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Field Service Management (FSM)
Field service management (FSM) software is a set of functionalities for organizations or departments within organizations that have as main focus the intallation, maintanance, reparing, and meter r...
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Documents related to » most data


Master Data Management: Extracting Value from Your Most Important Intangible Asset
In a 2006 SAP survey, 93 percent of respondents experienced data management issues during their most recent projects. The problem: many organizations believe

most data  management issues during their most recent projects. Data management was also identified as the root cause of problems in process improvement projects. The best way to manage data depends on the unique characteristics of the data. For instance, master data is reference data about key entities within the organization, such as customers, products, employees, and so on. Unlike transactional data '' for example, a sales order '' master data does not change frequently and typically involves a relatively small Read More...
Data, Data Everywhere: A Special Report on Managing Information
The quantity of information in the world is soaring. Merely keeping up with, and storing new information is difficult enough. Analyzing it, to spot patterns and

most data  revenue. Yet making the most of data is not easy. The first step is to improve the accuracy of the information. Nestlé, for example, sells more than 100,000 products in 200 countries, using 550,000 suppliers, but it was not using its huge buying power effectively because its databases were a mess. On examination, it found that of its 9m records of vendors, customers and materials around half were obsolete or duplicated, and of the remainder about one-third were inaccurate or incomplete. The name of a Read More...
Data Management and Business Performance: Part 1-Data
Research for one of my projects led me to ask both software vendors and customers about the factors most important to software users in the selection of a

most data  address them all. The most important thing we can do to ensure a more efficient data management process is stay attuned to the changing business priorities of the organization. As always, I welcome your thoughts—leave a comment below, and I’ll respond as soon as I can. Read More...
Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

most data  has advanced considerably, and most data integration platforms offer a variety of features tailored for most business scenarios. Validation checks if every data value follows specified business rules. If, for instance, the data format of a social security number is incorrect, or a mandatory data value is missing, a validation procedure can flag and even clean or correct the data value. Complex business rules specific to the business environment can also be built to validate permissible data values where Read More...
Unified Data Management: A Collaboration of Data Disciplines and Business Strategies
In most organizations today, data are managed in isolated silos by independent teams using various data management tools for data quality, integration

most data  and Business Strategies In most organizations today, data are managed in isolated silos by independent teams using various data management tools for data quality, integration, governance, and so on. In response to this situation, some organizations are adopting unified data management (UDM), a practice that holistically coordinates teams and integrates tools. This report can help your organization plan and execute effective UDM efforts. Read More...
Considerations for Owning versus Outsourcing Data Center Physical Infrastructure
When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both

most data  for Owning versus Outsourcing Data Center Physical Infrastructure When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both quantitative and qualitative differences to consider. The 10-year TCO may favor upgrading or building over outsourcing; however, this paper demonstrates that the economics may be overwhelmed by a business’ sensitivity to cash flow, cash crossover point, deployment timeframe, Read More...
Data Quality: A Survival Guide for Marketing
Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge

most data  Quality: A Survival Guide for Marketing Even with the finest marketing organizations, the success of marketing comes down to the data. Ensuring data quality can be a significant challenge, particularly when you have thousands or even millions of prospect records in your CRM system and you are trying to target the right prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more. Read More...
The Necessity of Data Warehousing
An explanation of the origins of data warehousing and why it is a crucial technology that allows businesses to gain competitive advantage. Issues regarding

most data  still in their infancy. Most operational data is stored in what is referred to as an OLTP (On-Line Transaction Processing) system. These systems are specifically designed for high levels of transaction volume with many concurrent users. If the database is relational, it has probably been normalized (the process of organizing data in accordance with the rules of a relational database). If the database is non-relational, custom programs have to be written to store and retrieve data from the database. Read More...
Network Data Protection Playbook: Network Security Best Practice for Protecting Your Organization
Malicious hacking and illegal access are just a few of the reasons companies lose precious corporate data every year. As the number of network security breaches

most data  avenue of attack, as most data loss occurs via the network. So the network is where an end-to-end security infrastructure should best be established. In addition to a strong perimeter defense and effective access control, a comprehensive data protection strategy should be a primary focus. After all, the key attribute of a defense-in-depth architecture is that if one layer of security is bypassed or doesn''t defend against an attack, the next one will. A comprehensive data protection strategy will still Read More...
Data Quality: Cost or Profit?
Data quality has direct consequences on a company''s bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and

most data  of data is the most common type of data quality issue. Customer record duplication is caused by a multitude of situations. It can occur from variations in spelling or assigning multiple addresses to the same customer profile. As a result, data becomes corrupted and is then misinterpreted. Having a customer in a database two or more times may create the impression that they are different customers. Businesses may lose money if, for example, they do a mailing campaign based on this customer base. Multiple Read More...
Customer Data Integration: A Primer
Customer data integration (CDI) involves consolidation of customer information for a centralized view of the customer experience. Implementing CDI within a

most data  loads are likely the most important factors to consider. Also, different vendors within the data integration space may specialize in sub-categories such as data quality, and may partner with larger industry- or solution-specific vendors to have their solutions embedded within larger software packages. This gives organizations the ability to mix and match solutions based on their needs. Considerations for CDI CDI requires specialized data integration solutions. Aside from the general data integration Read More...
Meet PCI DSS Compliance Requirements for Test Data with Data Masking
Whether you’re working toward your first or your next payment card industry (PCI) data security standard (DSS) audit, you know compliance is measured on a

most data  PCI DSS Compliance Requirements for Test Data with Data Masking Whether you’re working toward your first or your next payment card industry (PCI) data security standard (DSS) audit, you know compliance is measured on a sliding scale. But full compliance can’t be achieved with just one policy or technology. Using data masking, a technology that alters sensitive information while preserving realism, production data can be eliminated from testing and development environments. Learn more. Read More...
Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio
Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the

most data  your view, the two most important advances to date in the data warehouse and BI space? The number one advance to date is massively parallel processing, or MPP. Without it we could not possibly cope with the ever-increasing data volumes and analytic complexity that business applications command. While Moore’s Law , which dictates that processing power doubles every two years, is still valid for most applications, it does not ring true in the data warehousing space, where data volumes double every year. Read More...

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