data quality

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By: SAP     Published Date: Jun 23, 2009
Learn the importance of Data Quality and the six key steps that you can take and put into process to help you realize tangible ROI on your data quality initiative.
Tags : roi, data quality, sap, return-on-investment, crm, erp, enterprise resource management, customer relationship management, crm, business intelligence, referential integrity, sql, data quality scoring, target marketing, enterprise applications, data management
     SAP
By: SAP     Published Date: Feb 21, 2008
Many significant business initiatives and large IT projects depend upon a successful data migration. Your goal is to minimize as much risk as possible through effective planning and scoping. This paper will provide insight into what issues are unique to data migration projects and offer advice on how to best approach them.
Tags : sap, data architect, data migration, business objects, information management software, bloor, sap r/3, application, enterprise applications, data quality management, master data management, mdm, extraction, transformation load, etl
     SAP
By: SAP     Published Date: Mar 10, 2009
Learn about the importance of having a data quality strategy and setting the overall goals. The six factors of data are also explained in detail and how to tie it together for implementation.
Tags : sap, data quality, strategy, project management, erp
     SAP
By: SAP     Published Date: Apr 13, 2011
In today's heightened competitive and regulatory environment, an organization's fortunes can rise or fall based on the effectiveness of its financial systems, particularly those that drive its performance management. Organizations today are looking to optimize these systems, at the core of which are financial consolidation and planning.
Tags : unified planning, bpm partners, performance management application, procurement, deployment, data quality, risk and compliance, lob, financial application
     SAP
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.
Tags : sap, smbs, high-quality data, erp software solution, decision management, data visibility, data quality, corporate strategy, business activities, resource allocation, compliance, metrics, tec
     SAP
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
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
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.
Tags : 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.
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.
Tags : apc, power, cooling, it wiring, heat removal, green computing, ieee, equipment failure
     APC
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.
Tags : pillar data systems, quality of service, qos, roi, shared storage environment
     Pillar Data Systems
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.
Tags : data mining, qualitative data, quantitative data, john f. elder, data quality, sas, bottom-line
     SAS
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.
Tags : 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
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.
Tags : sas, cio, chief information officer, data utility, information management, software development
     SAS
By: SAS     Published Date: Sep 13, 2013
Insights from a webinar in the Applying Business Analytics webinar series.
Tags : sas, big data, big data quality, data, terabytes, petabytes, exabytes, software development
     SAS
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.
Tags : 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
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.
Tags : 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
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.
Tags : 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
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.
Tags : 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
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.
Tags : 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
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.
Tags : 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
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.
Tags : 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
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.
Tags : it infrastructure, data management, backup and recovery, data strategy, data quality, blazent, improving business results
     EMA
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.
Tags : 3par, storeserv, storage, data, solutions, flash, data management, business technology
     HP
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