business model

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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: 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: 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: 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: Cambridge Semantics     Published Date: Mar 13, 2015
As the quantity and diversity of relevant data grows within and outside the enterprise, how can IT easily deploy secure governed solutions that allow business users to identify, extract, link together and derive value from the right data at the right time, at big data scale, while keeping up with ever changing business needs? Smart Enterprise Data Management (Smart EDM) is new, sensible paradigm for managing enterprise data. Anzo Smart Data solutions allow IT departments and their business users to quickly and flexibly access all of their diverse data. Based upon graph data models and Semantic data standards, Anzo enables users to easily perform advanced data management and analytics through the lens of their business at a fraction of the time and cost of traditional approaches, while adhering to the governance and security required by enterprise IT groups. Download this whitepaper to learn more.
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enterprise data management, data governance, data integration, cambridge semantics
    
Cambridge Semantics
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: CMMI Institute     Published Date: Mar 21, 2016
CMMI Institute®’s Data Management Maturity (DMM)SM framework enables organizations to improve data management practices across the full spectrum of their business. It is a unique, comprehensive reference model that provides organizations with a standard set of best practices to assess their capabilities, strengthen their data management program, and develop a custom roadmap for improvements that align with their business goals.
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CMMI Institute
Published By: Neo Technology     Published Date: Jun 28, 2015
The future of Master Data Management is deriving value from data relationships which reveal more data stories that become more and more important to competitive advantage as we enter into the future of data and business analytics. MDM will be about supplying consistent, meaningful views of master data and being able to unify data into one location, especially to optimize for query performance and data fit. Graph databases offer exactly that type of data/performance fit. Use data relationships to unlock real business value in MDM: - Graphs can easily model both hierarchical and non-hierarchical master data - The logical model IS the physical model making it easier for business users to visualize data relationships - Deliver insights in real-time from data relationships in your master data - Stay ahead of the business with faster development Download and read the white paper Your Master Data Is a Graph: Are You Ready? to learn why your master data is a graph and how graph databases like Neo4j are the best technologies for MDM.
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database, nosql, graph database, big data, master data management, mdm
    
Neo Technology
Published By: Reltio     Published Date: May 22, 2018
"Forrester's research uncovered a market in which Reltio [and other companies] lead the pack,” the Forrester Wave Master Data Management states. "Leaders demonstrated extensive and MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.” Reltio executes the vision for next-generation MDM by converging trusted data management with business insight solutions at scale and in the cloud. Machine learning and graph technology capabilities enable a contextual data model while also maintaining temporal and lineage changes of the master data.
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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: 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: Semarchy     Published Date: Aug 18, 2016
David Loshin reexamines the way we ingest, manage, consume, and transform data into actionable information and intelligence. Read how this industry expert makes the case for data governance with an unconventional business-first focus. The conventional wisdom on data governance proposes hierarchies, operating models, and processes for data policy definition and implementation. Unfortunately, poorly-designed and minimally-planned data governance processes are ineffective because they are bureaucratic and overwhelming. This is especially true when processes are imposed by fiat, take a long time, and don't result in any short-term improvement in information value. But proper data governance is a critical success factor for master data management! In this paper, we examine the motivations for coupling data governance with master data management and consider how to evolve data policies and processes to position master data management for success.
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Semarchy
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: Hewlett Packard Enterprise     Published Date: Jan 31, 2019
"Extracting value from data is central to the digital transformation required for businesses to succeed in the decades to come. Buried in data are insights that reveals what your customers need and how they want to receive it, how sales, manufacturing, distribution, and other aspects of business operations are functioning, what risks are arising to threaten the business, and more. That insight empowers your businesses to reach new customers, develop and deliver new products, to operate more efficiently and more effectively, and even to develop new business models. "
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Hewlett Packard Enterprise
Published By: Dell EMC     Published Date: Feb 14, 2019
Your organization has a small IT team, but you can’t ignore the technology challenges facing your growing business today. You are expected to drive digital transformation, address the needs of a changing workforce and increase business agility. This whitepaper discusses the benefits IT decision-makers at midmarket organizations can achieve by transitioning to a professional support service model that helps them choose the right devices to maximize workforce productivity—and then manage those devices throughout their lifecycle. Read this report from Dell and Intel® Intel Inside®. Powerful Productivity Outside.
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Dell EMC
Published By: IBM APAC     Published Date: Mar 06, 2019
The Forrester Study on cost savings and business benefits enabled by Watson Studio and Watson Knowledge Catalog. Watson Studio provides a suite of tools for data scientists, application developers, and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy machine learning models at scale. The Forrester provides readers a framework to evaluate the potential financial impact of the Watson Studio and Watson Knowledge Catalog investment on their organizations.
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IBM APAC
Published By: Genesys     Published Date: Feb 12, 2019
Everyone says they’re “in the cloud,” but most technology leaders would agree that not all clouds are created equal. When evaluating a cloud contact centre solution for your business, it’s important to understand the difference between a true Cloud 2.0 application and traditional software, including which features to look for and why those features are important. Download this eBook and learn: How a true Cloud 2.0 model is built to provide levels of reliability, scalability, flexibility and security that that far exceed those of previous generations The benefits of utilising a platform built on microservices architecture How to take your business to the next level with a built to scale cloud contact centre platform
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Genesys
Published By: Oracle     Published Date: Mar 21, 2019
A new culture of innovation is sweeping through the business world. In this webinar, IDC’s chief analyst and Oracle’s VP of digital innovation show how Finance can take a strategic lead. Key topics discussed include: • How to accelerate insight-gathering • Innovating future digital business models • Using AI to automate – and create better business processes Watch webinar
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Oracle
Published By: Oracle     Published Date: Mar 13, 2019
A new culture of innovation is sweeping through the business world. In this webinar, IDC’s chief analyst and Oracle’s VP of digital innovation show how Finance can take a strategic lead. Key topics discussed include: • How to accelerate insight-gathering • Innovating future digital business models • Using AI to automate – and create better business processes Watch webinar
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Oracle
Published By: TARGUSinfo.     Published Date: May 20, 2010
The arrival of 3rd party data is changing the landscape of online targeting. There is an influx of companies offering these services. But, what works with your business model? How do you evaluate the options? THE QUESTIONS YOU NEED TO KNOW AND WHY: • Is there high fidelity data? • Is the provided data stable? • Is your partner built to last? • Does your partner use a transparent methodology? Download our guide to evaluating data providers and find out the importance and relevance of these terms and conditions.
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targus info
    
TARGUSinfo.
Published By: SAP     Published Date: Mar 09, 2017
Small and midsize retailers around the world are seeing their businesses transform in a variety of ways. These firms, typically with fewer than 1,000 employees, have been transforming themselves as customers seek new types of engagement and as suppliers expect higher levels of efficiency and effectiveness. New business models and new competitors are changing the way retailers do business. Rather than simply react to new threats, successful retailers are leveraging technology in new ways to sharpen business practices, improve agility, and better serve customers while strengthening the role of retailers in the supply chain. Through digital transformation including the effective engagement of the internet of things (IoT) to track inventory, the opportunity to maintain and gain competitive advantage can be significant.
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SAP
Published By: Entrust Datacard     Published Date: Nov 15, 2017
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Entrust Datacard
Published By: Windstream     Published Date: Oct 30, 2015
UCaaS enables businesses to transition their enterprise communications costs from a capital-expenditure (CapEx) to an operational-expense (OpEx) model, transforming the upfront costs related to on-premises PBX and UC deployments to a flexible per-user pricing model, which allows them to quickly scale the number of UC users up or down as business needs dictate.
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Windstream
Published By: Windstream     Published Date: Oct 30, 2015
UCaaS enables businesses to transition their enterprise communications costs from a capital-expenditure (CapEx) to an operational-expense (OpEx) model, transforming the upfront costs related to on-premises PBX and UC deployments to a flexible per-user pricing model, which allows them to quickly scale the number of UC users up or down as business needs dictate.
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Windstream
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