Abstract: Data leakage and data breach are two disparate problems requiring different solutions. Data leakage prevention (DLP) monitors and prevents content from leaving a company via e-mail or Web applications. Database activity monitoring (DAM) is a data center technology that monitors how stored data is accessed. Learn why DAM complements DPL, and how you can benefit by making it part of your overall data security strategy.

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: On October 19, Aladdin Knowledge Systems ( Nasdaq: ALDN ), a global leader in the field of Internet content and software security, announced with Packard Bell NEC International ( Nasdaq: NIPNY ), the leading brand of consumer PCs in Europe, an agreement to bundle approximately 50,000 eTokens on Packard Bell PCs. The eTokens, or Internet keys, will come in different colors so that all family members can have a unique color code. Retail shops in Belgium expect to have eTokenized PCs on their shelves by November.

Abstract: Data collection systems work; however, they require a investment in technology. Before the investment can be justified, we need to understand why a data collection system may be preferable to people with clipboards.

Abstract: With the number of vehicles worldwide expected to triple in the next few decades, the toll collection market will need to adapt to meet demand. Increasing traffic will make the use of conventional toll plazas unfeasible. Electronic toll collection (ETC) systems can help by supporting multilane, free-flow tolling that can be tailored to specific standards and processes. Find out what you need to know, before ETC passes you by.

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: Card auditing and calculating takes a great deal of time, particularly for companies using full-time time-keepers to calculate and audit time cards. If the time cards are handwritten, it takes even more time to review, calculate, edit, and (typically) re-enter the data into a payroll system. However, automation reduces audit time by pre-processing punches against rules defined within the system.

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: 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: Any business that would benefit by holding less inventory, decreasing mis-shipments and, reducing errors should seriously consider implementing bar code systems and other forms of automated data collection (ADC) technology. Companies of all sizes apply bar code and wireless data collection systems to save labor, increase efficiency, and cut operations costs. ADC is a proven productivity enhancer, and advancements in the technology have made such systems affordable and practical for more businesses than ever before. Common errors and inefficiencies don’t have to be part of business as usual. This paper exposes some of the hidden expenses that hamper profitability, and how accurate data collection can eliminate them. The examples and techniques presented show the link between quality information and efficient operations. When investing in ADC, small and medium sized businesses (SMBs) can earn a full and rapid return on investment through reduced expenses and increased productivity. This white paper will help show how by presenting ADC costjustification guidelines and strategies. From Intermec and Epicor Software Corporation.

Abstract: The 802.1x protocol should be the obvious choice when implementing user-based authentication in enterprise networks. So why doesn’t everyone use it? Because it doesn’t give a complete solution for authentication and policy requirements, even when it’s integrated with a network access control (NAC) solution. But if you integrate 802.1x with a local area network (LAN) secure switch, you get a more complete secure solution.

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: 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: Today’s extensive use of data collection tools coupled with the easy access to communication networks has woven more devices and systems into the fabric of our daily lives. As the scale of integration and complexity grows, so does the need for the appropriate paradigm to address it. In order to scale, system software architecture must be organized around a common-shared information model that can span multiple systems.

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: 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.

Abstract: Once a revolutionary concept, data warehouses are now the status quo—enabling IT professionals to manage and report on data originating from diverse sources. But where does log data fit in? Historically, log data was reported on through slow legacy applications. But with today’s log data warehouse solutions, data is centralized, allowing users to analyze and report on it with unparalleled speed and efficiency.