Home
 > search for

Featured Documents related to »  data cube

Halo Source: High performance data warehouse, ETL and OLAP Cube


data cube  Source: High performance data warehouse, ETL and OLAP Cube Read More...
Outsourcing, IT Infrastructure
The IT Infrastructure Outsourcing knowledge base focuses on the selection of companies who provide outsource services in the areas of information technology (IT) infrastructure. The typical type...
Start evaluating software now
Country:

 
   

 Security code
Already have a TEC account? Sign in here.
 
Don't have a TEC account? Register here.

Documents related to » data cube


Avoiding the Four Data Display Pitfalls in Dashboard Design
Presenting data and results is one of the fundamental stages of every business intelligence (BI) or business performance (BPM) deployment. Data is also

data cube  the Four Data Display Pitfalls in Dashboard Design Presenting data and results is one of the fundamental stages of every business intelligence (BI) or business performance (BPM) deployment. Data is also important when adopting a new solution, and for the overall success of a BI project—even when the project stage does not represent any major technical challenge. A dashboard is the main screen by which end users, executives, managers, and information workers can see data generated by the BI Read More...
Ask the Experts: Approaches to Data Mining ERP
From one of our readers comes this question:I am a student of IT Management; I have an ERP course and I am supposed to write an article to review new

data cube  essentially a report. The data can be saved in a cube so that one does not have to rerun the report. Now, one would like to go further and examine the data associatively, or analytically. As examples, for anyone who bought Product X, what else did they buy? And (clustering) what else did similar shoppers buy? Call that the basket. Then the business can make recommendations (as does Amazon to its customers) based on the fact that other customers also bought products Y and Z. ALEXANDER HANKEWICZ In a more 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

data cube  held as low-level transactional data in standard relational structures, but to the application layer it appears as a “virtual cube.” By virtualizing the cube structures, organizations can still get a holistic view of their data but with zero latency given that large virtual cubes can be created within just seconds. Moreover, users can benefit from one single version of the truth; instead of contending with multiple physical cubes that represent data snapshots that age as soon as they are created, 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

data cube  Margin over Continously Cleansed Data Optimizing Gross Margin over Continously Cleansed Data If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. Advanced functionality to manage costs, sell prices, promotions, discounts, chargebacks, and other key attributes while optimizing gross profits. Source : epaCUBE Resources Related to Optimizing Gross Margin over Continously Cleansed Data : Data cleansing (Wikipedia) Gross margin (Wikipedia) 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 cube  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...
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

data cube  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...
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

data cube  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...
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

data cube  Success Factors to Cleansing Data Four Critical Success Factors to Cleansing Data If you receive errors when attempting to view this white paper, please install the latest version of Adobe Reader. Data Cleansing and Synchronization Services The pace with which companies are forced to operate and to compete globally has taxed exisitng systems and increased their inefficiencies. Source : PM ATLAS Business Group, LLC Resources Related to Critical Success Factors to Cleansing Data : Data Cleansing Read More...
The Teradata Database and the Intelligent Expansion of the Data Warehouse
In 2002 Teradata launched the Teradata Active Enterprise Data Warehouse, becoming a key player in the data warehouse and business intelligence scene, a role

data cube  Intelligent Expansion of the Data Warehouse In 2002 Teradata launched the Teradata Active Enterprise Data Warehouse, becoming a key player in the data warehouse and business intelligence scene, a role that Teradata has maintained until now. Teradata mixes rigorous business and technical discipline with well-thought-out innovation in order to enable organizations to expand their analytical platforms and evolve their data initiatives. In this report TEC Senior BI analyst Jorge Garcia looks at the Teradata Read More...
Meet PCI DSS Compliance Requirements for Test Data with Data Masking
Whether you’re working toward your first or your next payment card industry (PCI) data security standard (DSS) audit, you know compliance is measured on a

data cube  Compliance Requirements for Test Data with Data Masking Whether you’re working toward your first or your next payment card industry (PCI) data security standard (DSS) audit, you know compliance is measured on a sliding scale. But full compliance can’t be achieved with just one policy or technology. Using data masking, a technology that alters sensitive information while preserving realism, production data can be eliminated from testing and development environments. Learn more. 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 cube  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...
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 cube  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...
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

data cube  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...
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

data cube  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...

Recent Searches
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Others