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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
Business Basics: Unscrubbed Data Is Poisonous Data
Most business software system changes falter--if not fail--because of only a few root causes. Data quality is one of these root causes. The cost of high data

most data  . Project Risk Analysis Most business software system changes falter--if not fail--because of only a few root causes, among which poor data quality is high on the list. In this case, not only was data quality seen to be a troublesome issue for implementation of a new system, it was apparent that it was at the root of the material management problems that had been plaguing the company for more than a year. There were several telltale operational symptoms: 150 purchase orders and 115 change orders per 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
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
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
TCO Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center
Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30

most data  Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30% compared with traditional, built-out data center power and cooling infrastructure. Avoiding overbuilt capacity and scaling the design over time contributes to a significant percentage of the overall savings. This white paper provides a quantitative TCO analysis of the two Read More
Data Migration Management: A Methodology to Sustaining Data Integrity for Going Live and Beyond
For many new system deployments, data migration is one of the last priorities. Data migration is often viewed as simply transferring data between systems, yet

most data  Migration Management: A Methodology to Sustaining Data Integrity for Going Live and Beyond For many new system deployments, data migration is one of the last priorities. Data migration is often viewed as simply transferring data between systems, yet the business impact can be significant and detrimental to business continuity when proper data management is not applied. By embracing the five phases of a data migration management methodology outlined in this paper, you can deliver a new system with Read More
Developing a Universal Approach to Cleansing Customer and Product Data
Data quality has always been an important issue for companies, and today it’s even more so. But are you up-to-date on current industry problems concerning data

most data  fully enforce those rules. Most data quality initiatives focus almost exclusively on structured data, which represents only a fraction of the data that exists in an organization. There is, however, increasing interest in leveraging the value of unstructured data in both master data and business intelligence applications. This is especially the case for customer and product data, which may be managed in unstructured files, or encapsulated in a single field with a structured file or database. To be useful 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
Data Quality Strategy: A Step-by-Step Approach
To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how

most data  Quality Strategy: A Step-by-Step Approach To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how to clean it and how to keep it clean. Those organizations that approach this issue strategically will be successful. But what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality. 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 Warehousing in the Big Data Era: Are You BIReady?
Netherlands-based BIReady automates the process of designing, deploying, and maintaining a data warehousing solution, allowing the optimization of all necessary

most data  Warehousing in the Big Data Era: Are You BIReady? Netherlands-based BIReady automates the process of designing, deploying, and maintaining a data warehousing solution, allowing the optimization of all necessary BI and analytics tasks. Read this TEC product note from TEC senior BI and data management analyst Jorge Garcia to learn more about how BIReady is meeting its goal of helping companies become BI ready. Read More
Big Data: Operationalizing the Buzz
Integrating big data initiatives into the fabric of everyday business operations is growing in importance. The types of projects being implemented

most data  Data: Operationalizing the Buzz Integrating big data initiatives into the fabric of everyday business operations is growing in importance. The types of projects being implemented overwhelmingly favor operational analytics. Operational analytics workloads are the integration of advanced analytics such as customer segmentation, predictive analytics, and graph analysis into operational workflows to provide real-time enhancements to business processes. Read this report to learn more. Read More

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