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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

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.

integration maturity model data  Requirement , Schedule , Integration , Project , Analysis , Audit , Best Practices , Checklist , Conversion , Cost , Download , Guide , Issues , Specialist , Essential Data Migration , Scope Data Migration Effort . Data migration is the one-time movement of data from a legacy source, or multiple sources, to a new target database. This simple concept and requirement can drive a scope that is much larger than expected. A data migration requirement can be driven by a range of initiatives, such as an 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.

integration maturity model data  Level of Automation , Integration Maturity Model , Automation Processes and Systems Maturity , Take Sample Automation Maturity , Review Automation Fundamentals , Main Automation Maturity Levels , Basic Level of Automation Maturity , Test Automation Maturity Efforts , Enterprise Continuous Integration Maturity Model , Scoring the Maturity of Organizations Automation , Release Automation Maturity Survey , Data Centre Automation , Test Management Automation , Advancing in Automation Maturity , Automation 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

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 quality? Do you know how to address quality problems with customer, product, and other types of corporate data? Discover how data cleansing tools help improve data constancy and accuracy, and find out why you need an enterprise-wide approach to data management.

integration maturity model data  Management: Data Profiling, Data Integration and Data Quality ) by David Loshin that discusses this topic in detail and is recommended reading. Types of Data Involved in Ma naging Data Quality A data quality program involves many different types of data. The data may be current or historic, detailed or summarized, and may have a structured, unstructured, or semi-structured format. It may consist of master business entity or master reference data, or activity data created by business transaction (order Read More

The Advantages of Row- and Rack-oriented Cooling Architectures for Data Centers


The traditional room-oriented approach to data center cooling has limitations in next-generation data centers. Next-generation data centers must adapt to changing requirements, support high and variable power density, and reduce power consumption and other operating costs. Find out how row- and rack-oriented cooling architectures reduce total cost of ownership (TCO), and address the needs of next-generations data centers.

integration maturity model data  problem shows that the integration of humidification equipment into air conditioners as is commonly done is fundamentally flawed, and that humidification should be separate from air conditioning equipment and done at the room level. This is for three reasons: Higher density installations may have a large number of CRAC units no matter which architecture is chosen; there is no technical need to have as many humidification units and there are many practical disadvantages, such as maintenance, of having 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.

integration maturity model data  DCIM, data center, data center infrastructure management, DCIM management, IT, IT infrastructure, APC by Schneider Electric, facility operations and maintenance, data center facility operations, data center PUE, PUE, data center cooling module, data center power and cooling infrastructure, traditional data center, containerized data center, data center TCO Read More

The New Virtual Data Centre


Old-style, one application per physical server data centers are not only nearing the end of their useful lives, but are also becoming barriers to a business’ future success. Virtualization has come to the foreground, yet it also creates headaches for data center and facilities managers. Read about aspects of creating a strategy for a flexible and effective data center aimed to carry your business forward.

integration maturity model data  Data Management,Data Infrastructure,Data Center,Knowledge Management,Cost Control,IT Budgeting,Outsourcing,Strategic Planning,Business Applications,Business Management,data management software,data management technology,enterprise data management,data management services,customer data management Read More

The Value of Big Data


As the use of big data grows, the need for data management will also grow. Many organizations already struggle to manage existing data. Big data adds complexity, which will only increase the challenge. This white paper looks at what big data is, the value of big data, and new data management capabilities and processes, required to capture the promised long-term value.

integration maturity model data  big data,data management,analytics,SAP Read More

Top 10 Evaluation Criteria for Copy Data Management & Data Virtualization


Data virtualization is becoming more important, as industry-leading companies learn that it delivers accelerated IT projects at a reduced cost. With such a dynamic space, one must make sure that vendors will deliver on their promises. This white paper outlines 5 qualification questions to ask before and during the proof of concept (POC), and 5 things to test during the POC.

integration maturity model data  data management, data virtualization, proof of concept, POC, data center, provisioning, data security, copy data management, CDM Read More

Data Storage in the Cloud-Can you Afford Not To?


Storing data in the cloud using Riverbed Technology’s Whitewater cloud storage gateway overcomes a serious challenge for IT departments: how to manage the vast, and ever-growing, amount of data that must be protected. This paper makes the business case for cloud storage, outlining where capital and operational costs can be eliminated or avoided by using the cloud for backup and archive storage.

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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 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.

integration maturity model data  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

Demystifying Data Science as a Service (DaaS)


With advancements in technology, data science capability and competence is becoming a minimum entry requirement in areas which have not traditionally been thought of as data-focused industries. As more companies perceive the significance of real-time data capture and analysis, data as a service will become the next big thing. India is now the third largest internet user after China and the U.S., and the Indian economy has been growing rapidly. Read this white paper to find out more about how data SaaS is set to become a vital part of business intelligence and analytics, and how India will play a role in this trend.

integration maturity model data  data science, big data, analytics, data SaaS, data scientist, India, data as a service, real-time data, data analytics Read More