Abstract: Today's usage of Decision Support Systems (DSS), combined with vetted CRM 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: 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: 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: Investing in a customer relationship management (CRM) system can increase sales productivity and represent an addition to, not a subtraction from, your company’s bottom line. But a return on investment (ROI) from CRM software depends on whether you’ve identified strategies that leverage the CRM system’s sales productivity. Find out how to formulate CRM strategies before you choose a CRM solution, and compare CRM products.

Abstract: Hearing confusing messages from your customer resource management (CRM) and enterprise resource planning (ERP) vendors? You may be dealing with dozens of software vendors and system integrators, each one praising the benefits of his solution. Among these proposals, one claims not only to efficiently manage the entire customer life cycle, but also to take full advantage of your previous technology investments. Your incumbent ERP system vendor also has a CRM solution, and praises the benefits of the tight integration of both systems. The question then is, how do you know whether this solution is the best for you? Request your copy of Integrating CRM with ERP compliments of Baseline Consulting. The report gives you the criteria to use when formulating your strategy of integrating CRM with ERP, best practices of CRM implementation, dealing with ERP integration, and how to integrate CRM with ERP impacts the value chain.

Abstract: Since the last recession in 2001, customer relationship management (CRM) systems have gained greater acceptance. Though CRM systems haven’t been widely tested under adverse business conditions, results achieved by CRM strategies indicate that businesses with an effective CRM approach will have a vital competitive advantage in recessionary conditions. Discover three key strategies to using CRM as a tool against a recession.

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: Why do some customer relationship management (CRM) implementations fail? The answer: companies’ lack of understanding of their current CRM environments, and of what areas need modification or improvement. Companies with a clear understanding of what they need from a CRM solution—as well as of what CRM means to their business—are more likely to succeed. To clinch that success, some key elements should be assessed first.

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: Microsoft's foray into the CRM arena has not been a bed of roses, despite its indisputably large marketing muscle and R&D investment, its strong channel, traditionally attractive pricing policies, and the aura and experience within the market segment. Microsoft CRM remains both a threat and an opportunity for the most nimble mid-market CRM vendors. Microsoft’s entry with CRM evangelism through an array of seminars nationwide has bolstered the market’s awareness of the need for CRM applications.

Abstract: Customer relationship management (CRM) solutions can help you achieve success by managing your company’s customer-facing processes and implementing a customer-centric vision. But to make the most of CRM’s benefits, you should weigh and consider the options by answering key questions about your processes and CRM solutions’ capabilities. Find out key CRM principles, and how to best mitigate the cost of CRM implementation.

Abstract: Customer relationship management (CRM) is rapidly morphing from a customer management model to one of customer engagement. Social networks, podcasts, blogs, and wikis are enabling customers to become advocates, and not simply the targets they were in the traditional CRM process. The same techniques are also being used within the CRM industry itself to create a content-rich, social media environment for CRM professionals. Find out what these sweeping changes mean to businesses and CRM professionals alike, as TEC's director of research Wayne Thompson sits down with Paul Greenberg and Bruce Culbert of BPT Partners, a leading CRM consulting firm.

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.