.
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Experian
Published By: Experian     Published Date: Mar 12, 2018
Data is quickly becoming the currency of the emerging digital economy. As digital transformation efforts proliferate and become commonplace, data will take center stage as a critical driver of these initiatives. Organizations that are able to mobilize their data assets to power critical business initiatives will see a distinct advantage in the years to come. In fact, a majority of C-level executives (87%) believe that data has greatly disrupted their organization’s operations over the last 12 months. As the reliance on data deepens, the need for trustworthy and reliable data assets will become increasingly important. This year’s global study highlights several important issues and opportunities throughout the data management and data quality spaces. By discussing the latest advancements and challenges within our industry, we hope to empower all organizations to better leverage their data and to thrive in the digital economy.
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Experian
Published By: Experian     Published Date: Jun 01, 2018
Better data insight is the key to becoming a more informed, profitable company. Download this white paper to understand: -How to overcome challenges you may have with your data management strategy -What improvements you should make that will make the biggest impact -The importance of being proactive rather than reactive to data quality issues
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Experian
Published By: Snowflake Computing     Published Date: Apr 19, 2016
Data warehouse as a service brings scalability and flexibility to organizations seeking to deliver data to all users and systems that need to analyze it. The ability to access and analyze data is the critical foundational element for competing in new and old industries alike. Yet, a recent survey of IT executives finds that most are still struggling— and frustrated — with widely used data analytics tools. Find out what your peers are saying, and see how your data analytics environment compares.
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Snowflake Computing
Published By: IDERA     Published Date: Feb 06, 2017
Data modeling can provide tangible economic benefits, which are best shown by quantifying the traditional benefits of data modeling. In this whitepaper, Tom Haughey discusses how to calculate the return on investment (ROI) of data modeling by assessing the economic value of real data modeling benefits, such as improved requirements definition, reduced maintenance, accelerated development, improved data quality and reuse of existing data assets. Download this whitepaper to learn how to: - Describe the value proposition of data modeling - Assess the economic value of data modeling benefits - Learn three methods to calculate data modeling ROI
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IDERA
Published By: Innovative Systems     Published Date: Oct 26, 2017
Even after investing significant time and resources implementing a data quality solution, many enterprises find that their data does not effectively support their goals. This white paper shows how to get the most out of your data quality solution by tailoring it to support your business goals.
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Innovative Systems
Published By: Infogix     Published Date: May 04, 2018
Over the last few years, the term “data governance” has evolved to more prominence, concurrently with big data. While organizations understand the need for governance around big data, the implementation of a successful data governance solution continues to remain elusive as organizations grapple with what exactly data governance is. This whitepaper provides a concise definition of data governance and offers some key considerations for a successful data governance solution.
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Infogix
Published By: Collibra     Published Date: Jul 09, 2018
Data governance can be a game changer when it comes to modern business. But leveraging data for strategic objectives is easier said than done. This new report, developed by the Economist Intelligence Unit, explores the challenges and opportunities for data governance both globally and across industries. Based on a survey of over 500 business executives and complemented by in-depth interviews, this report reveals: • What's working in data governance — and what's not • Why organizations must shift their thinking to data governance on offense • How to overcome barriers to better data governance
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Collibra
Published By: Syncsort     Published Date: Jul 17, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
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