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: Integrated enterprise resource planning software normalizes the reporting requirements for a mining company’s various departments. This article loosely shows the parallels between the operations in a mining company and those of a manufacturer whose product is sold on store shelves.

Abstract: It is now imperative that businesses be prudent. With rising volumes of data, traditional analytical techniques may not be able to discover valuable data. Consequently, data mining technology becomes important. Here is a framework to help understand the data mining process.

Abstract: Microsoft released a new version of OLE DB (Object Linking and Embedding Database, based on Microsoft’s Component Object Model or COM) which supports a proprietary data mining specification. It is purported to extend the Structured Query Language (SQL) to allow easier and faster incorporation of data mining queries into existing data warehouse solutions.

Abstract: Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. Data mining is a set of techniques for automating the exploration of data and uncovering hidden truths.

Abstract: Client relationship management (CRM) technology can help capital-markets firms streamline investment banking activities, increase brokerage trade volume, and meet compliance challenges. As a nerve center for information about clients, prospects, institutions, syndicate partners, and other contacts, the right CRM system enables capital-markets firms to share intelligence, coordinate activities, and derive advantage from an integrated body of corporate knowledge and relationships.

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: 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: 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: 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: In contrast to traditionally investing in automation technologies for better use of tangible assets, enterprises have begun to invest in optimizing human capital. Human capital management, or talent management, is a set of human resources practices focusing on the acquisition, management, and optimization of the enterprise workforce.

Abstract: Human Capital Management (HCM) is a strategic approach to people management that focuses on the knowledge, skills, abilities and capacity to develop and innovate possessed by people in an organisation (source: "Human Capital Management [HCM]: Achieving Added Value Through People", by Angela Baron & Michael Armstrong).

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: Two-thirds of the 400 survey participants in a 2007 Aberdeen report about inventory management place a high priority on working capital optimization. Many companies are reconsidering their business strategies, including implementing appropriate technology platforms that help automate supply chain processes. Learn about cost-effective and practical ways to reduce inventory so that working capital can be released.

Abstract: In times of economic uncertainty, you need to actively seek out best practices in how to adopt practical working capital optimization to improve financial performance while maintaining customer satisfaction. Learn about the three key performance criteria that define best-in-class organizations, and how they’re making their focus within the supply chain on working capital—primarily by optimizing inventory management.