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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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 technical data analyst

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

Core PLM--Product Data Management - Discrete RFI/RFP Template

Product Data Management (PDM), Engineering Change Order and Technology Transfer, Design Collaboration, Process and Project Management, Product Technology  

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Documents related to » technical data analyst

Captured by Data


The benefits case for enterprise asset management (EAM) has been used to justify huge sums in EAM investment. But to understand this reasoning, it is necessary to explore how asset data can be used to further the aims of maintenance.

technical data analyst  Maintenance , Springfield: National Technical Information Service, US Department of Commerce) Critical failures are, by their very nature, serious. When they occur they are often designed out, or a replacement asset is installed, or some other initiative is put in place to ensure that they don''t recur. As a result, the volume of data available for analysis is often small, and therefore the ability of statistical analysis to deliver results within a high level of confidence is questionable at best. This Read More

TEC Research Analyst Roundtable: Predictions for 2011


It’s that time of year again for TEC’s analysts to polish their crystal balls and spread their tarot cards to gaze on the future of enterprise software for 2011.Aleksey Osintsev, Research Analyst—Enterprise Resource Planning The growing interest of businesses of all sizes in so-called cloud technologies in general and in on-demand—cloud or software as a service (SaaS)—enterprise

technical data analyst  more user-centric and less technical. Kurt Chen, Research Analyst—Product Lifecycle Management The year 2010 in the product lifecycle management (PLM) space has seen some exciting undertakings: social product development, computer-aided design (CAD) interoperability enhancements, PLM moving closer to the cloud, better supports for design and engineering decision making, and more. Although there were various approaches behind this year’s events, connectivity is a common thread, and it will become an ev Read More

December 2012 Boston Analyst Roadshow Snapshot


I am glad I was among the analysts invited to the traditional December analyst roadshow, which takes place in the beautiful city of Boston, by the event organizer, Judith Rothrock, the energetic and vibrant president of JRocket Marketing. In this event, several software vendors announce their latest software offerings and convene with analysts for friendly and informal discussions. The 2012 event

technical data analyst  that describe the unique technical and business approach taken by UNIT4. The first is the Eval-Source report on UNIT4’s next-generation multitenancy, describing advantages over the traditional multitenant model in terms of reduced number of redeployment steps and overall process simplification. The second is UNIT4 Coda Financials'' survey results, which discovered intriguing data that only 56 percent of mid-market CFOs that use Coda Financials software in North America and Europe take all of their Read More

Analyst Take on SAPPHIRE 2013


With a very interesting book presentation on "The Human Face of Big Data," announcements on cloud-based solutions, and extensive and intensive discussions regarding the readiness (or not) of HANA for prime-time deployments in the enterprise, the recent SAPPHIRE 2013 conference was full of exciting and interesting developments—though, I must admit, I was disinterested at times by the repetitive

technical data analyst  of investment, a reduced technical footprint, and faster results, all while providing an improved user experience. Read More

CMOs Thriving in the Age of Big Data


CRM analyst Raluca Druta interviews TEC’s marketing specialist.Recently, the task of selecting customer relationship management (CRM) software tools appears to reside in the front yard of the chief marketing officer (CMO). Is this a natural evolution of the CMO’s job responsibilities?It makes sense. The CMO is responsible for making the most of the data that passes through the CRM system. But to

technical data analyst  to summarize what their technical expertise amounts to? Yes. They have no choice. With so much data available now, marketers live and die by the numbers. They have to measure and test everything. But knowing what to measure and how to test isn’t easy. These are definitely technical skills, and marketing departments are always looking for people who are comfortable with data. Consequently, the CMOs have to be more technical in order to direct what their departments are doing. The flipside here is that Read More

The Evolution of a Real-time Data Warehouse


Real-time data warehouses are common in some organizations. This article reviews the basic concepts of a real-time data warehouse and it will help you determine if your organization needs this type of IT solution.

technical data analyst  is generated. Despite the technical difficulties of implementing a true real-time data warehouse, there are some advantages. It shortens information delivery times. It improves integration throughout the organization. It eases the analysis of future trends. Basic Principles to Consider With the growing popularity and increasing implementation of real-time data warehouses, it is important to consider some basic principles when considering a real-time data warehouse implementation. Data on Time, at the Righ 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 company policies that set expectations and establish data management procedures, we will explore applications and tools that help reduce the negative impact of poor data quality. Some CRM application providers like Interface Software have definitely taken data quality seriously and are contributing to solving some data quality issues.

technical data analyst  Quality: Cost or Profit? Market Overview In the past year, TEC has published a number of articles about data quality. ( Poor Data Quality Means A Waste of Money ; The Hidden Role of Data Quality in E-Commerce Success ; and, Continuous Data Quality Management: The Cornerstone of Zero-Latency Business Analytics .) This time our focus takes us to the specific domain of data quality within the customer relationship management (CRM) arena and how applications such as Interaction from Interface Software can Read More

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.

technical data analyst  master data. At a technical level, you can indeed link new software solutions obtained in an acquisition, but fundamental incongruities between master data models routinely impede true integration at the business process level. OVERCOMING BARRIERS TO MASTER DATA MANAGEMENT EXCELLENCE To achieve effective master data management and improve operating performance, you must adopt a solution that addresses the following three elements: Master data consolidation means matching, normalizing, cleansing, and 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 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.

technical data analyst  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

Backing up Data in the Private Cloud: Meeting the Challenges of Remote Data Protection - Requirements and Best Practices


This white paper describes some of the common challenges associated with protecting data on laptops and at home and remote offices and portrays proven solutions to the challenges of protecting distributed business data by establishing a private cloud/enterprise cloud. Learn which best practices can ensure business continuity throughout an organization with a distributed information technology (IT) infrastructure.

technical data analyst  up Data in the Private Cloud: Meeting the Challenges of Remote Data Protection - Requirements and Best Practices This white paper describes some of the common challenges associated with protecting data on laptops and at home and remote offices and portrays proven solutions to the challenges of protecting distributed business data by establishing a private cloud/enterprise cloud. Learn which best practices can ensure business continuity throughout an organization with a distributed information 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 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 quality data.

technical data analyst  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 quali 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 customer relationship management initiative can help provide organizations with a successful framework to manage data on a continuous basis.

technical data analyst  Data Integration: A Primer Originally published - August 22, 2006 Introduction Implementing a customer data management system can be the difference between success and failure in terms of leveraging an organization''s customer relationship management (CRM) system. Since customers drive profitability, organizations need a way to provide their employees with a single view of the customer and to provide that customer with above-average customer service. Unfortunately, this is not always the case. 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 technology selection and access to historical 'legacy' data are also discussed.

technical data analyst  Necessity of Data Warehousing The Necessity of Data Warehousing M. Reed - August 2, 2000 Why the market is necessary Data warehousing is an integral part of the information age . Corporations have long known that some of the keys to their future success could be gleaned from their existing data, both current and historical. Until approximately 1990, many factors made it difficult, if not impossible, to extract this data and turn it into useful information. Some examples: Data storage peripherals such 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.

technical data analyst  Mallikarjunan has held include technical lead and applications development manager of a team of .NET, data warehousing, and BI professionals for a fashion retail company. In this role, she was responsible for the development, maintenance, and support of Windows and Web-based applications, as well as an operational data store, data marts, and BI applications. Mallikarjunan holds a BSc in computer science from the University of Madras (India), and an MSc in computer science from Anna University in Madras, I Read More

Reinventing Data Masking: Secure Data Across Application Landscapes: On Premise, Offsite and in the Cloud


Be it personal customer details or confidential internal analytic information, ensuring the protection of your organization’s sensitive data inside and outside of production environments is crucial. Multiple copies of data and constant transmission of sensitive information stream back and forth across your organization. As information shifts between software development, testing, analysis, and reporting departments, a large "surface area of risk" is created. This area of risk increases even more when sensitive information is sent into public or hybrid clouds. Traditional data masking methods protect information, but don’t have the capability to respond to different application updates. Traditional masking also affects analysis as sensitive data isn’t usually used in these processes. This means that analytics are often performed with artificially generated data, which can yield inaccurate results.

In this white paper, read a comprehensive overview of Delphix Agile Masking, a new security solution that goes far beyond the limitations of traditional masking solutions. Learn how Delphix Agile Masking can reduce your organization’s surface area risk by 90%. By using patented data masking methods, Delphix Agile Masking secures data across all application lifecycle environments, providing a dynamic masking solution for production systems and persistent masking in non-production environments. Delphix’s Virtual Data Platform eliminates distribution challenges through their virtual data delivery system, meaning your data can be remotely synchronized, consolidated, and takes up less space overall. Read detailed scenarios on how Delphix Agile Data Masking can benefit your data security with end-to-end masking, selective masking, and dynamic masking.

technical data analyst  Data Masking: Secure Data Across Application Landscapes: On Premise, Offsite and in the Cloud Be it personal customer details or confidential internal analytic information, ensuring the protection of your organization’s sensitive data inside and outside of production environments is crucial. Multiple copies of data and constant transmission of sensitive information stream back and forth across your organization. As information shifts between software development, testing, analysis, and Read More