er modeling

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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: 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.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
This paper explores the differences between three situations that appear on the surface to be very similar: a data attribute that may occur zero or one times, a data attribute that is optional, and a data attribute whose value may be unknown. It shows how each of these different situations is represented in Concept and Object Modeling Notation (COMN, pronounced “common”). The theory behind the analysis is explained in greater detail by three papers: Three-Valued Logic, A Systematic Solution to Handling Unknown Data in Databases, and An Approach to Representing Non-Applicable Data in Relational Databases.
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Ted Hills
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: 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: 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: 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
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.
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white paper, ca technologies, erwin, data, data management, data modeling, dataversity
    
CA Technologies
Published By: CMMI Institute     Published Date: Sep 03, 2014
To drive strategic insights that lead to competitive advantage, businesses must make the best and smartest use of today’s vast amount of data. To accomplish this, organizations need to apply a collaborative approach to optimizing their data assets. For organizations that seek to evaluate and improve their data management practices, CMMI® Institute has developed the Data Management Maturity (DMM)? model to bridge the perspective gap between business and IT. Download the white paper Why is Measurement of Data Management Maturity Important? to enable you to: - Empower your executives to make better and faster decisions using a strategic view of their data. - Achieve the elusive alignment and agreement between the business and IT - Create a clear path to increasing capabilities
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white paper, enterprise data management, data model, data modeling, data maturity model, cmmi institute
    
CMMI Institute
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: 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: IDERA     Published Date: Nov 07, 2017
Increasing dependence on enterprise-class applications has created a demand for centralizing organizational data using techniques such as Master Data Management (MDM). The development of a useful MDM environment is often complicated by a lack of shared organizational information and data modeling. In this paper, David Loshin explores some of the root causes that have influenced an organization’s development of a variety of data models, how that organic development has introduced potential inconsistency in structure and semantics, and how those inconsistencies complicate master data integration.
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IDERA
Published By: IDERA     Published Date: Nov 07, 2017
Data modeling is all about data definition but has a much wider impact on the data of your organization. Quality data definition impacts how data is produced and directly impacts how the data is or will be used throughout an organization. That means that we must proactively govern the process of how we define data, to establish a common understanding across the team. In this whitepaper, Robert Seiner describes how data modeling is a form of data governance and provides insights on the three actions of governing data.
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IDERA
Published By: DATAVERSITY     Published Date: Jan 21, 2013
This report examines the biggest challenges faced by data modelers at both quantitative and qualitative levels. It discusses the results of four different data modeling surveys in 2007, 2009, 2011, and 2012.
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data management
    
DATAVERSITY
Published By: Embarcadero     Published Date: Apr 02, 2014
IT professionals in organizations developing an enterprise data modeling program may feel overwhelmed at the scope and complexity of initiating new methods, tools, and techniques. Whether their organization is just starting out or experienced in enterprise data modeling efforts, there are certain pitfalls that can become obstacles to success. This paper looks at the benefits of an effective enterprise data modeling effort and discusses seven common mistakes that organizations can make in developing enterprise data models.
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Embarcadero
Published By: Embarcadero     Published Date: Apr 23, 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
Published By: Ted Hills     Published Date: Mar 29, 2016
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. Ted Hills has been active in the Information Technology industry since 1975. At LexisNexis, Ted co-leads the work of establishing enterprise data architecture standards and governance processes, working with data models and business and data definitions for both structured and unstructured data. His book, NoSQL and SQL Data Modeling, was recently released by Technics Publications (http://technicspub.com).
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Ted Hills
Published By: Oracle     Published Date: Jan 12, 2017
In this report, you will learn why: • Business stakeholders need better planning, insight, reporting and compliance. • Why Oracle is a leader in Enterprise Performance Management. • Modeling and management reporting are key differentiators as EPM moves rapidly to SaaS.
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Oracle
Published By: Salesforce     Published Date: Nov 19, 2015
Based on responses from more than 1,100 adults who currently have investments, the research found the wave of millennials (ages 18-34) entering the market is not only putting pressure on financial advisors to use newer technologies in managing their money, but also pushing even Gen Xers (ages 35-54) and baby boomers (ages 55+) toward more modern financial tools, such as modeling on mobile devices or online portfolio rebalancing.
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investing
    
Salesforce
Published By: CA Technologies EMEA     Published Date: Sep 11, 2018
Modern solutions like CA PPM continue to raise the bar above last-generation IT demand management tools, continuously providing new features to ease the burden of the PMO, the financial manager, the resource manager and the product manager. In the last few years, new vendors looking to exploit the large and increasingly influential project and portfolio management (PPM) market have developed modules that “snap” into their SaaS platforms. These vendors claim their tools are easy to install, easy to manage and save customers money. It sounds too good to be true. And for most organizations, it is. Carefully consider whether you need a PPM solution that is only capable of providing low-level functionality for the project manager, or if your organization could benefit from PPM technology that provides 360-degree optics across your organization, delivers actionable business intelligence and enables extensive modeling and forecasting capabilities to make data-driven business decisions.
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CA Technologies EMEA
Published By: CA Technologies EMEA     Published Date: Sep 13, 2018
Auf einen Blick Mitarbeiter von heute sind es gewohnt, privat digitale Technologien zu nutzen, die eine problemlose Kommunikation ermöglichen und die Abläufe des täglichen Lebens vereinfachen. Bedauerlicherweise können viele Unternehmenstools nicht Schritt halten. Die meisten Projekt- und Portfoliomanagementlösungen, die heute eingesetzt werden, bieten keine unkomplizierten Optionen, um alltägliche Aufgaben auszuführen. Sie ermöglichen keine kontextbasierte Kommunikation, mit der sich Probleme lösen lassen, und verfügen nicht über entscheidende Funktionalität. Dazu zählen z. B. die Möglichkeit, den Fokus auf relevante Daten einzuschränken, das Durchführen von Drilldowns, um zusätzliche Informationen zu erhalten, sowie die Vorhersage finanzieller Entwicklungen und das Modeling von Ergebnissen.
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CA Technologies EMEA
Published By: CA Technologies_Business_Automation     Published Date: Jun 29, 2018
In the 26-criteria evaluation of continuous delivery and release automation (CDRA) providers, we identified the 15 most significant — Atlassian, CA technologies, Chef Software, Clarive, CloudBees, electric Cloud, Flexagon, Hewlett packard enterprise (Hpe), IBM, Micro Focus, Microsoft, puppet, Red Hat, VMware, and Xebialabs — and researched, analyzed, and scored them. We focused on core features, including modeling, deploying, managing, governing, and visualizing pipelines, and on each vendor’s ability to match a strategy to these features. this report helps infrastructure and operations (I&o) professionals make the right choice when looking for CDRA solutions for their development and operations (Devops) automation.
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CA Technologies_Business_Automation
Published By: Vena Solutions     Published Date: Oct 29, 2018
Based on in-depth research and customer interviews, the annual Nucleus Research Value Matrix map out the corporate performance management (CPM) market landscape, evaluating vendors on a matrix contrasting usability and ease-of-use versus features and depth of functionality. Read or download the 2018 edition to uncover the most up-to-date CPM landscape, to find the best finance software solution for your needs, and to see why Vena led the pack in usability to land in the Leader quadrant for the third straight year.
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nucleus research, cpm matrix, cpm technology value, cpm technology matrix 2018, excel replacement, financial planning, budgeting, forecasting, financial modeling, business modeling, finance what-if scenarios, financial close and consolidation, risk and audit management, erp data integration systems, adaptive insights, hyperion, anaplan, prophix, vena solutions
    
Vena Solutions
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