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Published By: DATAVERSITY     Published Date: Jul 24, 2014
Will the “programmable era” of computers be replaced by Cognitive Computing systems which can learn from interactions and reason through dynamic experience just like humans? With rapidly increasing volumes of Big Data, there is a compelling need for smarter machines to organize data faster, make better sense of it, discover insights, then learn, adapt, and improve over time without direct programming. This paper is sponsored by: Cognitive Scale.
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data, data management, cognitive computing, machine learning, artificial intelligence, research paper
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Nov 05, 2014
Ask any CEO if they want to better leverage their data assets to drive growth, revenues, and productivity, their answer will most likely be “yes, of course.” Ask many of them what that means or how they will do it and their answers will be as disparate as most enterprise’s data strategies. To successfully control, utilize, analyze, and store the vast amounts of data flowing through organization’s today, an enterprise-wide approach is necessary. The Chief Data Officer (CDO) is the newest member of the executive suite in many organizations worldwide. Their task is to develop and implement the strategies needed to harness the value of an enterprise’s data, while working alongside the CEO, CIO, CTO, and other executives. They are the vital “data” bridge between business and IT. This paper is sponsored by: Paxata and CA Technologies
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chief data officer, cdo, data, data management, research paper, dataversity
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Jul 06, 2015
The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across-the-board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. The time-honored techniques for data modeling are being rewritten, reworked, and modified in a multitude of different ways, often wholly dependent on the NoSQL platform under development. The research report analyzes a 2015 DATAVERSITY® survey titled “Modeling NoSQL.” The survey examined a number of crucial issues within the NoSQL world today, with focus on data modeling in particular.
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DATAVERSITY
Published By: DATAVERSITY     Published Date: Nov 20, 2015
The competitive advantages realized from a dependable Business Intelligence and Analytics (BI/A) are well documented. Everything from reduced business costs and increased customer retention to better decision making and the ability to forecast opportunities have been observed outcomes in response to such programs. The implementation of such a program remains a necessity for any growing or mature enterprise. The establishment of a comprehensive BI/A program that includes traditional Descriptive Analytics along with next generation categories such as Predictive or Prescriptive Analytics is indispensable for business success.
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data, data management, analytics, business intelligence, data science
    
DATAVERSITY
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
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: 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
Published By: TD Bank Group     Published Date: Aug 10, 2018
This paper examines whether blockchain distributed ledger technology could improve the management of trusted information, specifically considering data quality. Improvement was determined by considering the impact of a distributed ledger as an authoritative source in TD Bank Group's Enterprise Data Quality Management Process versus the use of standard authoritative sources such as databases and files. Distributed ledger technology is not expected, or proven, to result in a change in the Data Quality Management process. Our analysis focused on execution advantages possible due to distributed ledger properties that make it an attractive resource for data quality management (DQM).
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TD Bank Group
Published By: DATAVERSITY     Published Date: Feb 27, 2013
In its most basic definition, unstructured data simply means any form of data that does not easily fit into a relational model or a set of database tables.
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white paper, dataversity, unstructured data, enterprise data management, data, data management
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Jun 14, 2013
This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. “Data Governance” is a term fraught with nuance, misunderstanding, myriad opinions, and fear. It is often enough to keep Data Stewards and senior executives awake late into the night. The modern enterprise needs reliable and sustainable control over its technological systems, business processes, and data assets. Such control is tantamount to competitive success in an ever-changing marketplace driven by the exponential growth of data, mobile computing, social networking, the need for real-time analytics and reporting mechanisms, and increasing regulatory compliance requirements. Data Governance can enhance and buttress (or resuscitate, if needed) the strategic and tactical business drivers every enterprise needs for market success. This paper is sponsored by: ASG, DGPO and DebTech International.
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data, data management, data governance, data steward, dataversity, research paper
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Nov 11, 2013
This report investigates the level of Information Architecture (IA) implementation and usage at the enterprise level. The primary support for the report is an analysis of a 2013 DATAVERSITY™ survey on Data and Information Architecture. This paper is sponsored by: HP, Vertica, Denodo, Embarcadero and CA Technologies.
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information architecture, data architecture, white paper, mdm, master data management, data, data management, enterprise information management, enterprise data management, data virtualization, metadata, data modeling, research paper, survey, data integration
    
DATAVERSITY
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: DATAVERSITY     Published Date: Oct 12, 2017
The foundation of this report is a survey conducted by DATAVERSITY® that included a range of different question types and topics on the current state of Data Architecture. The report evaluates the topic through a discussion and analysis of each presented survey question, as well as a deeper examination of the present and future trends.
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data architecture, data, data management
    
DATAVERSITY
Published By: SAP     Published Date: May 19, 2016
SAP® solutions for enterprise information management (EIM) support the critical abilities to architect, integrate, improve, manage, associate, and archive all information. By effectively managing enterprise information, your organization can improve its business outcomes. You can better understand and retain customers, work better with suppliers, achieve compliance while controlling risk, and provide internal transparency to drive operational and strategic decisions.
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SAP
Published By: First San Francisco Partners     Published Date: Sep 03, 2014
This white paper discusses how awareness, ownership and accountability are key to activating and engaging people to adopt new data governance initiatives and the related policies and processes. It provides you with information on how your organization can ensure that the data stewards adopt the new data governance policies and processes.
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data, data management, data governance, data governance program
    
First San Francisco Partners
Published By: First San Francisco Partners     Published Date: Oct 29, 2015
One of the biggest challenges in a data management initiative is aligning different and sometimes competing organizations to work towards the same long-term vision. That is why a proactive approach to aligning the organization around a common goal and plan is critical when launching a data management program.
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First San Francisco Partners
Published By: First San Francisco Partners     Published Date: Mar 03, 2017
Getting people successfully through a new enterprise information management (EIM) initiative requires a focus on the change’s impact to your organization’s data culture, processes and policies.
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First San Francisco Partners
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: Adaptive     Published Date: May 10, 2017
Enterprise metadata management and data quality management are two important pillars of successful enterprise data management for any organization. A well implemented enterprise metadata management platform can enable a successful data quality management at the enterprise level. This paper describes in detail an approach to integrate data quality and metadata management leveraging the Adaptive Metadata Manager platform. It explains the various levels of integrations and the benefits associated with each.
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Adaptive
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: Embarcadero     Published Date: Oct 21, 2014
Metadata defines the structure of data in files and databases, providing detailed information about entities and objects. In this white paper, Dr. Robin Bloor and Rebecca Jowiak of The Bloor Group discuss the value of metadata and the importance of organizing it well, which enables you to: - Collaborate on metadata across your organization - Manage disparate data sources and definitions - Establish an enterprise glossary of business definitions and data elements - Improve communication between teams
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data, data management, enterprise data management, enterprise information management, metadata, robin bloor, rebecca jozwiak, embarcadero
    
Embarcadero
Published By: Embarcadero     Published Date: Jan 23, 2015
There are multiple considerations for collaborating on metadata within an organization, and you need a good metadata strategy to define and manage the right processes for a successful implementation. In this white paper, David Loshin describes how to enhance enterprise knowledge sharing by using collaborative metadata for structure, content, and semantics.
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data, data management, metadata, enterprise information management, data modeling, embarcadero
    
Embarcadero
Published By: Embarcadero     Published Date: Apr 29, 2015
Everything about data has changed, but that only means that data models are even more essential to understanding that data so that businesses can know what it means. As development methodologies change to incorporate Agile workflows, data architects must adapt to ensure models stay relevant and accurate. This whitepaper describes key requirements for Agile data modeling and shows how ER/Studio supports this methodology.
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data, data management, data modeling, agile, agile data modeling, it management
    
Embarcadero
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