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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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 data collection forms


Meeting the Challenges of Product Traceability with Automated Data Collection
An effective traceability system involves determining which product and manufacturing process attributes to collect and maintain@and deciding when during the

data collection forms  Product Traceability with Automated Data Collection The backbone of Radley's WorkForce Productivity Solutions is x/DC, a .Net, XML based data collection development and integration platform. x/DC’s client/server architecture offers scalability, performance and availability unachievable by other solutions. Source : Radley Corporation Resources Related to Meeting the Challenges of Product Traceability with Automated Data Collection : Traceability (Wikipedia) Data Collection (Wikipedia) Meeting the

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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.

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

Enterprise Content Management (ECM)

A content management system is a software package designed to manage an organization''s entire collection of documents, records, and other information assets. Content management systems are built on a central repository, which holds all content, metadata, rules, and supporting information. Around this, processes and disciplines are established that ensure that day-to-day activities run smoothly and efficiently.  

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New Data Triggers for International Supply Chain Finance


Corporations have drastically changed their global supply chain models over a short period. The market is also in the early stages of migrating to data triggered finance, creating opportunities for banks and logistic companies to develop international supply chain finance solutions.

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3 Big Trends in Data Visualization


The greatest value of a picture is when it forces us to notice what we never expected to see. —John W. Tukey. Exploratory Data Analysis. 1977. Data visualization is core to business intelligence (BI), as it’s the means by which data is communicated to the user. Results, alerts suggestions, or even more controlled guidance—all of these elements are passed through the data visualization

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A Definition of Data Warehousing


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.

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Understanding the PCI Data Security Standard


The payment card industry data security standard (PCI DSS) defines a comprehensive set of requirements to enhance and enforce payment account data security in a proactive rather than passive way. These include security management, policies, procedures, network architectures, software design, and other protective measures. Get a better understanding of the PCC DSS and learn the costs and benefits of compliance.

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2013 Big Data Opportunities Survey


While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry groups are now leveraging big data. A survey of 304 data managers and professionals was conducted by Unisphere Research in April 2013 to assess the enterprise big data landscape, the types of big data initiatives being invested in by companies today, and big data challenges. Read this report for survey responses and a discussion of the results.

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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.

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The Operational Data Lake: Your On Ramp to Big Data


Companies recognize the need to integrate big data into their real-time analytics and operations, but this poses a lot of technical and resource challenges. Meanwhile, those organizations that have operational data stores (ODSs) in place find that, while useful, they are expensive to scale. The ODS gives real-time visibility into operational data. While more cost-effective than a data warehouse, it uses outdated scaling technology, and performance upgrades require very specialized hardware. Plus, ODSs just can't handle the volume of data that has become a matter of fact for businesses today.

This white paper discusses the concept of the operational data lake, and its potential as an on-ramp to big data by upgrading outdated ODSs. Companies that are building a use case for big data, or those considering an upgrade to their ODS, may benefit from this stepping stone. With a Hadoop relational database management system (RDBMS), companies can expand their big data practices at their own pace.

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The Path to Healthy Data Governance


Many companies are finally treating their data with all the necessary data quality processes, but they also need to align their data with a more complex corporate view. A framework of policies concerning its management and usage will help exploit the data’s usefulness. TEC research analyst Jorge Garcia explains why for a data governance initiative to be successful, it must be understood as a key business driver, not merely a technological enhancement.

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Making Big Data Actionable: How Data Visualization and Other Tools Change the Game


To make big data actionable and profitable, firms must find ways to leverage their data. One option is to adopt powerful visualization tools. Through visualization, organizations can find and communicate new insights more easily. Learn how to make big data more actionable by using compelling data visualization tools and techniques.

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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.

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