essential data

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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: 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: Embarcadero     Published Date: Jul 23, 2015
Whether you’re working with relational data, schema-less (NoSQL) data, or model metadata, you need a data architecture that can actively leverage information assets for business value. The most valuable data has high quality, business context, and visibility across the organization. Check out this must-read eBook for essential insights on important data architecture topics.
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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: TopQuadrant     Published Date: Jun 11, 2018
Data governance is a lifecycle-centric asset management activity. To understand and realize the value of data assets, it is necessary to capture information about them (their metadata) in the connected way. Capturing the meaning and context of diverse enterprise data in connection to all assets in the enterprise ecosystem is foundational to effective data governance. Therefore, a data governance environment must represent assets and their role in the enterprise using an open, extensible and “smart” approach. Knowledge graphs are the most viable and powerful way to do this. This short paper outlines how knowledge graphs are flexible, evolvable, semantic and intelligent. It is these characteristics that enable them to: • capture the description of data as an interconnected set of information that meaningfully bridges enterprise metadata silos. • deliver integrated data governance by addressing all three aspects of data governance — Executive Governance, Representative Governance, and App
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TopQuadrant
Published By: Melissa Data     Published Date: Jan 18, 2018
Maintaining high quality data is essential for operational efficiency, meaningful analytics and good long-term customer relationships. But, when dealing with multiple sources of data, data quality becomes complex, so you need to know when you should build a custom data quality tools over canned solutions. To answer this question, it is important to understand the difference between rules-based data quality, where internal subject matter expertise is necessary – and active data quality, where different domain expertise and resources are required.
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Melissa Data
Published By: Experian     Published Date: Mar 30, 2017
Businesses today recognize the importance of the data they hold, but a general lack of trust in the quality of their data prevents them from achieving strategic business objectives. Nearly half of organizations globally say that a lack of trust in their data contributes to increased risk of non-compliance and regulatory penalties (52%) and a downturn in customer loyalty (51%). To be of value to organizations, data needs to be trustworthy. In this report, you will read about the findings from this unique study, including: · How data powers business opportunities · Why trusted data is essential for performance · Challenges that affect data quality · The current state of data management practices · Upcoming data-related projects in 2017
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Experian
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: DATAVERSITY     Published Date: Dec 23, 2013
Everyone in an organization relies on Metadata to do their jobs. Whenever an email is sent, a report is run, inventory is ordered, compliance procedures are verified, a new IT system is integrated, applications are executed, or essentially any other business function, process, or decision is undertaken, Metadata is facilitating in the background. If that Metadata is corrupt, missing, redundant, or unpredictable then they cannot do their jobs well, they cannot trust the data they are using, and the organization ultimately suffers at all levels. Data Stewards are the people who are use, define, cleanse, archive, analyze, and share the data that is mapped directly to the Metadata of their myriad database and application systems. If your organization does not have Data Stewards (or an inefficient Stewardship Program), you need them. This paper is sponsored by: ASG.
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DATAVERSITY
Published By: ASG     Published Date: Jun 10, 2013
Everyone in an organization relies on Metadata to do their jobs. Whenever an email is sent, a report is run, inventory is ordered, compliance procedures are verified, a new IT system is integrated, applications are executed, or essentially any other business function, process, or decision is undertaken, Metadata is facilitating in the background. If that Metadata is corrupt, missing, redundant, or unpredictable then they cannot do their jobs well, they cannot trust the data they are using, and the organization ultimately suffers at all levels. Data Stewards are the people who are use, define, cleanse, archive, analyze, and share the data that is mapped directly to the Metadata of their myriad database and application systems. If your organization does not have Data Stewards (or an inefficient Stewardship Program), you need them.
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asg, data, data management, data governance, white paper, dataversity, data steward, metadata
    
ASG
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.
Tags : 
data, data management, data modeling, agile, agile data modeling, it management
    
Embarcadero
Published By: Zynapse     Published Date: Nov 01, 2010
The 4-undying reasons to take care of during an ERP Data Migration.
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zynapse, erp projects, unspsc, data information, data management, governance, mdm, master data management, odm, mmdm, material, business intelligence, database development
    
Zynapse
Published By: Hewlett Packard Enterprise     Published Date: Oct 23, 2017
Midsized firms operate in the same hypercompetitive, digital environment as large enterprises—but with fewer technical and budget resources to draw from. That’s why it is essential for IT leaders to leverage best-practice processes and models that can help them support strategic business goals such as agility, innovation, speed-tomarket, and always-on business operations. A hybrid IT implementation can provide the infrastructure flexibility to support the next generation of high-performance, data-intensive applications. A hybrid foundation can also facilitate new, collaborative processes that bring together IT and business stakeholders.
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digital environment, hyper competitive, business goals, hybrid it, technology, hpe
    
Hewlett Packard Enterprise
Published By: Red Hat     Published Date: Aug 22, 2018
In the emerging digital enterprise, there’s a good chance some application development will be taking place outside the information technology department. It’s not that the role of IT is in any way being diminished – in fact, IT managers are getting busier than ever, overseeing the technology strategies of their enterprises. Rather, the pieces are in place for business users to build and configure the essential business applications they need, on a self-service basis, with minimal or no involvement of their IT departments. As the world moves deeper into an era of ongoing disruption from digital players – be they startups, or teams within established enterprises – technology has become an essential part of every job, from the boardroom to the boiler room. Accordingly, the discipline of IT is no longer confined to the data center or development shop. Many business managers and professionals are building, launching or downloading their own applications to achieve productivity and respond
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Red Hat
Published By: Oracle     Published Date: Feb 24, 2015
Data is the fuel of all marketing activities. Customers and prospective buyers are savvy, empowered, and carry high expectations for personalization across all interactions. Without the right data—at the right time—as well as the most effective strategies in place to manage our data sources, our communications and offers are dead on arrival. A one-size-fits-all approach to marketing is not a sound tactic—it’s a marketing fruitcake that no one wants to receive. It’s time to get down with data to delight your audience and provide them with the personal experiences they deserve. Read the marketing essentials guide now.
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business technology, data center
    
Oracle
Published By: Oracle     Published Date: Feb 24, 2015
Content marketing is driving the way marketing organizations are engaging their audiences. In our consumer-empowered world, it’s become more difficult for marketers to cut through clutter and a lot more challenging to garner loyalty with their audiences. That’s why smart content delivered in context will make you stand out in the crowd. Read the modern marketing essentials guide on Content Marketing now.
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oracle, marketing cloud, essentials, content, strategy, business objectives, target audience, engagement, metrics, topics, categories, demand, promotion, data, awareness, sales, retention, analytical applications, business intelligence, database development
    
Oracle
Published By: Carbonite     Published Date: Jul 18, 2018
Businesses virtualize to consolidate resources, reduce costs and increase workforce mobility. But failing to protect VMs with purpose-built protection could erase some of those gains. Here are five essential requirements IT managers should look for when deploying data protection for virtual environments.
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Carbonite
Published By: TIBCO Software     Published Date: Jan 17, 2019
Are you considering data virtualization for your organization today? In this paper you will learn 10 core truths about data virtualization and gain essential knowledge for overcoming analytic data bottlenecks and driving better outcomes.
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virtualization, data, analytics, datasets, software, access, integration, projects, tools, scalability
    
TIBCO Software
Published By: Akamai Technologies     Published Date: Mar 09, 2017
For many financial institutions, traffic from bots (aggregators, scrapers, crawlers) can account for up to 40-60% of their overall website traffic — from good bots engaged in essential business tasks to bad bots performing harmful activities. Banks, traders, asset managers, and insurers might know how much of their traffic is from bots, but what they may not know is the impact those bots may be having on their business. Read the report to learn more about: • Managing Financial Data Aggregators • How Bots Affect the Digital Experience • What a good bot looks like?
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financial services, financial data, bots, bot management, website traffic, the digital experience
    
Akamai Technologies
Published By: CA Technologies     Published Date: Sep 25, 2017
In the digital economy, application programming interfaces (APIs) are essential for executing ideas quickly and seizing new business opportunities. They are the building blocks of digital transformation, enabling organizations to deliver exceptional customer experiences, create new revenue streams and connect employees, partners, apps and devices to data—anytime, anywhere.
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customer experience, workflow, competitive differentiation, speed to market, new markets, business models, ca technologies
    
CA Technologies
Published By: Datto Inc.     Published Date: May 08, 2013
Successful backup is the foundation for disaster recovery and business continuity. Without it, recovery and continuity cannot exist. Without it, an MSP cannot fulfill their SLA. This Executive Brief addresses four essential elements to ensure successful data protection.
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backup, recovery, backup and recovery, business continuity, msp, managed service provider
    
Datto Inc.
Published By: Oracle     Published Date: Feb 28, 2018
Getting the most from your data, driving innovation, processing orders faster, and reducing operating costs are all essential. And Oracle Exadata is the database platform to deliver these improvements. Read five top reasons for running your business on Oracle Exadata, and find out why other organisations say it was such a good choice for them.
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business, oracle, exadata, organisation, operation
    
Oracle
Published By: CA Technologies     Published Date: Aug 24, 2017
In the digital economy, application programming interfaces (APIs) are essential for executing ideas quickly and seizing new business opportunities. They are the building blocks of digital transformation, enabling organizations to deliver exceptional customer experiences, create new revenue streams and connect employees, partners, apps and devices to data—anytime, anywhere.
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CA Technologies
Published By: Carbonite     Published Date: Apr 09, 2018
There are five essential steps to protecting data and applications from the most common causes of data loss and downtime. IT pros who follow these steps can feel confident in their long-term organizational plan. Read this expert guide from Ben Maas, an independent consultant and system architect who has guided many companies through backup and disaster recovery (BDR) deployments.
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Carbonite
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