data quality

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Published By: SAP     Published Date: Jun 30, 2011
This white paper explores why today's executives still lack the relevant information or data quality to make decisions in a timely manner. Inside, learn about the biggest decisions-making challenges facings modern businesses and three keys to achieving better data-driven decisions.
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sap, smbs, high-quality data, erp software solution, decision management, data visibility, data quality, corporate strategy, business activities, resource allocation, compliance, metrics, tec
    
SAP
Published By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
The most recent decade has seen rapid advances in connectivity, mobility, analytics, scalability, and data, spawning what has been called the fourth industrial revolution, or Industry 4.0. This fourth industrial revolution has digitalized operations and resulted in transformations in manufacturing efficiency, supply chain performance, product innovation, and in some cases enabled entirely new business models. This transformation should be top of mind for quality leaders, as quality improvement and monitoring are among the top use cases for Industry 4.0. Quality 4.0 is closely aligning quality management with Industry 4.0 to enable enterprise efficiencies, performance, innovation and business models. However, much of the market isn’t focusing on Quality 4.0, since many quality teams are still trying to solve yesterday’s problems: inefficiency caused by fragmented systems, manual metrics calculations, quality teams independently performing quality work with minimal cross-functional own
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SAS
Published By: Neolane, Inc.     Published Date: Dec 30, 2008
The Hager Group is a $1.5-billion electronics manufacturer. With a distributed global workforce of more than 10,000 employees, Hager has 40 sales subsidiaries and 25 industrial sites worldwide.  Today, with a centralized marketing database and software, Hager can ensure data quality and deliver personalized, targeted communications according to customer and prospect profiles and behavior.  This program allows Hager to capture 1,000 new prospects each month, and achieve a 10 percent conversion rate - resulting in an incremental revenue increase of $42 million per year.
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neolane, the hager group, e-marketing program, centralized marketing database, central repository customer data, crm software, custom content, deliverability, e-commerce, email marketing, emerging marketing, international marketing, lead generation, rich media
    
Neolane, Inc.
Published By: APC     Published Date: Apr 08, 2010
Many of the mysteries of equipment failure, downtime, software and data corruption, are the result of a problematic supply of power. There is also a common problem with describing power problems in a standard way. This white paper will describe the most common types of power disturbances, what can cause them, what they can do to your critical equipment, and how to safeguard your equipment, using the IEEE standards for describing power quality problems.
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apc, power, cooling, it wiring, heat removal, green computing, ieee, equipment failure
    
APC
Published By: Pillar Data Systems     Published Date: Apr 20, 2010
Download this white paper to learn how to avoid over-provisioning your storage so you can avoid additional capital expenditure and increase your ROI.
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pillar data systems, quality of service, qos, roi, shared storage environment
    
Pillar Data Systems
Published By: SAS     Published Date: Apr 13, 2011
Learn how organizations are using data mining to solve their problems, including a $1 billion decision that produced positive results.
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data mining, qualitative data, quantitative data, john f. elder, data quality, sas, bottom-line
    
SAS
Published By: SAS     Published Date: Mar 01, 2012
Learn what criteria distinguished certain companies as top performers within the SMB sector, the factors to consider when assessing your organization's BI competency and the required actions to achieve best-in-class performance.
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sas, analytics, business analytics, business intelligence, customer intelligence, data management, fraud & financial crimes, high-performance analytics, it management, ondemand solutions, performance management, risk management, sas® 9.3, supply chain intelligence, sustainability management, business intelligence, michael lock, predictive analytics, business insight, business visibility
    
SAS
Published By: SAS     Published Date: Sep 13, 2013
If businesses are recognizing the need for a dial-tone approach to establishing “data utility” services for meeting user expectations for data accessibility, availability and quality, it is incumbent upon the information management practitioners to ensure that the organization is properly prepared, from both a policy/process level and a technology level.
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sas, cio, chief information officer, data utility, information management, software development
    
SAS
Published By: SAS     Published Date: Sep 13, 2013
Insights from a webinar in the Applying Business Analytics webinar series.
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sas, big data, big data quality, data, terabytes, petabytes, exabytes, software development
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This Q&A with Tom Davenport, Director of Research for the International Institute for Analytics (IIA), will help you understand how analytics is evolving, where you need to go, and how to get there.
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sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, analytics, analytical study, visualization deployment, deployment, institute for analytics
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This paper explores the challenges organizations have today in implementing a data governance program via an actual business case. It highlights SAS technology that can help you solve many of those challenges.
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sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics, data center
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This report examines how data visualization can help organizations unleash the full value of information, and outlines key considerations to guide the solution evaluation process.
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sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics, data center
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
Managing expectations before, during and after the adoption of visualization software is crucial. Users should know what the rollout process will look like and how it will take place, and have clear goals for using the tool. Make sure that the desired outcome isn’t just look-and-feel. Creating beautiful charts and graphs is not a substitute for practical business decisions.
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sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics, data center
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This paper explores ways to qualify data control and measures to support the governance program. It will examine how data management practitioners can define metrics that are relevant.
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sas, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, potential classifications, data analyst, scorecard, reporting the scorecard, improve scorecard, business process
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
This paper will consider the relevance of measurement and monitoring – defining inspection routines, inserting them into the end-to-end application processing, and reporting the results.
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sas, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, potential classifications, data analyst, scorecard, reporting the scorecard, improve scorecard, business process, data center
    
SAS
Published By: SAS     Published Date: Mar 14, 2014
Jill Dyche and SpectraDynamo explains the importance of understanding how to manage data and issues regarding data categorization, retrieval and quality.
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sas, data categorization, retrieval and quality, spectradynamo, telemetry data, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, data center
    
SAS
Published By: EMA     Published Date: Aug 22, 2012
Join EMA Research Director, Charles Betz, and Blazent Senior Director of Sales Engineering, Adam Clark, to learn how Blazent is pioneering a new approach to master data management that can greatly improve the business results from IT.
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it infrastructure, data management, backup and recovery, data strategy, data quality, blazent, improving business results
    
EMA
Published By: HP     Published Date: Jul 22, 2014
HP offers an approach to the modern data center that addresses systemic limitations in storage by offering Tier-1 solutions designed to deliver the highest levels of flexibility, scalability, performance, and quality—including purpose-built, all-flash arrays that are flash-optimized without being flash-limited. This white paper describes how, through the incorporation of total quality management throughout each process and stage of development, HP delivers solutions that exceed customer quality expectations, using HP 3PAR StoreServ Storage as an example.
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3par, storeserv, storage, data, solutions, flash, data management, business technology
    
HP
Published By: Adobe     Published Date: Apr 03, 2015
A lack of executive support and poor data quality are just some reasons why analytics programs fail. The guide by Adam Greco, Reenergize Your Web Analytics, identifies the key reasons for program failures and provides ten ways to make your analytics program successful. Read the guide to discover key ways to improve your analytics program, including: • How to deal with your stakeholders • How to set your analytics priorities • How to reap the rewards of change
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analytics program, stakeholders, adobe, marketing, personalization
    
Adobe
Published By: IBM     Published Date: May 28, 2014
Read the whitepaper to find out how one client improved business value of their data by implementing InfoSphere Optim processes and technologies.
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ibm, data lifecycle management, infosphere optim, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence, virtualize data, lifecycle management
    
IBM
Published By: IBM     Published Date: May 28, 2014
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
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ibm, data retention, information governance, archiving, historical data, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence
    
IBM
Published By: IBM     Published Date: Jul 22, 2016
"Increasingly, brands are looking to differentiate based on an exceptional customer experience. The key to improving the customer experience is being able to effectively measure what’s working and what you need to improve. IBM host a webinar presenting tips on how to measure the customer experience for your brand and how to use that data to build better journeys. Please join IBM and guest speaker Andrew Hogan from Forrester Research as we share tips on how to best measure the digital experiences customers have with your brand and how to use that information to build better journeys. The webinar will provide attendees with: • Best practices to measure the quality of digital customer experiences • Guidance on the kinds of tools to use to capture the right CX metrics • Tips for integrating metrics, including the role of customer journeys • Techniques to drive action and improve digital experiences"
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ibm, commerce, customer analytics, marketing, customer experience, customer insight, forrester, digital experience, knowledge management, enterprise applications
    
IBM
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