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Continuous Data Quality Management: The Cornerstone of Zero-Latency Business Analytics
No matter how well an enterprise implements a CRM, ERP, SCM, Business Intelligence, or Data Warehouse project, poor data quality can destroy its utility and

data level zero  if-then logic of certain data objects. For instance, if an employee is of Level 2, then he or she should receive a raise of 5%, unless the employee's total pay is greater than $55,000/year. Functional Dependencies . These rules measure the invariants across columns of data. For example, for every customer number the data should always contain the same customer name. Structural Integrity Structural integrity checks examine whether the data is complete on a macro level. These checks ensure that when you Read More

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Documents related to » data level zero


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

data level zero  sizes help to facilitate data compression as Oracle does its compression at the database block level by eliminating duplicate values within a block. db_file_multiblock_read_count SQL parallel execution is generally used for queries that will access a lot of data, for example when doing a full table scan. Since parallel execution will by-pass the buffer cache and access data directly from disk you want each I/O to be as efficient as possible, and using large I/Os is a way to reduce latency. Set Read More
Continuous Data Quality Management: The Cornerstone of Zero-Latency Business Analytics Part 2: One Solution
Most enterprises fight fires with axes, fire hoses, trucks, and hordes of firemen, but the CDQM approach is a smoke detector. It’s far less expensive to put a

data level zero  Data Quality Management: The Cornerstone of Zero-Latency Business Analytics Part 2: One Solution Implementing A CDQM Application It is impossible to improve that which cannot be measured. A CDQM (Continuous Data Quality Management) tool provides a real-time, up-to-date scorecard to measure data quality within the enterprise. By checking data quality in real-time, data fires can be detected when they are just starting, before any real damage has occurred. Most enterprises fight fires with axes, Read More
Guidelines for Specification of Data Center Power Density
Conventional methods for specifying data center density don’t provide the guidance to assure predictable power and cooling performance for the latest IT

data level zero  required Table 2 ' Data required at the row level Data Units Description Primary use # rack locations # The number of rack locations in a row. Includes all locations, some of which may ultimately be consumed by power or cooling equipment, depending on architecture To determine the total power and cooling requirement for the row Rack average in row kW / rack The average per-rack power density exhibited by IT racks in a specified row. Must be specified for each row in the room To determine the bulk power Read More
Business Basics: Unscrubbed Data Is Poisonous Data
Most business software system changes falter--if not fail--because of only a few root causes. Data quality is one of these root causes. The cost of high data

data level zero  Unscrubbed Data Is Poisonous Data Introduction A manufacturing company experienced continually increasing overhead costs in its purchasing and materials departments. It also began to see increased inventory levels and production line delays. A consulting firm was brought in to analyze the company's materials management processes and the level of competence of its materials and procurement staff. After mapping the entire process, from the creation of a bill of materials through post-sales service and Read More
MSI Data


data level zero  Data Read More
Protecting Critical Data
The first step in developing a tiered data storage strategy is to examine the types of information you store and the time required to restore the different data

data level zero  Critical Data The first step in developing a tiered data storage strategy is to examine the types of information you store and the time required to restore the different data classes to full operation in the event of a disaster. Learn how in this white paper from Stonefly. 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

data level zero  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

data level zero  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
Considerations for Owning versus Outsourcing Data Center Physical Infrastructure
When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both

data level zero  for Owning versus Outsourcing Data Center Physical Infrastructure When faced with the decision of upgrading an existing data center, building new, or leasing space in a retail colocation data center, there are both quantitative and qualitative differences to consider. The 10-year TCO may favor upgrading or building over outsourcing; however, this paper demonstrates that the economics may be overwhelmed by a business’ sensitivity to cash flow, cash crossover point, deployment timeframe, data center life Read More
Data Center Automation
With the increasing complexity of the data center and its dependent systems, data center automation (DCA) is becoming a necessity. To replace the costly and

data level zero  increasing complexity of the data center and its dependent systems, data center automation (DCA) is becoming a necessity. To replace the costly and inefficient human aspect of managing the data center, IT departments must adopt DCA solutions. Combined with utility-based computing architectures, these solutions can provide greater dynamics in the environment and facilitate speed of response to market demands. Read More
Re-think Data Integration: Delivering Agile BI Systems with Data Virtualization
Today’s business intelligence (BI) systems have to change, because they’re confronted with new technological developments and new business requirements, such as

data level zero  think Data Integration: Delivering Agile BI Systems with Data Virtualization Today’s business intelligence (BI) systems have to change, because they’re confronted with new technological developments and new business requirements, such as productivity improvement and systems as well as data in the cloud. This white paper describes a lean form of on-demand data integration technology called data virtualization, and shows you how deploying data virtualization results in BI systems with simpler and more Read More
Customer Data Integration: A Primer
Customer data integration (CDI) involves consolidation of customer information for a centralized view of the customer experience. Implementing CDI within a

data level zero  Data Integration: A Primer Originally published - August 22, 2006 Introduction Implementing a customer data management system can be the difference between success and failure in terms of leveraging an organization's customer relationship management (CRM) system. Since customers drive profitability, organizations need a way to provide their employees with a single view of the customer and to provide that customer with above-average customer service. Unfortunately, this is not always the case. 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

data level zero  Necessity of Data Warehousing The Necessity of Data Warehousing M. Reed - August 2, 2000 Why the market is necessary Data warehousing is an integral part of the information age . Corporations have long known that some of the keys to their future success could be gleaned from their existing data, both current and historical. Until approximately 1990, many factors made it difficult, if not impossible, to extract this data and turn it into useful information. Some examples: Data storage peripherals such Read More
Massive Data Requires Massive Measures
One thing we learned in the data warehouse and data management world is that when it comes to the analysis of big data, there is also a lot of big money

data level zero  Data Requires Massive Measures   From Sun Tzu’s The Art of War : In the operations of war, where there are in the field a thousand swift chariots, as many heavy chariots, and a hundred thousand mail-clad soldiers, with provisions enough to carry them a thousand Li, the expenditure at home and at the front, including entertainment of guests, small items such as glue and paint, and sums spent on chariots and armor, will reach the total of a thousand ounces of silver per day. Such is the cost of Read More

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