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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

inaccurate data  Beginning The problem of inaccurate data often begins in application systems (the sources of data). There are simple best practices that can help limit the extent of inaccurate data. Tying data types to business entities . Data types in source databases must closely describe the business entities they represent. For instance, numeric entities should not be stored as columns with string data types. When non-numeric data is accidentally stored in such columns, integrity problems are bound to occur Read More...
Merchandising Systems
Merchandising systems are the enterprise back and front-office software solutions upon which the majority of retailers rely to manage and support their daily tasks. These systems typically record p...
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Documents related to » inaccurate data


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

inaccurate data  issues like duplication or inaccurate data; for example you may employ a lookup table to cross reference purchase orders with stock keeping units (SKUs) that your company uses. Duplicate SKU numbers with different descriptions may not be a problem for a data entry clerk who may intuitively know the difference, but they could create significant problems for your data integration project. This is just one example of how dirty data could impact your project. As you prepare for your EDI integration project, Read More...
Achieving Efficiency and Effectiveness with Your Master Data
Master data is instrumental in determining how an organization produces, buys, and sells its goods and services. Inaccurate master data can lead to improper

inaccurate data  its goods and services. Inaccurate master data can lead to improper business decisions, loss of revenue, and noncompliance with regulatory and quality mandates. Find out how an Enterprise data management (EDM) strategy can help your company avoid these pitfalls by achieving clean, consolidated, and consistent master data. Read More...
Why Systems Fail - The Dead-end of Dirty Data
If your data does not reflect reality, the system can never be effective. In today’s world of collaboration, showing a trading partner dirty data is giving them

inaccurate data  data may have been inaccurately recorded. In the case of our inventory example, one of the potentially many transactions that change the on-hand data could have been inaccurate. For example, a receipt may have had an incorrect quantity. Alternatively, the physical act may have been flawed, for example, an order called for 100 units to be picked, the transaction may have been recorded as 100 but the physical picking resulted in more or less than 100 being picked. Perhaps worse, when the material was 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

inaccurate data  fully populated, or has inaccurate data (230 years old), the team can reference the known uses of that data to determine the extent of data cleansing needed. In another example, the medical history for an automated patient chart must be of the highest quality when migrated. The business need, which is driven by legal requirements, must be clearly understood by the entire migration team. Ideally, the project team will include data experts and business users as part of a data governance strategy to ensure Read More...
Optimizing Gross Margin over Continously Cleansed Data
Imperfect product data can erode your gross margin, frustrate both your customers and your employees, and slow new sales opportunities. The proven safeguards

inaccurate data  with product data. While inaccurate data can adversely affect many areas, its consequences can be particularly detrimental when distributors analyze cost changes and their affect on complex customer price matrices and gross margin. Without clean, accurate data, other operational systems can fail and lead to costly and timeconsuming problems. Examples include: Your customer can’t find a particular product. Often your customers or your own sales or service representatives can’t find products in the Read More...
Six Steps to Manage Data Quality with SQL Server Integration Services
Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business

inaccurate data  Steps to Manage Data Quality with SQL Server Integration Services 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 Six Steps to Manage Data Quality with SQL Server Integration Services : Data quality (Wikipedia) Six Steps to Manage Data Quality with SQL Server Integration Services Data Quality is also known as : 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

inaccurate data  Evolution of a Real-time Data Warehouse The Evolution of a Real-time Data Warehouse Jorge Garcia - December 23, 2009 Understanding Real-time Systems Today, real time computing is everywhere, from customer information control systems (CICSs) to real-time data warehouse systems. Real-time systems have the ability to respond to user actions in a very short period of time. This computing behavior gives real-time systems special features such as instant interaction: users request information from the system 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

inaccurate data  for Specification of Data Center Power Density Guidelines for Specification of Data Center Power Density 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. 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

inaccurate data  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...
2013 Big Data Opportunities Survey
While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry

inaccurate data  Big Data Opportunities Survey While big companies such as Google, Facebook, eBay, and Yahoo! were the first to harness the analytic power of big data, organizations of all sizes and industry groups are now leveraging big data. A survey of 304 data managers and professionals was conducted by Unisphere Research in April 2013 to assess the enterprise big data landscape, the types of big data initiatives being invested in by companies today, and big data challenges. Read this report for survey responses Read More...
Tailoring SAP Data Management for Companies of All Sizes
The need for accurate data management such as upload or download of data between a company’s data sources and SAP systems is more critical than ever. Users are

inaccurate data  SAP Data Management for Companies of All Sizes The need for accurate data management such as upload or download of data between a company’s data sources and SAP systems is more critical than ever. Users are relying on manual operations, which are inherently error-prone, and time- and resource-intensive. Today's environment requires enterprise-class automation to overcome these challenges of data management. Learn about one solution that can help improve SAP data management. Read More...
Implementing Energy-Efficient Data Centers
But in the white paper implementing energy-efficient data centers, you'll learn how to save money by using less electricitywhether your data cente...

inaccurate data  Energy-Efficient Data Centers Did you realize that your data center(s) may be costing you money by wasting electricity ? Or that there are at least 10 different strategies you can employ to dramatically cut data center energy consumption ? The fact is, most data centers are not designed with energy efficiency in mind. But in the white paper Implementing Energy-efficient Data Centers , you'll learn how to save money by using less electricity—whether your data centers are still in the design Read More...
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

inaccurate data  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. Read More...

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