organization data quality

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Published By: SAS     Published Date: Jan 17, 2018
Executives, managers and information workers have all come to respect the role that data management plays in the success of their organizations. But organizations don’t always do a good job of communicating and encouraging better ways of managing information. In this e-book you will find easy to digest resources on the value and importance of data preparation, data governance, data integration, data quality, data federation, streaming data, and master data management.
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SAS
Published By: CA Technologies EMEA     Published Date: Sep 07, 2018
With the application economy in full swing, more organizations are turning to Continuous Testing and DevOps development practices in order to quickly roll out applications that reflect the ever-changing needs of tech-savvy, experience-driven consumers. Rigorous data they need, in the right formats. This forces teams to postpone their testing until the next sprint. As a result, organizations like yours are increasingly looking for ways to overcome the challenges of poor quality data and slow, manual data provisioning. They are also concerned about compliance and data privacy when using sensitive information for testing. CA Test Data Manager can help you mitigate all these concerns, so you’re positioned to achieve real cost savings.
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continuous delivery, application delivery, testing, test data management
    
CA Technologies EMEA
Published By: IBM     Published Date: Jul 08, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, idc, big data, data, analytics, information governance, knowledge management, data management, data center
    
IBM
Published By: Oracle     Published Date: Jul 31, 2017
Consolidation in the healthcare industry has reached a record pace. The volume of organizational change has generated solid templates and best practices for change management. In addition, technological advances provide opportunities for sophisticated data analytics and systems integrations. By identifying and taking advantage of these technologies, healthcare organizations can increase the odds of successful integrations that result in greater agility decrease their cost base, and improve the quality of care they provide.
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Oracle
Published By: IBM     Published Date: Jan 19, 2017
The outcome of any big data analytics project, however, is only as good as the quality of the data being used. As big data analytics solutions have matured and as organizations have developed greater expertise with big data technologies he quality and trustworthiness of the data sources themselves are emerging as key concerns. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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ibm, analytics, ecm, data, big data, information governance, data management, business technology
    
IBM
Published By: IBM     Published Date: Jul 09, 2018
As the information age matures, data has become the most powerful resource enterprises have at their disposal. Businesses have embraced digital transformation, often staking their reputations on insights extracted from collected data. While decision-makers hone in on hot topics like AI and the potential of data to drive businesses into the future, many underestimate the pitfalls of poor data governance. If business decision-makers can’t trust the data within their organization, how can stakeholders and customers know they are in good hands? Information that is not correctly distributed, or abandoned within an IT silo, can prove harmful to the integrity of business decisions. In search of instant analytical insights, businesses often prioritize data access and analysis over governance and quality. However, without ensuring the data is trustworthy, complete and consistent, leaders cannot be confident their decisions are rooted in facts and reality
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IBM
Published By: Group M_IBM Q418     Published Date: Oct 15, 2018
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data. Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
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Group M_IBM Q418
Published By: Group M_IBM Q119     Published Date: Jan 08, 2019
The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing business operations, analytics and emerging digital business scenarios. It covers much more than just technology. It includes program management, roles, organizational structures, use cases and processes (such as those for monitoring, reporting and remediating data quality issues). It is also linked to broader initiatives in the field of enterprise information management (EIM), including information governance and master data management (MDM)
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Group M_IBM Q119
Published By: MarkLogic     Published Date: Mar 29, 2018
It’s your golden opportunity: Rapidly integrate and harmonize data silos. Enhance drug discovery. Achieve faster time to insight. Get to market faster — all with less cost than you think. Explore how Life Sciences organizations can accelerate Real World Evidence (RWE) in a comprehensive and cost efficient manner. Download this white paper to learn about challenges, solutions and most importantly — how to equip your organization for success.
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manufacturers, organizations, integration, optimization, data, quality
    
MarkLogic
Published By: MarkLogic     Published Date: Mar 29, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
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enterprise, metadata, management, organizations, technology, tools, mark logic
    
MarkLogic
Published By: MarkLogic     Published Date: May 07, 2018
Executives, managers, and users will not trust data unless they understand where it came from. Enterprise metadata is the “data about data” that makes this trust possible. Unfortunately, many healthcare and life sciences organizations struggle to collect and manage metadata with their existing relational and column-family technology tools. MarkLogic’s multi-model architecture makes it easier to manage metadata, and build trust in the quality and lineage of enterprise data. Healthcare and life sciences companies are using MarkLogic’s smart metadata management capabilities to improve search and discovery, simplify regulatory compliance, deliver more accurate and reliable quality reports, and provide better customer service. This paper explains the essence and advantages of the MarkLogic approach.
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agile, enterprise, metadata, management, organization
    
MarkLogic
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
From small organizations using spreadsheets and visual discovery tools to large enterprises trying to improve data quality and delivery, data preparation difficulties are a major concern. Download your complimentary copy of the full report so you can tackle your data preparation challenges.
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Alteryx, Inc.
Published By: Experian QAS     Published Date: Feb 25, 2013
An omnichannel customer experience is more important than ever before. However, this new customer-centric approach requires accurate data in order to be successful. Find out how organizations approach data quality in the changing retail environment.
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omnichannel, data quality, customer experience, customer data
    
Experian QAS
Published By: DataFlux     Published Date: Jan 07, 2011
This white paper describes a general approach for planning your organization's efforts to improve data quality, providing a data-example-driven perspective of some of the unique challenges of product data quality, as well as discuss and demonstrate three critical steps to improving product data quality.
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dataflux, product data quality, standardization, matching
    
DataFlux
Published By: Trillium Software     Published Date: Jun 06, 2011
The first Web seminar in the series is titled "Operational Data Quality: Get the Data Right the First Time" and will be conducted by Ed Wrazen, vice president of product marketing at Trillium Software.
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trillium software, operational data quality, ed wrazen, operational system, business solutions, organization data quality, enterprise information
    
Trillium Software
Published By: Trillium Software     Published Date: Aug 10, 2011
The first Web seminar in the series is titled "Operational Data Quality: Get the Data Right the First Time" and will be conducted by Ed Wrazen, vice president of product marketing at Trillium Software.
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trillium software, operational data quality, ed wrazen, operational system, business solutions, organization data quality, enterprise information
    
Trillium Software
Published By: CDW-Trend Micro     Published Date: Mar 26, 2015
Virtualization and cloud computing can help your organization achieve significant savings in data center hardware costs, operational expenditures, and energy demands— while achieving improvements in quality of service and business agility. However, as data centers continue to transition from physical to virtual and now increasingly, cloud environments, traditional security can slow down provisioning, become difficult to manage, and cause performance lag. As you scale your virtual environment and adopt software defined networking, evolving your approach to security can reduce time, effort, and impact on CPU, network, and storage. Read this white paper to learn more about virtualization and cloud computing.
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security, virtualization, cloud computing, trend micro, it management
    
CDW-Trend Micro
Published By: ClearSlide, Inc     Published Date: Feb 25, 2015
Organizations treat customer data as a key component of CRM. Many organizations see an effective customer data management strategy as an important cornerstone of their CRM strategy. Most organizations are moving beyond tactical CRM initiatives focused on saving cost and driving efficiency to making their organization more effective and focused on driving better customer engagement and experience. Customer data is used to enhance customer experiences, improve service quality, target marketing efforts, capture customer sentiment, increase upsell opportunities and trigger product and service innovation.
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crm, big data, customer engagement, customer service
    
ClearSlide, Inc
Published By: IBM     Published Date: Feb 24, 2015
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, ibm, big data outcomes, information governance, big data analytics, it management, data management, data center
    
IBM
Published By: IBM     Published Date: Apr 06, 2016
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments
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ibm, big data, data security, risk management
    
IBM
Published By: IBM     Published Date: Jul 15, 2016
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments.
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ibm, data, security, big data, data management
    
IBM
Published By: IBM     Published Date: Oct 18, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
Tags : 
ibm, idc, big data, data, analytics, information governance, knowledge management, data management, data center
    
IBM
Published By: IBM     Published Date: Jul 13, 2015
This ebook explores how an enhanced 360-degree view of the customer optimizes and facilitates more personalized customer interactions.
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customer centric organizations, crm, customer usability, personalized interactions, big data, data quality, data management
    
IBM
Published By: IBM     Published Date: Apr 06, 2015
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
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big data, analytics, unstructured content, enterprise information, ibm, it management, knowledge management, storage, data management
    
IBM
Published By: IBM     Published Date: May 12, 2015
Creating more person-centric, coordinated and value-based care means all service providers must share risks and data, conducting business with partners that cross traditional boundaries, while data is transforming this industry at an unprecedented pace. Watch this webcast to learn more of how organizations are optimizing their business processes to lower costs, analyzing data to improve quality, care, and population health, while learning to engage in new ways to drive better outcomes.
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risk, data, service provider, healthcare, optimization
    
IBM
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