data quality management

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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: Oracle     Published Date: Nov 08, 2017
Digital developments are forever shaping the way we work, bringing new innovations through our office doors almost every day. These high-demand technologies have increased expectations, with organizations now having to drive business agility at unprecedented levels. Although we’re presented with enormous opportunity, we also face new obstacles that can block the path to success; obstacles such as the need to innovate quickly, keep costs down, and actively respond to competitive pressures.
Tags : oracle database, enterprise, quality assurance, data center, server infrastructure, storage management, oracle
     Oracle
By: Claravine     Published Date: Jan 03, 2019
Marketers have long struggled with the simple task of knowing which marketing spend is truly effective, and how to optimize that spend. At the heart of the issue lies the challenge of ensuring the data quality and consistency exists to make decisions based on real intelligence. Why is this a problem? First, effective tracking is reliant on the consistent, complete application of campaign tracking codes and associated metadata, which has traditionally been a manual, ungoverned process. Adding to this complexity has been the dramatic expansion of digital marketing point solutions, and the disparate teams expected to execute across each of these channels and geographies. The result is what you would expect—highly inaccurate, incomplete, and inconsistent data that must be manually cleaned before reporting is possible. Fortunately a solution exists. Progressive marketing leaders are implementing Digital Experience Data Management (DXDM), ensuring the rich, consistent insights critical to ma
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     Claravine
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
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: 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: IBM     Published Date: Dec 12, 2006
The purpose of this paper is to highlight the issues, requirements, and technologies available for automated advanced name recognition.
Tags : business process automation, data integration, data quality, information management, customer service, data management, enterprise software, contact management, marketing automation, crm, customer relationship management, ibm
     IBM
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.
Tags : enterprise, metadata, management, organizations, technology, tools, mark logic
     MarkLogic
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.
Tags : agile, enterprise, metadata, management, organization
     MarkLogic
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: Dun & Bradstreet     Published Date: Mar 03, 2017
Complexity, globalization and digitalization are just some of the elements at play in the risk landscape—and data is becoming a core part of understanding and navigating risk. How do modern finance leaders view, navigate and manage enterprise risk with data? Dun & Bradstreet surveyed global finance leaders across industries and business types. Here are the top trends that emerged from the study: 1. The Enterprise Risk & Strategy Disconnect—Finance leaders are using data and managing risk programs, but over 65% of finance leaders say there’s missing link between risk and strategy. 2. The Risks of the Use and Misuse of Data—Up to 50% of the data used to manage modern risk is disconnected. Only 15% of leaders are confident about the quality of their data. 3. Risky Relationships—Only 20% of finance leaders say the data they use to manage risk is fully integrated and shared. Download the study to learn how finance leaders are approaching data and enterprise risk management
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     Dun & Bradstreet
By: SugarCRM     Published Date: Nov 20, 2013
Organizations are frequently turning to SaaS solutions for their CRM needs. But there are risks when deploying any solution at scale, especially if you select the wrong vendor. Understand how to make the smart choice for your CRM solution in this informative eBook.
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
     SugarCRM
By: SugarCRM     Published Date: Apr 08, 2014
Organizations are frequently turning to SaaS solutions for their CRM needs. But there are risks when deploying any solution at scale, especially if you select the wrong vendor. Understand how to make the smart choice for your CRM solution in this informative eBook.
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
     SugarCRM
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
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: IBM     Published Date: Dec 30, 2008
Does your business need to safeguard information, keep auditors and regulators satisfied, and improve data quality? Data governance is the answer. This informative video outlines the latest challenges and best practices in data governance. IBM data governance solutions help businesses with:• Audit and reporting • Data architecture/infrastructure • Data quality • Information lifecyle management • Metadata/business glossaries • Organizational design/development • Policy/risk management • Security/privacy/compliance • Stewardship/value creation
Tags : ibm, data governance, safeguard information, data quality, data architecture, data infrastructure, information lifecycle management, metadata, business glossaries, organizational design, organizational development, policy management, risk management, security, privacy, compliance, stewardship, value creation, enterprise applications, data management
     IBM
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: 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.
Tags : ibm, idc, big data, data, analytics, information governance, data management, data center
     IBM
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, enterprise applications, data management, data center
     IBM
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.
Tags : big data, analytics, unstructured content, enterprise information, ibm, security, it management, storage, data 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: C2C Systems, Inc.     Published Date: Mar 15, 2007
This paper considers the use of email archives for compliance. It will also review how archives are trusted and look at what has to be done to ensure that integrity is maintained throughout the chain of events that take place within an email archive environment.
Tags : compliance, email archiving, data quality, records management, personal email, enterprise email, email security, c2c
     C2C Systems, Inc.
By: SugarCRM     Published Date: Jan 07, 2015
The way companies use and pay for customer relationship management (CRM) software has changed significantly over the past decade. Moving from a predominantly perpetual license-based system, where companies paid a large up-front sum and then smaller annual maintenance fees, CRM software providers are now moving towards monthly or annual subscription fees to access CRM software on the Internet.
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, networking
     SugarCRM
By: Trillium Software     Published Date: May 19, 2011
Effective CRM demands a single, complete, accurate view of customer information including purchasing history, product interest, and support interactions. Enterprises require a solution to discover data issues, correct and standardize, and maintain data accuracy and consistency.
Tags : trillium software, single customer view, data quality, crm, customer relationship management, high-quality data, erp, mdm, soa
     Trillium Software
By: Trillium Software     Published Date: May 19, 2011
Effective CRM demands a single, complete, accurate view of customer information including purchasing history, product interest, and support interactions. Enterprises require a solution to discover data issues, correct and standardize, and maintain data accuracy and consistency.
Tags : trillium software, single customer view, data quality, crm, customer relationship management, high-quality data, erp, mdm, soa
     Trillium Software
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