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Software Functionality Revealed in Detail
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 integration maturity model 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

ERP for the Oil and Gas Industry (Upstream)

The model of ERP systems for the upstream oil and gas sector addresses the particularly specialized business model of this industry. It is common practice for companies, individuals, and government agencies to form partnerships to explore, develop, and share production of oil wells. More often than not, this is a short-term alliance rather than a long-term partnership, and business structuring must therefore be flexible at initial set-up and throughout the lifespan of the venture. This model of ERP systems includes criteria for financials, human resources, production data capture and reporting, maintenance management, and supply chain management (SCM) functionality. 

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Documents related to » integration maturity model data

There Is No Execution without Integration


In fast-paced, low-margin manufacturing environments, companies must embrace technology in order to solidify or gain competitive advantages. It is equally important to avoid adopting technology for technology’s sake. Find out how leading companies are focusing on efficiency and cost reduction by integrating manufacturing execution systems (MES) or manufacturing intelligence (MI) with enterprise resource planning (ERP).

integration maturity model data  Execution Integration , MES Integration Systems , ERP Manufacturing Execution , MI Manufacturing Execution . Executive Summary Many manufacturers today are getting caught on the wrong side of a technology adoption wave that is quickly gaining momentum. Of the more than 200 surveyed manufacturers 54% are planning on adopting MI (Manufacturing Intelligence) within the next 24 months. Additionally, 56% of manufacturers having already implemented MES (Manufacturing Execution Systems) did so within the past Read More

Is the SaaS Model Right for You?


For IT departments drowning in complex and expensive software maintenance chores, the software-as-a-service (SaaS) model can ease the burden. SaaS reduces complexity by outsourcing most of the infrastructure needed to run software applications, and reduces costs by charging only for what is consumed. But you can also adopt a hybrid SaaS model, in which some systems are outsourced and others are kept in-house. Learn more.

integration maturity model data  this application/project dependent on integration with other applications or data? Will we have to customize the application to work in a cloud environment? Vendor Support: Does the cloud provider provide migration support, and support for service/performance issues? Vendor Compliance: Does the cloud provider meet all necessary regulatory requirements for this project/ application/data? Are there comparable instances supported by the provider meeting the same requirements? Is the provider open to audit Read More

Evolution to Revolution: The Test Automation Maturity Curve


The evolution of test automation towards data-driven and key/action word frameworks reflects the realization that the process becomes more efficient if there is less code to develop and maintain. Instead of taking twenty years to evolve towards efficiency, you can take a revolutionary leap with a code-free approach that makes it easier to implement, manage, and maintain automated tests.

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A One-stop Event for Business Intelligence and Data Warehousing Information


The Data Warehousing Institute (TDWI) hosts quarterly World Conferences to help organizations involved in data warehousing, business intelligence, and performance management. These conferences supply a wealth of information aimed at improving organizational decision-making, optimizing performance, and achieving business objectives.

integration maturity model data  design of the platform. Integration questions center on whether the current systems will integrate with the new software—and more importantly, how they will integrate. Data Integration The theme of data integration included all the topics related to implementing a data warehouse solution. Included were data profiling; data transformation; data cleansing; source and target mapping; data cleansing and transformation; and extract, transform, and load (ETL) development. It is important not to underestimate Read More

Reaching the Peak of CMMI: How Fast Can You Climb?


Implementing Capability Maturity Model Integration (CMMI) at Maturity Level 5 enables an organization to optimize its performance. Learn about the critical success factors for CMMI High Maturity level appraisals; world-class practice for establishment and coaching of a local SEPG group; and the secrets of one organization’s rapid implementation of CMMI High Maturity practices.

integration maturity model data  implementing Capability Maturity Model Integration (CMMI) 1 at Maturity Levels 2 and 3 2 usually contributes to some level of improved performance, successfully implementing Maturity Level 5 enables an organization to begin to truly optimize its performance. This distinction is even more noteworthy when an organization is able to successfully mature rapidly. A division of a large international systems integration company headquartered in Seoul, South Korea (LG CNS, LG Insurance Sector) reached this 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 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.

integration maturity model data   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, 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.

integration maturity model data  prospect. Data quality, data integration, and other functions of enterprise information management (EIM) are crucial to this endeavor. Read more. Read More

Re-think Data Integration: Delivering Agile BI Systems with Data Virtualization


Today’s business intelligence (BI) systems have to change, because they’re confronted with new technological developments and new business requirements, such as productivity improvement and systems as well as data in the cloud. This white paper describes a lean form of on-demand data integration technology called data virtualization, and shows you how deploying data virtualization results in BI systems with simpler and more agile architectures that can confront the new challenges much easier.

integration maturity model data  form of on-demand data integration technology called data virtualization, and shows you how deploying data virtualization results in BI systems with simpler and more agile architectures that can confront the new challenges much easier. 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 increase, companies must find ways to protect data beyond the perimeter of their businesses. But how do they build a data-defensible architecture that will protect data on an ever-evolving network? The answer: by first developing an in-depth defense strategy.

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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.

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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.

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Integration Validation of Networked Solutions


When implementing and running solution landscapes that drive mission-critical business processes, solution integration can be complex and challenging. Implementation work, typically distributed across many teams and many stakeholders, may include custom-built and third-party software. This paper discusses how you can make sure your solutions are integrated correctly so they run smoothly, without technical interruption.

integration maturity model data  mission-critical business processes, solution integration can be complex and challenging. Implementation work, typically distributed across many teams and many stakeholders, may include custom-built and third-party software. This paper discusses how you can make sure your solutions are integrated correctly so they run smoothly, without technical interruption. 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 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, data center life expectancy, regulatory requirements, and other strategic factors. This paper discusses how to assess these key factors to help make a sound decision.

integration maturity model data  DCIM, data center, data center infrastructure management, DCIM management, DCIM software, DCIM software tools, IT, IT infrastructure, APC by Schneider Electric, facility operations and maintenance, data center life cycle, data center facility operations, data center PUE, PUE, data center physical infrastructure, data center infrastructure outsourcing Read More

Don't Be Overwhelmed by Big Data


Big Data. The consumer packaged goods (CPG) industry is abuzz with those two words. And while it’s understandable that the CPG world is excited by the prospect of more data that can be used to better understand the who, what, why, and when of consumer purchasing behavior, it’s critical CPG organizations pause and ask themselves, “Are we providing retail and executive team members with “quality” data, and is the data getting to the right people at the right time? Big Data can be a Big Deal - read this white paper for some useful tips on ensuring secure, quality data acquisition and management.

integration maturity model data  big data white paper, consumer packaged goods, CPG industry, CPG big data, big data CPG, data acquisition CPG, CPG data acquisition, data CPG, CPG data, CPG LumiData 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.

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