mpl

Results 1 - 25 of 11592Sort Results By: Published Date | Title | Company Name
Published By: Denodo     Published Date: Mar 01, 2019
With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources. Data virtualization transcends the limitations of traditional data integration techniques such as ETL by delivering a simplified, unified, and integrated view of trusted business data. Learn how you can: • Conquer siloed data in the enterprise • Integrate all data sources and types • Cope with regulatory requirements • Deliver big data solutions that work • Take the pain out of cloud adoption • Drive digital transformation
Tags : 
    
Denodo
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
Tags : 
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.
Tags : 
    
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.
Tags : 
data, data management, analytics, business intelligence, data science
    
DATAVERSITY
Published By: Melissa Data     Published Date: Jan 31, 2019
Noted SQL Server MVP and founder/editor of SSWUG.org Stephen Wynkoop shares his take on the challenge to achieve quality data, and the importance of the "Golden Record" to an effective data quality regiment. Achieving the Golden Record involves collapsing duplicate records into a single version of the truth - the one single customer view (SCV). There are different approaches to achieving the Golden Record. Wynkoop explores Melissa's unique approach that takes into consideration the actual quality of the contact data as the basis of survivorship. Learn How: • Poor data quality negatively affects your business • Different data quality implementations in SQL Server • Melissa's unique approach to achieving the Golden Record based on a data quality score
Tags : 
    
Melissa Data
Published By: Ted Hills     Published Date: Mar 08, 2017
This document provides a complete reference for the Concept and Object Modeling Notation (COMN, pronounced “common”), release 1.1. The book NoSQL and SQL Data Modeling (Technics Publications, 2016) reflects release 1.0 of COMN. This is a reference, not a tutorial. This document is designed to support a quick check of how to draw or notate something in COMN.
Tags : 
    
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.
Tags : 
    
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.
Tags : 
    
Ted Hills
Published By: Innovative Systems     Published Date: Feb 21, 2019
From years of data quality initiatives, hundreds of case studies, and research by industry experts, a number of common data quality success factors have emerged. This paper discusses key characteristics of data quality initiatives and provides actionable guidelines to help make your project a success, from conception through implementation and tracking your ROI. Readers will learn how to: • Quantify the effect of poor data quality on the organization • Prioritize projects for faster ROI • Gain buy-in, from employees through senior management
Tags : 
    
Innovative Systems
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.
Tags : 
data architecture, data, data management, business intelligence, data warehousing
    
CData
Published By: Denodo     Published Date: Feb 27, 2019
Organizations continue to struggle with integrating data quickly enough to support the needs of business stakeholders, who need integrated data faster and faster with each passing day. Traditional data integration technologies have not been able to solve the fundamental problem, as they deliver data in scheduled batches, and cannot support many of today’s rich and complex data types. Data virtualization is a modern data integration approach that is already meeting today’s data integration challenges, providing the foundation for data integration in the future. Download this whitepaper to learn more about: The fundamental challenge for organizations today. Why traditional solutions fall short. Why data virtualization is the core solution.
Tags : 
    
Denodo
Published By: Tamr, Inc.     Published Date: Feb 08, 2019
Traditional data management practices, such as master data management (MDM), have been around for decades – as have the approaches vendors take in developing these capabilities. And they were well-equipped for the problem at hand: managing data at modest size and complexity. However, as enterprises mature and start to view their data assets as a source of competitive advantage, new methods to managing enterprise data become desirable. Enterprises now need approaches to data management that can solve critical issues around speed and scale in an increasingly complex data environment. This paper explores how data curation technology can be used to solve data mastering challenges at scale.
Tags : 
    
Tamr, Inc.
Published By: TigerGraph     Published Date: Mar 13, 2019
As the world’s first and only Native Parallel Graph (NPG) system, TigerGraph is a complete, distributed, graph analytics platform supporting web-scale data analytics in real time. The TigerGraph NPG is built around both local storage and computation, supports real-time graph updates, and works like a parallel computation engine. Download our white paper to learn of TigerGraph's unique advantages.
Tags : 
    
TigerGraph
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.
Tags : 
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.
Tags : 
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.
Tags : 
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: Collibra     Published Date: Dec 19, 2017
The GDPR is a complex regulation. And on the path to compliance, there will be unanticipated roadblocks. The good news is, you can find your way through them.
Tags : 
    
Collibra
Published By: Databricks     Published Date: Sep 13, 2018
Learn how to get started with Apache Spark™ Apache Spark™’s ability to speed analytic applications by orders of magnitude, its versatility, and ease of use are quickly winning the market. With Spark’s appeal to developers, end users, and integrators to solve complex data problems at scale, it is now the most active open source project with the big data community. With rapid adoption by enterprises across a wide range of industries, Spark has been deployed at massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. If you are a developer or data scientist interested in big data, learn how Spark may be the tool for you. Databricks is happy to present this ebook as a practical introduction to Spark. Download this ebook to learn: • Spark’s basic architecture • Why Spark is a popular choice for data analytics • What tools and features are available • How to get started right away through interactive sample code
Tags : 
    
Databricks
Published By: Adlib Software     Published Date: Feb 09, 2018
In financial services, contracts often exist in highly dispersed formats, with jurisdictional complexities and risk aspects that change over time—resulting in high levels of risk. Learn how to transform contracts into actionable, defensible assets with a robust contract intelligence solution.
Tags : 
    
Adlib Software
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.
Tags : 
    
SAP
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.
Tags : 
    
Adaptive
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.
Tags : 
data, data management, metadata, enterprise information management, data modeling, embarcadero
    
Embarcadero
Published By: MarkLogic     Published Date: Aug 04, 2014
The Age of Information and the associated growth of the World Wide Web has brought with it a new problem: how to actually make sense of all the information available. The overarching goal of the Semantic Web is to change that. Semantic Web technologies accomplish this goal by providing a universal framework to describe and link data so that it can be better understood and searched holistically, allowing both people and computers to see and discover relationships in the data. Today, organizations are leveraging the power of the Semantic Web to aggregate and link disparate data, improve search navigation, provide holistic search and discovery, dynamically publish content, and complete ETL processes faster. Read this white paper to gain insight into why Semantics is important, understand how Semantics works, and see examples of Semantics in practice.
Tags : 
data, data management, whitepaper, marklogic, semantic, semantic technology, nosql, database, semantic web, big data
    
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?
Tags : 
data management, data, reference data, reference data management, top quadrant, malcolm chisholm
    
TopQuadrant
Published By: CA Technologies     Published Date: Apr 24, 2013
This white paper by industry expert Alec Sharp illustrates these points and provides specific guidelines and techniques for a business-oriented approach to data modeling. Examples demonstrate how business professionals.
Tags : 
white paper, ca technologies, erwin, data, data management, data modeling, dataversity
    
CA Technologies
Start   Previous   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15    Next    End
Search      

Add Research

Get your company's research in the hands of targeted business professionals.

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept