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Documents related to » apa itu data fokus


Six Steps to Manage Data Quality with SQL Server Integration Services
Six Steps to Manage Data Quality with SQL Server Integration Services. Read IT Reports Associated with Data quality. Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.

APA ITU DATA FOKUS: while leveraging the integration capabilities inherent in Microsoft s SQL Server Integration Services (SSIS 2005/2008) to facilitate the assembly of data from one or more data sources. This solution is called Total Data Quality. The Six Steps to Total Data Quality The primary goal of an MDM or Data Quality solution is to assemble data from one or more data sources. However, the process of bringing data together usually results in a broad range of data quality issues that need to be addressed. For
9/9/2009 2:32:00 PM

Four Critical Success Factors to Cleansing Data
Four Critical Success Factors to Cleansing Data. Find Guides, Case Studies, and Other Resources Linked to Four Critical Success Factors to Cleansing Data. Quality data in the supply chain is essential in when information is automated and shared with internal and external customers. Dirty data is a huge impediment to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

APA ITU DATA FOKUS:
1/14/2006 9:29:00 AM

Achieving a Successful Data Migration
Achieving a Successful Data Migration. Solutions and Other Software to Delineate Your System and for Achieving a Successful Data Migration. The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

APA ITU DATA FOKUS: to the data migration capability. If making those adjustments is complex, it can increase the TCO of an application to unsustainable levels. The data migration capability must readily adapt to changes in business requirements, target interfaces, data models, and data requirements before, during, and after implementation. Ensure Quality Data quality is critical to the ultimate value of the application. Bad quality data in the target application will lead to end-user rejection, turning the target
10/27/2006 4:30:00 PM

Data Quality: A Survival Guide for Marketing
Data Quality: a Survival Guide for Marketing. Find Free Blueprint and Other Solutions to Define Your Project In Relation To Data Quality. The success of direct marketing, measured in terms of qualified leads that generate sales, depends on accurately identifying prospects. Ensuring data accuracy and data quality can be a big challenge if you have up to 10 million prospect records in your customer relationship management (CRM) system. How can you ensure you select the right prospects? Find out how an enterprise information management (EIM) system can help.

APA ITU DATA FOKUS: quality and other EIM capabilities is when they build a CRM or customer data integration (CDI) system, and find that the data they’ve loaded into the system is far less than expectations. Throughout this paper, we use CRM as the surrogate for marketing data repositories in general. Somewhere, somehow, customer and prospect data must be stored and accessed, and CRM/CDI systems, whether homegrown or vendor-supplied, are the common repositories for this data. BUT HOW DO I FIND MY DATA QUALITY PROBLEMS?
6/1/2009 5:02:00 PM

Data Quality Strategy: A Step-by-step Approach
Success start with data quality strategy: a step-by-step approach.Read Technology Evaluation Centers (TEC) whitepapers. To realize the full 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. The companies that approach this issue strategically are the companies that will be successful. Learn the six factors that go into a good data quality strategy, and find out how to go from strategy to implementation.

APA ITU DATA FOKUS: SAP, BusinessObjects, business intelligence, data warehousing, data quality, business intelligent, business intelligence software, business intelligence data, business intelligence system, business intelligence tools, data mining warehousing, business intelligence bi, business intelligence solutions, data mining and warehousing, cognos business intelligence, data warehousing data mining, business intelligence data warehousing, business intelligence services, business intelligence solution, business intelligence tool, data warehousing jobs, business intelligence pdf, business intelligence .
1/25/2010 1:13:00 PM

The Data Explosion
RFID and wireless usage will drive up data transactions by ten fold over the next few years. It is likely that a significant readdressing of the infrastructure will be required--in the enterprise and the global bandwidth.

APA ITU DATA FOKUS: manufacturing processes driving up capacity, as well as producing a more powerful chip, prices will come down. Nanotechnologies get introduced about three to four years from now, which will be truly powerful and cheaper! Moore s Law still applies here with the price/performance ratios careening ever higher! So, where are we? On CNN , ABC , and MSNBC . If you think of previous technology shifts, Prime Time came way later. ERP, as a term, was mentioned for the first time on Prime Time about ten years ago.
10/20/2004

Data Mining: The Brains Behind eCRM
Data mining has emerged from obscure beginnings in artificial intelligence to become a viable and increasingly popular tool for putting data to work. Data mining is a set of techniques for automating the exploration of data and uncovering hidden truths.

APA ITU DATA FOKUS: User Recommendations Data mining capabilities can play an important role in successful customer relationship management. In evaluating a data mining package, users should ask in which category, supervised or unsupervised, it falls and whether it is suitable for their needs. For instance, insurance companies seeking to predict insurance fraud should consider packages that use unsupervised learning, as there are typically no baselines to serve as predictors. Consumer products retailers can use unsupervised
11/6/2000

3 Big Trends in Data Visualization » The TEC Blog
provide flexibility and navigation capabilities equal to those of client-based applications. 2. Geolocation According to the Pitney Bowes white paper Location Intelligence: The New Geography of Business , “ More than 80% of all data maintained by an organization has a location component. ” Along with mobility, the incorporation of mapping and geolocation capabilities into BI applications is boosting all phases of the BI process. The ability to handle maps, drill down to information based on a

APA ITU DATA FOKUS: TEC, Technology Evaluation, Technology Evaluation Centers, Technology Evaluation Centers Inc., blog, analyst, enterprise software, decision support.
15-12-2011

Data Migration Best Practices
Large-scale data migrations can be challenging, but with the appropriate planning—and through careful execution of that plan—organizations can greatly reduce the risks and costs associated with these projects. This paper offers a handy checklist of issues to consider before, during, and after migration.

APA ITU DATA FOKUS: data migration, data migration best practices, Globanet, migration software, data migration compliance.
8/8/2013 1:47:00 PM

New Data Protection Strategies
One of the greatest challenges facing organizations is protecting corporate data. The issues that complicate data protection are compounded by increasing demand for data capacity, and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment, which impact infrastructure. IT organizations must meet these demands while maintaining flat budgets. Find out how.

APA ITU DATA FOKUS: increasing demand for data capacity, and higher service levels. Often these demands are coupled with regulatory requirements and a shifting business environment, which impact infrastructure. IT organizations must meet these demands while maintaining flat budgets. Find out how. New Data Protection Strategies style= border-width:0px; />   comments powered by Disqus Related Topics:   Regulatory and Compliance,   IT Infrastructure and Development,   Archival and Disaster Recovery Related Keywords:  
4/23/2010 5:47:00 PM

A CRM System Needs A Data Strategy
A customer relationship management (CRM) system is inherently valuable for supporting customer acquisition and retention by gathering data from each contact with customers and prospects. Collecting data, however, cannot be isolated from a strategy for actually using that data. Here is an overview of how to evolve the focus of a data strategy to specifically suit both the acquisition and retention phases.

APA ITU DATA FOKUS:
7/3/2003


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