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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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 implementing real time data warehousing owb

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

Business Intelligence (BI)

Business intelligence (BI) and performance management applications enable real-time, interactive access, analysis, and manipulation of mission-critical corporate information. These applications provide users with valuable insights into key operating information to quickly identify business problems and opportunities. Users are able to access and leverage vast amounts of information to analyze relationships and understand trends that ultimately support business decisions. These tools prevent the potential loss of knowledge within the enterprise that results from massive information accumulation that is not readily accessible or in a usable form. It is an umbrella term that ties together other closely related data disciplines including data mining, statistical analysis, forecasting, and decision support. 

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Documents related to » implementing real time data warehousing owb

Real-time In-memory Technologies Do Not Make Data Warehousing Obsolete


The benefits of well thought-out architecture for designing and implementing large-scale DW environments are well-documented. Perhaps the best known of the DW architectures is the Corporate Information Factory. Since the creation of this architecture, there have been many technological advances, making its implementation faster, more scalable, and better performing. The data warehouse is no longer “off limits” to the business community; self-service BI, more sophisticated types of analytics, and true experimental (data science) analyses can now be performed with ease, and an increase in productivity and agility and flexibility of overall BI deployments is the result. Learn more in this white paper authored by one of the co-developers of the corporate information factory DW architecture.

implementing real time data warehousing owb  architecture for designing and implementing large-scale DW environments are well-documented. Perhaps the best known of the DW architectures is the Corporate Information Factory. Since the creation of this architecture, there have been many technological advances, making its implementation faster, more scalable, and better performing. The data warehouse is no longer “off limits” to the business community; self-service BI, more sophisticated types of analytics, and true experimental (data science) Read More

Best Practices for a Data Warehouse on Oracle Database 11g


Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things correctly can help you scale your EDW without constantly tuning or tweaking the system.

implementing real time data warehousing owb  will only deal with implementing S.A.M.E using a combination of hardware and ASM features. From the hardware perspective, build redundancy in by implementing mirroring at the storage subsystem level using RAID 1. Once the RAID groups have been create you can turn them over to ASM. ASM provides filesystem and volume manager capabilities, built into the Oracle Database kernel. To use ASM for database storage, you must first create an ASM instance. Once the ASM instance is started, you can create your ASM Read More

5 Keys to Automated Data Interchange


The number of mid-market manufacturers and other businesses using electronic data interchange (EDI) is expanding—and with it, the need to integrate EDI data with in-house enterprise resource planning (ERP) and accounting systems. Unfortunately, over 80 percent of data integration projects fail. Don’t let your company join that statistic. Learn about five key steps to buying and implementing EDI to ERP integration software.

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Attaining Real Time, On-demand Information Data: Contemporary Business Intelligence Tools


Demand for instant access to dispersed information is being met by vendors offering enterprise business intelligence tools and suites. Portlet standardization, enterprise information integration, and corporate performance management are among the proposed solutions, but do they really deliver real time information?

implementing real time data warehousing owb  feel somewhat better about implementing cumbersome ERP and other enterprise systems. Indeed, many enterprises have already deployed some BI products too, such as querying and reporting tools, planning and budgeting applications, analytic applications, incentive management systems, portals, dashboards, and scorecards, along with data warehousing technology, data models, and integration software, and whatnot. In fact, anyone stocktaking technology inventory will likely find some CPM components already in 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.

implementing real time data warehousing owb  processes that they are implementing. Allow time in your planning for changes to the mapping, business rules, and re-loads. It is not uncommon for there to be confusion during beta testing and implementation as to where errors originate. It''s all too easy for bad data to be blamed for every test error. What can you do as part of your planning to avoid this? Implement the migration with as much metadata and reconciliation as possible. Not only are you ensuring the integrity of your data, you are Read More

Revamping Data Management: Big Data Proves Catalyst to Updating Data Management Strategies


Data management plays a key role in helping organizations make strategic sense of their data and how to best use it. Organizations with data management maturity have ushered in clear data goals, but many obstacles persist. This white paper reports survey results that help to establish a clear picture of how organizations are capitalizing on data management today, as well as what challenges and opportunities remain.

implementing real time data warehousing owb  data management, data management strategy, managing complex data sources, big data, data management adoption, big data management 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.

implementing real time data warehousing owb  architectures data centers,data center network,scalable data center architecture,data center infrastructure,data center consolidation,data center virtualization,reference architecture data center,data centre designs,commodity data center Read More

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


In Data Storage in the Cloud Can You Afford Not To?

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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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Data Blending for Dummies


Data analysts support their organization’s decision makers by providing timely key information and answers to key business questions. Data analysts strive to use the best and most complete information possible, but as data increases over time, so does the time required to identify and combine all data sources that might be relevant.

Data blending allows data analysts a way to access data from all data sources, including big data, the cloud, social media sources, third-party data providers, department data stores, in-house databases, and more, and become faster at delivering better information and results to their organizations. In the past, the challenge for data analysts has been accessing this data and cleansing and preparing the data for analysis. The access, cleansing, and preparing data stages are complex and time intensive. These days, however, software tools can help reduce the burden of data preparation, and turn data blending into an asset.

Read this e-book to understand why data blending is important, and learn how combining data means that you can get answers to your business questions and better meet your business needs. Also learn how to identify what features to look for in data blending software solutions, and how to successfully deploy these tools within your business. Data Blending for Dummies breaks the subject down into digestible sections, from understanding data blending to using data blending in the real world. Read on to discover how data blending can help your organization use its data sources to the utmost.

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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.

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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.

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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.

implementing real time data warehousing owb   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.

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