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Software Functionality Revealed in Detail
We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.
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Visit the TEC store to compare leading software solutions by funtionality, so that you can make accurate and informed software purchasing decisions.
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 most data

Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

Get free sample report
Compare Software Solutions

Visit the TEC store to compare leading software by functionality, so that you can make accurate and informed software purchasing decisions.

Compare Now

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 reading for industries relying heaviling on equipment. FSM workers require functionality for customer engagement management, service and asset management as well as workforce management. Since most activities in FSM take place outside of the office, mobility is a big component of the a FSM software solutions. Typically, FSM software is not used as a stand-alone solution, as it needs to integrate with Financials, ERP, CRM and EAM to ensure accurate data exchange. Even if its main purpose is to maintain and repair equipment, it can also be used to gather customer satisfaction and equipment performance feedback. To allocate human resources efficiently, workforce management is an integral part of an FSM system. 

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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 that they are using master data, when in fact what they are relying on is data that is dispersed throughout the enterprise. Discover the importance of master data and how the ideal master data management (MDM) solution can help your business get it under control.

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

The Path to Healthy Data Governance through Data Security


Companies today are challenged to maintain their data safe and secure from hackers and others with unauthorized access. In his article, TEC business intelligence (BI) analyst Jorge García looks the risks and issues that companies face with securing their data, the importance and advantages of data security, and outlines a path that companies can follow to achieve data security as part of an overall data governance initiative.

most data  to focus on the most relevant and critical issues to be solved, otherwise you risk not delivering effective results. The identification process can potentially help you define the potential use case (i.e., initial objective) and initial scope of your strategy. You need to ask yourself some questions: What are the more relevant risks or critical issues regarding your information security? If you already have an ongoing data governance program, do you need to modify your ongoing initiative to meet this new 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 quality is low, and the short- and long-term benefits are great.

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

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 business intelligence (BI) solution. Two topics resounded: the use of BI tools to improve data management and business performance management. Consumers are continuously looking for innovative ways to move, store, and improve the quality of

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 environment. This article looks at issues in data quality and how they can be addressed.

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

Increasing Sales and Reducing Costs across the Supply Chain-Focusing on Data Quality and Master Data Management


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.

most data  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. 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, 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.

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

New Data Protection Strategies


One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased demand for data capacity and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment. Learn about data protection strategies that can help organizations meet these demands while maintaining flat budgets.

most data  Data Protection Strategies One of the greatest challenges facing organizations is the protection of corporate data. The issues complicating data protection are compounded by increased demand for data capacity and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment. Learn about data protection strategies that can help organizations meet these demands while maintaining flat budgets. 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% 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 architectures, and illustrates the key drivers of both the capex and opex savings of the improved architecture.

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

A Road Map to Data Migration Success


Many significant business initiatives and large IT projects depend upon a successful data migration. But when migrated data is transformed for new uses, project teams encounter some very specific management and technical challenges. Minimizing the risk of these tricky migrations requires effective planning and scoping. Read up on the issues unique to data migration projects, and find out how to best approach them.

most data  fit to use. The most important phase in a data migration project involves the tasks needed to understand the data content. You would not put dirty petrol in your new car—why would you map bad data into your new software application? It''s unlikely that anyone on your team really understands the current state of the data at the level of detail needed for your project. And even if the source data is pristine, that doesn''t mean that it is fit for the requirements of the new application. Experience shows Read More

Data Management and Analysis


From a business perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it is also a resource for acquiring knowledge and supporting the decisions companies need to make in all aspects of economic ventures, including mergers and acquisitions (M&As).

For organizational growth, all requirements and opportunities must be accurately communicated throughout the value chain. All users—from end users to data professionals—must have the most accurate data tools and systems in place to efficiently carry out their daily tasks. Data generation development, data quality, document and content management, and data security management are all examples of data-related functions that provide information in a logical and precise manner.

most data  data professionals—must have the most accurate data tools and systems in place to efficiently carry out their daily tasks. Data generation development, data quality, document and content management, and data security management are all examples of data-related functions that provide information in a logical and precise manner. Read More

Scalable Data Quality: A Seven-step Plan for Any Size Organization


Every record that fails to meet standards of quality can lead to lost revenue or unnecessary costs. A well-executed data quality initiative isn’t difficult, but it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

most data  what would be the most effective point to enforce data quality. Step 4: Technology assessment Deciding upon an approach to data quality requires a thorough and realistic appraisal of the technology and resources available within the organization. Ask yourself the following questions: If data quality is to be enforced via a web site, is it feasible to integrate the technology into the web site or will the site have to be retooled to make it possible? Is the volume of addresses to be verified small enough Read More

Overall Approach to Data Quality ROI


Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company. Of the many benefits that can accrue from improving the data quality of an organization, companies must choose which to measure and how to get the return on investment (ROI)—in hard dollars. Read this paper to garner an overall approach to data quality ROI.

most data  Approach to Data Quality ROI Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company. Of the many benefits that can accrue from improving the data quality of an organization, companies must choose which to measure and how to get the return on investment (ROI)—in hard dollars. Read this paper to garner an overall approach to data quality ROI. Read More

Achieving a Successful Data Migration


The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

most data  wags the dog because most data problems are identified too late. Since there is little time left to fix the problem, most application projects wind up compromised in some way reduced functionality, budget overrun, or late delivery. For example, if the application is a new inventory management system that is supposed to reduce inventory by $20 million annually, then every day that application is late may be costing the business $55,000. Anecdotal evidence suggests that the data migration phase can Read More