data quality management

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By: Oracle     Published Date: Nov 27, 2013
Today customers look to engage with organizations through an increasing number of channels – and expect more from every customer service and support experience. As a result, connecting customers quickly and efficiently with the information they need has become doubly important: both as a means to reduce service costs in a harsh economic climate, and as a key battleground in the drive to establish competitive differentiation and edge.
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management
     Oracle
By: Oracle     Published Date: Nov 27, 2013
You need an answer fast. You searched online and almost got the answer, but require a little more information without having to call someone. What do you do? Fortunately, in today's customer service world, there are options. Download this White Paper for more information.
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management
     Oracle
By: Oracle     Published Date: Nov 27, 2013
According to Forrester, 72% of customers prefer using a company’s Website to answer their questions. But only 52.4% find the information they need online. Customers want to solve their issues quickly and easily on the Web. When they can, they are likely to buy more, with 88% saying they will increase their spending. Download this White Paper for more information.
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management
     Oracle
By: Oracle     Published Date: Nov 27, 2013
Social-enabled customer service requires three primary capabilities. The capability to: 1. Listen and Respond: Treat Social Media as an Integrated Interaction Channel Most social-enabled contact centers are at the early adopter stage, attempting to “bolt on” social media as a side process. Many are experiencing inconsistent customer experiences, higher costs and negligible return on investments. Download this White Paper for more info.
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management
     Oracle
By: Oracle     Published Date: Jan 16, 2014
You need an answer fast. You searched online and almost got the answer, but require a little more information without having to call someone. What do you do?
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management, knowledge management
     Oracle
By: Oracle     Published Date: Jan 16, 2014
You need an answer fast. You searched online and almost got the answer, but require a little more information without having to call someone. What do you do?
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management, knowledge management
     Oracle
By: Oracle     Published Date: Jan 16, 2014
You need an answer fast. You searched online and almost got the answer, but require a little more information without having to call someone. What do you do?
Tags : crm best practices, crm software, customer data management, customer experience & engagement, customer relationship management (crm), lead generation, lead management, lead nurturing, lead quality, lead scoring, sales automation, sales channels, sales force automation, sales management, knowledge management
     Oracle
By: SAP     Published Date: Jun 23, 2009
In this paper, Frank Dravis, Six Factors Consulting, discusses how even with the finest marketing organizations, the success of marketing ultimately comes down to the data.
Tags : single platform, data integration, data quality, quality management, soa, architecture, soa, sap businessobjects data services, platform, sap, service-oriented architecture, xl 3.0, data management, developer, source, data integration, business intelligence, legacy, crm, customer relationship management
     SAP
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: 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: 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: 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
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: 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
     HP
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.
Tags : 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
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.
Tags : big data, ibm, big data outcomes, information governance, big data analytics, it management, data management, data center
     IBM
By: IBM     Published Date: Apr 18, 2016
"Built using the IBM® InfoSphere® Information Server, IBM BigInsights® BigIntegrate and BigInsights BigQuality provide the end-to-end information integration and governance capabilities that organizations need."
Tags : ibm, big data, ibm infosphere, ibm biginsights, ibm bigintegrate, ibm bigquality, data management, data quality, data integration
     IBM
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