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Distilling Data: The Importance of Data Quality in Business Intelligence
As an enterprise’s data grows in volume and complexity, a comprehensive data quality strategy is imperative to providing a reliable business intelligence

etl data mining  extract, transform, and load (ETL) process in a data warehousing system extracts records from data source(s), transforms them using rules to convert data into a form that is suitable for reporting and analysis, and finally loads the transformed records into the destination (typically a data warehouse or data mart). Data cleansing is an integral part of the transformation process and enforces business and schema rules on each record and field. Data cleansing involves the application of quality screens Read More...
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

etl data mining  are currently over 50 ETL tools on the market. The data acquisition phase can cost millions of dollars and take months or even years to complete. Data acquisition is then an ongoing, scheduled process, which is executed to keep the warehouse current to a pre-determined period in time, (i.e. the warehouse is refreshed monthly). Changed Data Capture: The periodic update of the warehouse from the transactional system(s) is complicated by the difficulty of identifying which records in the source have changed Read More...
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

etl data mining  extract, transform, and load (ETL) development. It is important not to underestimate the importance of data integration, as the way data is integrated into a data warehouse or BI solution is the essence of that system. If a scorecard is developed to measure an organization''s sales metrics and the source data is not accurate, the key performance indicators (KPIs) set and reported on will be meaningless. Administration and Technology The administration and technology track identified and covered topics 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

etl data mining  via another set of ETL processes. It is in this layer data begins to take shape and it is not uncommon to have some end-user application access data from this layer especially if they are time sensitive, as data will become available here before it is transformed into the dimension / performance layer. Traditionally this layer is implemented in the Third Normal Form (3NF). Optimizing 3NF Optimizing a 3NF schema in Oracle requires the three Ps – Power, Partitioning and Parallel Execution. Power means Read More...
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

etl data mining  issue is whether the ETL tool moves all the data through its own engine on the way to the target, or can be a proxy and move the data directly from the source to the target. Selection of the business intelligence tool(s) requires decisions such as: Will multi-dimensional analysis be necessary, or does the organization need only generalized queries? Not all warehouse implementations require sophisticated analysis techniques such as data mining (statistical analysis to discover trends in the data), data Read More...
Data Management Wish List: IT Is Open to Big Changes
Data management, access, and analysis haven’t always received the same amount of consideration, or respect, as the corporate applications that generate and

etl data mining  Management Wish List: IT Is Open to Big Changes Data management, access, and analysis haven’t always received the same amount of consideration, or respect, as the corporate applications that generate and consume the data. In recent years, however, information technology (IT) leaders have focused much more attention on their organizations’ data management platforms and capabilities. Download this white paper to get a better picture of the current data management landscape. Read More...
Data Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics
Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident

etl data mining  Enrichment and Classification to Drive Data Reliability in Strategic Sourcing and Spend Analytics Spend analytics and performance management software can deliver valuable insights into savings opportunities and risk management. But to make confident decisions and take the right actions, you need more than just software—you also need the right data. This white paper discusses a solution to help ensure that your applications process reliable data so that your spend analyses and supplier risk 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

etl data mining  Data Quality: A Seven-step Plan for Any Size Organization Melissa Data’s Data Quality Suite operates like a data quality firewall – instantly verifying, cleaning, and standardizing your contact data at point of entry, before it enters your database. Source : Melissa Data Resources Related to Scalable Data Quality: A Seven-step Plan for Any Size Organization : Data quality (Wikipedia) Scalable Data Quality: A Seven-step Plan for Any Size Organization Data Quality is also known as : Customer Read More...
Data Center Projects: Project Management
In data center design projects, flawed management frequently leads to delays, expense, and frustration. Effective project management requires well-defined

etl data mining  Center Projects: Project Management Data Center Projects: Project Management If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. In today''s always on, always available world where businesses can''t stop and downtime is measured in dollars, American Power Conversion (APC) provides protection against some of the leading causes of downtime, data loss and hardware damage: power problems and temperature. As a global leader in Read More...
Data Masking: Strengthening Data Privacy and Security
Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating

etl data mining  Masking: Strengthening Data Privacy and Security Many business activities require access to real production data, but there are just as many that don’t. Data masking secures enterprise data by eliminating sensitive information, while maintaining data realism and integrity. Many Fortune 500 companies have already integrated data masking technology into their payment card industry (PCI) data security standard (DSS) and other compliance programs—and so can you. Read More...
Data Quality Trends and Adoption
While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ

etl data mining  Quality Trends and Adoption While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers. 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

etl data mining  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. Read More...
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

etl data mining  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 Read More...

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