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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...
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...
The Evolution of a Real-time Data Warehouse
Real-time data warehouses are common in some organizations. This article reviews the basic concepts of a real-time data warehouse and it will help you determine

etl data mining  data warehouse design. All ETL data warehouse processes were originally designed to be executed in batch mode, during previously scheduled downtimes. All operational data from distinct sources (e.g. ERP systems) was extracted, cleansed under a stage repository, and loaded into the data warehouse over long periods of time, mostly at night. These processes can take minutes or hours, depending on the volume of data being uploaded to the data warehouse. With the pressure to load more recent data into the 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...
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...
Data Quality Basics
Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at

etl data mining  Quality Basics Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue. Read More...
Data Management and Business Performance: Part 1-Data
Research for one of my projects led me to ask both software vendors and customers about the factors most important to software users in the selection of a

etl data mining  Management and Business Performance: Part 1-Data Research for one of my projects led me to ask both software vendors and customers about the factors most important to software users in the selection of a business intelligence (BI) solution. Two topics resounded: the use of BI tools to improve data management and business performance management. Consumers are continuously looking for innovative ways to move, store, and improve the quality of their data as well as to ways to capture the most valuable Read More...
The Path to Healthy Data Governance
Many companies are finally treating their data with all the necessary data quality processes, but they also need to align their data with a more complex

etl data mining  Path to Healthy Data Governance This article is based on the presentation, “From Data Quality to Data Governance,” by Jorge García, given at ComputerWorld Technology Insights in Toronto, Canada, on October 4, 2011. Modern organizations recognize that data volumes are increasing. More importantly, they have come to realize that the complexity of processing this data has also grown in exponential ways, and it’s still growing. Many companies are finally treating their data with all the necessary Read More...
Addressing the Complexities of Remote Data Protection
As companies expand operations into new markets, the percentage of total corporate data in remote offices is increasing. Remote offices have unique backup and

etl data mining  the Complexities of Remote Data Protection Because data protection is a concern for organizations of all sizes, IBM offers remote data protection for the enterprise, as well. This high performance and easy-to-use service delivers consistent and cost-effective data protection without increased network investment. Source: IBM Resources Related to Addressing the Complexities of Remote Data Protection : Continuous Data Protection (CDP) (Wikipedia) Addressing the Complexities of Remote Data 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...
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

etl data mining  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 Read More...
Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio
Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the

etl data mining  Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the traditional ways to store and analyze large amounts of data are being replaced with new technologies and methodologies to manage the new volume, complexity, and analysis requirements. These include new ways of developing data warehousing, the emergen Read More...
Flexible Customer Data Integration Solution Adapts to Your Business Needs
Siperian''s master data management and customer data integration (CDI) solutions allow organizations to consolidate, manage, and customize customer-related data.

etl data mining  Customer Data Integration Solution Adapts to Your Business Needs Customer data integration (CDI) has become one of the buzzwords within the master data management (MDM) industry. Although the concept of creating a single organizational view of the customer is noble and desirable, its value should also be justified by organizations. To implement a customer data hub that only creates a centralized view of an organization''s customer-related data does not affect a company''s bottom line, unless Read More...
Operationalizing the Buzz: Big Data 2013
The world of Big Data is maturing at a dramatic pace and supporting many of the project activities, information users and financial sponsors that were once the

etl data mining  the Buzz: Big Data 2013 The world of Big Data is maturing at a dramatic pace and supporting many of the project activities, information users and financial sponsors that were once the domain of traditional structured data management projects. Research conducted by Enterprise Management Associates (EMA) and 9sight Consulting makes a clear case for the maturation of Big Data as a critical approach for innovative companies. The survey went beyond simple questions of strategy, adoption, and Read More...

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