 |
Reinsurance Company Improves Data Quality, Enhances Profitability with DataFlux sponsored by DataFlux Corporation
 | Case Study: | Posted: 14 Jan 2009
| | Published: | 14 Jan 2009 | |
Summary: |
DataFlux dfPower Studio combines several tools to help standardize incoming data and assist companies in building business rules into their system.
|
|
|
 |
Leading Bank Chooses DataFlux to Create More Accurate, Complete Risk Reports sponsored by DataFlux Corporation
 | Case Study: | Posted: 14 Jan 2009
| | Published: | 14 Jan 2009 | |
Summary: |
Read how one of the largest banks in the southeast chose DataFlux dfPower® Studio to review and compare multiple data sources simultaneously through data quality and data integration workflows.
|
|
|
 |
Mortgage Company Uses DataFlux Technology to Improve Reporting, Manage Compliance sponsored by DataFlux Corporation
 | Case Study: | Posted: 14 Jan 2009
| | Published: | 14 Jan 2009 | |
Summary: |
Read how using DataFlux technology, this financial company implemented a data governance program, creating more complete and accurate data and drastically reducing the time needed to comply with Federal reporting regulations.
|
|
|
 |
Tactical Data Quality Projects Deliver Quick ROI - Expert Podcast sponsored by DataFlux Corporation
 | Podcast: | Posted: 21 Oct 2009
| | Premiered: | 21 Oct 2009 | | | Speaker: |
Rob Karel, Principal Analyst, Forrester Research
|
| |
Summary: |
This expert podcast offers advice for deploying and completing tactical data quality projects from Forrester's Rob Karel.
|
|
|
 |
Building a Data Quality Scorecard to Achieve Data Governance sponsored by DataFlux Corporation
 | Webcast: | Posted: 06 Jan 2009
| | Premiered: | Available On Demand | | | Speakers: |
David Loshin, President
Daniel Teachey, Director of Corporate Communications
|
| |
Summary: |
Operational data governance is the manifestation of the actionable processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization's business needs.
|
|
|
 |
Defining Relevant Metrics for Populating a Data Quality Scorecard sponsored by DataFlux Corporation
 | Webcast: | Posted: 06 Jan 2009
| | Premiered: | Available On Demand | | | Speaker: |
David Loshin
Dan Soceanu
|
| |
Summary: |
In this webcast your will learn how to Populate a Data Quality Scorecard with Relevant Metrics.
|
|
|
 |
Implementing a Data Quality Strategy sponsored by DataFlux Corporation
 | Webcast: | Posted: 06 Jan 2009
| | Premiered: | Available On Demand | | | Speaker: |
Ted Fredman, Vice President
Tony Fisher, President and CEO
|
| |
Summary: |
In this program Gartner Research Vice President Ted Fredman and DataFlux President and CEO Tony Fisher discuss the data quality issues facing business today as well as the solutions that can solve these problems.
|
|
|
 |
Getting Started with Master Data Management sponsored by DataFlux Corporation
 | White Paper: | Posted: 05 Jan 2009
| | Published: | 24 Dec 2008 | |
Summary: |
This paper focuses on what is master data, why it is needed, how to get started in managing it, and methodologies for implementing master data management.
|
|
|
 |
Populating a Data Quality Scorecard with Relevant Metrics sponsored by DataFlux Corporation
 | White Paper: | Posted: 26 Jun 2008
| | Published: | 04 Jun 2008 | |
Summary: |
Too often, data governance teams rely on existing measurements as the metrics used to populate a data quality scorecard. But without a defined understanding of the relationship between specific measurement scores and the business's success criteria..
|
|
|
 |
Observing Data Quality Service Level Agreements: Inspection, Monitoring, and Tracking sponsored by DataFlux Corporation
 | White Paper: | Posted: 05 Jan 2009
| | Published: | 24 Dec 2008 | |
Summary: |
This paper discusses how implementing a DQ SLA via formalized processes can transform data quality management from a constant "fire-fighting" mode to a more consistent, proactive approach.
|
|
|
 |
Data Governance: A Business Value-Driven Approach sponsored by DataFlux Corporation
 | White Paper: | Posted: 20 Nov 2009
| | Published: | 20 Nov 2009 | |
Summary: |
This white paper by Dr. Walid el Abed, CEO of Global Data Excellence, discusses how an effective data strategy embedded within the business strategy can turn data into a real competitive advantage, deliver short and longer term value and enable business success and sustainability.
|
|
|
 |
Data Migration for Project Leaders sponsored by DataFlux Corporation
 | White Paper: | Posted: 04 Nov 2009
| | Published: | 02 Nov 2009 | |
Summary: |
This guidebook provides the answers with a series of detailed sections including essential activities such as planning, forecasting, risk management, team selection, communication and collaboration.
|
|
|
 |
Commodity Coding: Code It or Buy (Too Much of) It sponsored by DataFlux Corporation
 | White Paper: | Posted: 04 Nov 2009
| | Published: | 02 Nov 2009 | |
Summary: |
This paper will present you with all the information needed to make an educated decision on whether to commodity coding is right for your organization and it will help with choosing the commodity coding that fits your company.
|
|
|
 |
A Guide to the Value of Reliable Data in Retail Banking sponsored by DataFlux Corporation
 | White Paper: | Posted: 04 Nov 2009
| | Published: | 02 Nov 2009 | |
Summary: |
Banks need to get smarter, attract the right customers, implement customer level risk management, and implement risk adjusted customer relationship pricing and all of this is dependent on trusted data. This paper examines the impact of unreliable data on retail banks. It then defines the requirements needed to guarantee data reliability in banking.
|
|
|
 |
A Guide to the Value of Reliable Data in Insurance sponsored by DataFlux Corporation
 | White Paper: | Posted: 04 Nov 2009
| | Published: | 02 Nov 2009 | |
Summary: |
Only when data is trusted can it be used in confidence in all insurance operational and analytical process activities. This paper examines the impact of unreliable data on insurance companies. It then defines the requirements needed to guarantee data reliability in insurance and offers a practical approach to creating and governing that data.
|
|
|