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Published By: DATAVERSITY     Published Date: Oct 04, 2016
This report evaluates each question posed in a recent survey and provides subsequent analysis in a detailed format that includes the most noteworthy statistics, direct comments from survey respondents, and the influence on the industry as a whole. It seeks to present readers with a thorough review of the state of Metadata Management as it exists today.
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DATAVERSITY
Published By: Ted Hills     Published Date: Mar 08, 2017
NoSQL database management systems give us the opportunity to store our data according to more than one data storage model, but our entity-relationship data modeling notations are stuck in SQL land. Is there any need to model schema-less databases, and is it even possible? In this short white paper, Ted Hills examines these questions in light of a recent paper from MarkLogic on the hybrid data model.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Much has been written and debated about the use of SQL NULLs to represent unknown values, and the possible use of three-valued logic. However, there has never been a systematic application of any three-valued logic to use in the logical expressions of computer programs. This paper lays the foundation for a systematic application of three-valued logic to one of the two problems inadequately addressed by SQL NULLs.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing unknown data, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing that data are not applicable, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
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: DATAVERSITY     Published Date: Dec 27, 2013
There are actually many elements of such a vision that are working together. ACID and NoSQL are not the antagonists they were once thought to be; NoSQL works well under a BASE model, but also some of the innovative NoSQL systems fully conform to ACID requirements. Database engineers have puzzled out how to get non-relational systems to work within an environment that demands high availability, scalability, with differing levels of recovery and partition tolerance. BASE is still a leading innovation that is wedded to the NoSQL model, and the evolution of both together is harmonious. But that doesn’t mean they always have to be in partnership; there are several options. So while the opening anecdote is true in many cases, organizations that need more diverse possibilities can move into the commercial arena and get the specific option that works best for them. This paper is sponsored by: MarkLogic.
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nosql, database, acid v base, white paper
    
DATAVERSITY
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: ASG     Published Date: Apr 02, 2014
This Case Study focuses on a highly successful data lineage project between ASG Software Solutions and a major global financial institution. The initial project which began in 2011 with the primary goal of achieving greater control, awareness, and ownership over the institution’s data assets due to new regulatory and federal audit controls. As the project progressed and the positive relationship between ASG and the Bank deepened, all stakeholders involved began to see much broader potential for the entire project than originally envisioned.
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metadata, data, data management, white paper, case study
    
ASG
Published By: CA Technologies     Published Date: Feb 25, 2016
As combinations of both internal and externally-imposed business policies imply dependencies on managed data artifacts, organizations are increasingly instituting data governance programs to implement processes for ensuring compliance with business expectations. One fundamental aspect of data governance involves practical application of business rules to data assets based on data elements and their assigned values. Yet despite the intent of harmonizing data element definitions and resolution of data semantics and valid reference values, most organizations rarely have complete visibility into the metadata associated with enterprise data assets.
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CA Technologies
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: 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: Skytree     Published Date: Nov 23, 2014
Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. In this brief, discover how you can use machine learning to analyze both unstructured text data and semi- structured log data, providing you with the insights needed to achieve your business goals.
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log data, machine learning, natural language, nlp, natural language processing, skytree, unstructured data, semi-structured data, data analysis
    
Skytree
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: Ted Hills     Published Date: Jul 02, 2015
Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. Furthermore, E-R notation has some limitations even for SQL database design. This article shows how a new notation, the Conceptual and Objective Modeling (COM) notation, is able to represent NOSQL designs that are beyond the reach of E-R notation. At the end, it gives a peek into the tutorial workshop to be given at the 2015 NOSQL Conference in San Jose, CA, US, in August, which will provide opportunities to apply COM notation to practical problems.
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nosql, sql, data modeling, data model, er modeling, entity relationship, database, relational, dbms, schema-less, xml, conceptual, logical, physical
    
Ted Hills
Published By: Trillium Software     Published Date: Apr 10, 2017
For the 11th consecutive year, the Gartner Magic Quadrant for Data Quality Tools1 research report positions Trillium Software as a leader in the Data Quality Software industry. Data Quality is vital to ensuring trust in your data-driven, decision making business processes. Confidence is the result of a well thought out and executed data quality management strategy and is critical to remaining competitive in a rapidly and ever-changing business world. The 2016 Gartner Magic Quadrant for Data Quality Tools report is a valuable reference, providing the latest insights into the strengths and cautions of leading vendors. Access the report to learn how a leading data quality solution can help you achieve your long-term strategic objectives.
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Trillium Software
Published By: Reltio     Published Date: May 21, 2018
Effective May 25, 2018, General Data Protection Regulation (GDPR) will represent the most rigorous data protection regulation ever. Complying is not optional, and the penalties are very high. As companies scurry for total compliance, it makes sense to pause, assess, and use this opportunity not only for compliance but for managing customer data efficiently and gainfully. Reltio proposes ten simple steps to ensure your data management strategy is ready for GDPR, not just for assured compliance but going beyond and enabling better customer experiences and building business competence.
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Reltio
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: 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: 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: 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
Published By: Syncsort     Published Date: Oct 25, 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
Published By: Octopai     Published Date: Sep 01, 2018
For many BI professionals, every task can feel like MISSION IMPOSSIBLE. All the manual mapping required to sort out inconsistencies in data and the lack of tools to simplify and shorten the process of finding and understanding data leaves BI groups frustrated and slows down the business. This whitepaper examines the revolutionary impact of automation on the cumbersome manual processes that have been dragging BI down for so long. • Data correction vs process correction • Root-cause analysis with data lineage: reverse-tracing the data flow • Data quality rules and data controls • Automated data lineage mapping
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Octopai
Published By: Deloitte Process Robotics     Published Date: May 04, 2018
Deloitte Process Robotics (DPR) solutions use a lightweight approach to train ‘bots’ that automate repetitive tasks of medium complexity without changes to existing process or IT infrastructure. With ever-growing repositories of unanalyzed and underutilized data, organizations require highly flexible and scalable enterprise data management (EDM) processes, DPR makes it possible for organizations to adopt comprehensive yet affordable EDM processes in the Big Data Era, helping organizations transform data into strategic assets through the automation of capabilities.
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Deloitte Process Robotics
Published By: Erwin     Published Date: Sep 13, 2018
Do you know what data you have, where it is and how to wring all the possible value from it? By harmonizing your data management and data governance efforts, you can accelerate your time to data preparation, data visibility and data-driven insights. Then you’ll know how to get the results you need.
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Erwin
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