Abstract: Today's usage of Decision Support Systems (DSS), combined with vetted ERP knowledge bases, allows organizations to save time and money, achieving better and more reliable/fully-documented decisions, a quantum improvement over the widely-used subjective process of selecting complex enterprise software...

Abstract: Today’s mobile software companies face a daunting challenge: How can they achieve quality while getting to market swiftly? After all, speed is king in the mobile market. The trade-off between speed and quality in mobile software development is an illusion—or should be. Get a closer look at the situation with a framework that presents a way to use quality assurance (QA) processes to accelerate development.

Abstract: Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.

Abstract: When your quality control plan is complex because there are many features to control—and it involves many people—you need to pay special attention to your quality control process. Also, a complex plan places high demands on your software solution. Discover how an online integrated factory information system can work across production, job tracking, spoilage, and quality to support all of your quality control processes.

Abstract: You can blame your sales people all you want, but if the lead data is bad, they’re not going to bring in business. You can blame your product managers for ineffective promotions, but if the target lists are redundant, the pitches fall on deaf ears. You can blame your customer service representatives for low satisfaction scores, but if customer data is missing, then no wonder the complaint resolution pipeline is backed up. Think it’s your customer resource management (CRM) system? Think again. It’s bad data, and it’s costing you millions. Request your copy of The Bottom Line on Bad Customer Data that delivers detailed advice from Jill Dyche, partner and co-founder of Baseline Consulting, about what you can do to address the impact of bad data on your company. The report gives you insight into how bad data is impacting your company and what you can do about it. How to identify where the bad data is and quantify its impact, and different approaches to determine the sources and causes of bad data are all offered in this paper.

Abstract: Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

Abstract: Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

Abstract: Quality does not start at the receiving dock and end at the shipping dock. The focus on the supply chain demands that the quality department be involved from the beginning to the end of the supply chain.

Abstract: Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses leverage data as an asset.

Abstract: Rising data volume is not the only reason companies are concerned with issues of data integration and data quality. The growing numbers of disparate systems that produce and distribute data add to the complexity. But in many companies, data quality management has not kept pace with the growth of data integration projects, and its use is immature. Find out how moving toward a single data services architecture can help.

Abstract: In today’s global market, providing quality products and services is essential for any manufacturer’s continued growth—but maintaining a competitive edge is not always easy. For success, quality awareness must begin at the conception of the product and continue throughout the various stages of its development. To improve in this area, many manufacturers are now adopting the total quality management (TQM) approach.

Abstract: In today’s world, current and future customers are interested to know whether or not you have implemented a quality control system. Some customers will demand International Organization for Standards (ISO) certification, and others may just ask about it. Whatever their requirements, having a quality control system assures your customers that the products and services you offer are of high quality.

Abstract: Companies are fighting a constant battle to integrate business data and content while managing data quality. Data quality serves as the foundation for business intelligence (BI), enterprise resource planning (ERP), and customer relationship management (CRM) projects. Learn more about software that unifies leading data quality and integration solutions—helping your organization to move, transform, and improve its data.

Abstract: Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you.

Abstract: There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing describes the process of defining, populating, and using a data warehouse. Creating, populating, and querying a data warehouse typically carries an extremely high price tag, but the return on investment can be substantial. Over 95% of the Fortune 1000 have a data warehouse initiative underway in some form.

Abstract: Data auditing is a form of data protection involving detailed monitoring of how stored enterprise data is accessed, and by whom. Data auditing can help companies capture activities that impact critical data assets, build a non-repudiable audit trail, and establish data forensics over time. Learn what you should look for in a data auditing solution—and use our checklist of product requirements to make the right decision.

Abstract: Do you use real customer profiles and statistics to drive marketing efforts, or real employee data for salary or benefit analysis? While these activities are critical to success, they can put you at risk for a data breach. But with a data security system, you can maintain the data’s original properties, while giving clearance for key business activities to proceed. Learn how to assure your sensitive data is protected.

Abstract: Copper Mountain debuts a Multi-mode Asymmetric Digital Subscriber Line (ADSL) line card. This line card will deliver voice and data service simultaneously over a standard plain old telephone service (POTS) line.

Abstract: Oracle Database 11g is a database platform for data warehousing and business intelligence (BI) that includes integrated analytics, and embedded integration and data-quality. Get an overview of Oracle Database 11g’s capabilities for data warehousing, and learn how Oracle-based BI and data warehouse systems can integrate information, perform fast queries, scale to very large data volumes, and analyze any data.