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Published By: CData     Published Date: Jan 04, 2019
The growth of NoSQL continues to accelerate as the industry is increasingly forced to develop new and more specialized data structures to deal with the explosion of application and device data. At the same time, new data products for BI, Analytics, Reporting, Data Warehousing, AI, and Machine Learning continue along a similar growth trajectory. Enabling interoperability between applications and data sources, each with a unique interface and value proposition, is a tremendous challenge. This paper discusses a variety of mapping and flattening techniques, and continues with examples that highlight performance and usability differences between approaches.
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data architecture, data, data management, business intelligence, data warehousing
    
CData
Published By: Stardog Union     Published Date: Jul 27, 2018
When enterprises consider the benefits of data analysis, what's often overlooked is the challenge of data variety, and that most successful outcomes are driven by it. Yet businesses are still struggling with how to query distributed, heterogeneous data using a unified data model. Fortunately, Knowledge Graphs provide a schema flexible solution based on modular, extensible data models that evolve over time to create a truly unified solution. How is this possible? Download and discover: • Why businesses should organize information using nodes and edges instead of rows, columns and tables • Why schema free and schema rigid solutions eventually prove to be impractical • The three categories of data diversity including semantic and structural variety
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Stardog Union
Published By: Couchbase     Published Date: Jul 15, 2013
NoSQL database technology is increasingly chosen as viable alternative to relational databases, particularly for interactive web applications. Developers accustomed to the RDBMS structure and data models need to change their approach when transitioning to NoSQL. Download this white paper to learn about the main challenges that motivates the need for NoSQL, the differences between relational databases and distributed document-oriented databases, the key steps to perform document modeling in NoSQL databases, and how to handle concurrency, scaling and multiple-place updates in a non-relational database.
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white paper, database, nosql, couchbase
    
Couchbase
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: Basho     Published Date: Sep 30, 2016
The Internet of Things (IoT) or the Internet of Everything is changing the way companies interact with their customers and manage their data. These connected devices generate high volume time series data that can be created in milliseconds. This fast growth of IoT data and other time series data is producing challenges for enterprise applications where data must be collected, saved, and analyzed in the blink of an eye. Your application needs a database built to uniquely handle time series data to ensure your data is continuously available and accurate.Learn about the only NoSQL database optimized for IoT and Time Series data in this technical overview. Riak TS stores and analyzes massive amounts of data and is designed to be faster than Cassandra.
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Basho
Published By: AnalytixDS     Published Date: Feb 28, 2015
With future business intelligence solutions clearly evolving from data that comes from highly efficient and well behaved systems, to data that comes from the extended enterprise where data is not necessarily so well structured and behaved - Organizations are forced into a more collaborative mode of operation with their core infrastructure being adapted from the consumer space, and to the extent possible, conformed to their existing repositories. This whitepaper attempts to address various challenges consumers face while managing enormous data sets within the context of this complex scenario. Further, we’ll try to answer the question: Is Big Data Governance really that different from traditional data governance initiatives? Finally, we’ll see how AnalytiX™ Mapping Manager™ can help organizations accelerate the development and deployment of a successful Big Data/ Business Intelligence platform and accelerate delivery of all sorts of data – structured, semi-structured as well as unstruc
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big data, big data governance, data governance, analytixds
    
AnalytixDS
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: 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: 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: CloverETL     Published Date: Nov 24, 2017
The volume of data is increasing by 40% per year (Source: IDC). In addition, the structure and quality of data differs vastly with a growing number of data sources. More agile ways of working with data are required. This whitepaper discusses the vast options available for managing and storing data using data architectures, and offers use cases for each architecture. Furthermore, the whitepaper explores the benefits, drawbacks and challenges of each data architecture and commonly used practices for building these architectures.
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CloverETL
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: Converseon     Published Date: Apr 02, 2018
Separating signals from noisy social listening data has long been a problem for data scientists. Poor precision due to slag, sarcasm and implicit meaning has often made it too challenging to effectively model. Today, however, new approaches that leverage active machine learning are rapidly over taking aging rules-based techniques and opening up use of this data in new and important ways. This paper provides some detail on the evolution of text analysis including current best practices and how AI can be used by data scientists to use this data for meaningful analysis.
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Converseon
Published By: Datawatch     Published Date: Apr 06, 2018
Enterprises are focusing on becoming ever more data-driven, meaning that it is simply unacceptable to allow data to go to waste. Yet, as the amount of data businesses collect and control continues to increase exponentially, many organizations are failing to derive enough business value from their data. Companies are feeling the pressure to extract maximum value from all of their data, both defensive and offensive. Defensive analytics are the “plumbing aspects” of data management that must be captured to mitigate risk and establish a basic understanding of business performance. Offensive analytics build on defensive analytics and support overarching business objectives, strategic initiatives and long-term goals using predictive models. In this whitepaper, you will learn how to address many challenges, including streamlining operational reporting, delivering insight and providing a single, unified platform for everyone.
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Datawatch
Published By: Collibra     Published Date: Jul 02, 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: MarkLogic     Published Date: Sep 01, 2015
Organizations face a growing inability to handle the massive volumes of disparate, varied, and changing data with the relational databases that have been relied on for the past three decades. This white paper provides a detailed discussion of the key reasons why relational databases are ill-suited to address the challenges with today’s data, and an overview of how new kinds of databases solve those challenges.
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MarkLogic
Published By: TopQuadrant     Published Date: Jul 18, 2016
With information streaming in from more varied sources and at a faster pace than ever before, organizations are having an increasingly difficult time deriving accurate meaning from their data. Data governance systems that were once able to organize and process enterprise information are becoming too slow and limited.   Semantic information management makes it easier to reconcile data from different sources by compiling and organizing information about that data, its metadata. By connecting all kinds of data and metadata in a more accessible way, semantic information systems empower users, data stewards and analysts to unlock and use the true meaning and value of their organization’s data.     Learn more about the challenges in the evolving data landscape and how a semantic approach can help.
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TopQuadrant
Published By: TopQuadrant     Published Date: Aug 01, 2016
With information streaming in from more varied sources and at a faster pace than ever before, organizations are having an increasingly difficult time deriving accurate meaning from their data. Data governance systems that were once able to organize and process enterprise information are becoming too slow and limited. Semantic information management makes it easier to reconcile data from different sources by compiling and organizing information about that data, its metadata. By connecting all kinds of data and metadata in a more accessible way, semantic information systems empower users, data stewards and analysts to unlock and use the true meaning and value of their organization’s data. Learn more about the challenges in the evolving data landscape and how a semantic approach can help.
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TopQuadrant
Published By: Experian     Published Date: Nov 03, 2015
It is critically important for businesses today to leverage data as a strategic asset, instead of allowing it to become an obstacle. As more and more organizations are trying to make smarter, data-driven decisions, they are discovering that their data management strategy may not be as mature as it needs to be. One major shift we are seeing is the implementation of a Chief Data Officer. Experian Data Quality recently conducted a research study of more than 250 Chief Information Officers (CIOs) and Chief Data Officers (CDOs) in the US about their data management practices, and the explosion of the CDO role. Key insights in the report include: - Tips for overcoming typical data challenges within your organization - The changing data management landscape and its affect on the CIO - The new and growing need for the CDO - And more!
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Experian
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