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 implementing data warehouse bi

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Documents related to » implementing data warehouse bi

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 if your organization needs this type of IT solution.

implementing data warehouse bi  the technical difficulties of implementing a true real-time data warehouse, there are some advantages. It shortens information delivery times. It improves integration throughout the organization. It eases the analysis of future trends. Basic Principles to Consider With the growing popularity and increasing implementation of real-time data warehouses, it is important to consider some basic principles when considering a real-time data warehouse implementation. Data on Time, at the Right Time . The data Read More

More Data is Going to the Cleaners


WESTBORO, Mass., November 29, 1999 - Ardent Software, Inc. (Nasdaq: ARDT) today announced a strategic partnership with Firstlogic, Inc., the developer of i.d.Centric data quality software that helps companies cleanse and consolidate data in database marketing, data warehousing, and e-business applications. Under the partnership agreement Firstlogic will develop and support a link between its customer data quality tools and Ardent's DataStage Suite.

implementing data warehouse bi  Ardent''s DataStage Suite. Organizations implementing complex database marketing programs and e-business strategies depend on extensive data quality assurance. Together Ardent and Firstlogic are meeting this market need, said Mikael Wipperfeld, vice president of data warehouse marketing at Ardent Software. This partnership allows our joint customers to take advantage of Firstogic''s address verification, name parsing and extensive matching and consolidation capabilities inside the DataStage suite, the 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 to businesses. In this article, learn about the four critical success factors to clean data: 1- scope, 2- team, 3- process, and 4- technology.

implementing data warehouse bi  records. Unless you are implementing an ERP or CRM system, initial data cleansing efforts on transactional or historical data would be discouraged. How many integrations do you have that feed your master data to other systems (internal or external) Create a master list by system and data field name. Do you have a data warehouse, one central location for master data? Is now the time to make that move? Now identify the data owner and the business owners for each field. A data owner is the person that is 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 teams encounter some very specific management and technical challenges. Minimizing the risk of these tricky migrations requires effective planning and scoping. Read up on the issues unique to data migration projects, and find out how to best approach them.

implementing data warehouse bi  processes that they are implementing. Allow time in your planning for changes to the mapping, business rules, and re-loads. It is not uncommon for there to be confusion during beta testing and implementation as to where errors originate. It''s all too easy for bad data to be blamed for every test error. What can you do as part of your planning to avoid this? Implement the migration with as much metadata and reconciliation as possible. Not only are you ensuring the integrity of your data, you are 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 customer relationship management initiative can help provide organizations with a successful framework to manage data on a continuous basis.

implementing data warehouse bi  should be considered when implementing master data management (MDM) for CRM within the organization. Finally, key vendors in the industry and their key product features will be identified. Defining Customer Data Integration Within CRM, CDI is the management and consolidation of customer information from across the organization. This includes, but is not limited to, information stored in call centers, sales and marketing departments, and accounts receivables and payables. CDI ensures that each department 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 it is crucial to getting maximum value out of your data. In small companies, for which every sales lead, order, or potential customer is valuable, poor data quality isn’t an option—implementing a thorough data quality solution is key to your success. Find out how.

implementing data warehouse bi  publicity department. Prior to implementing a data quality solution to clean, verify and standardize addresses, Simon & Schuster was relying on UPS to make the correction. ZIP Code errors were the most common address problem. But UPS charged $5 per package to correct in the field, plus returns as undeliverable also cost $5, not to mention labor time at S&S to process the return. All in all, bad data was costing S&S about $250,000 per year. Now Simon & Schuster corrects addresses before the packages are Read More

Don't Be Overwhelmed by Big Data


Big Data. The consumer packaged goods (CPG) industry is abuzz with those two words. And while it’s understandable that the CPG world is excited by the prospect of more data that can be used to better understand the who, what, why, and when of consumer purchasing behavior, it’s critical CPG organizations pause and ask themselves, “Are we providing retail and executive team members with “quality” data, and is the data getting to the right people at the right time? Big Data can be a Big Deal - read this white paper for some useful tips on ensuring secure, quality data acquisition and management.

implementing data warehouse bi  t Be Overwhelmed by Big Data Big Data. The consumer packaged goods (CPG) industry is abuzz with those two words. And while it’s understandable that the CPG world is excited by the prospect of more data that can be used to better understand the who, what, why, and when of consumer purchasing behavior, it’s critical CPG organizations pause and ask themselves, “Are we providing retail and executive team members with “quality” data, and is the data getting to the right people at the right time? 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 performs and scales well, you need to get three things right: the hardware configuration, the data model, and the data loading process. Learn how designing these three things correctly can help you scale your EDW without constantly tuning or tweaking the system.

implementing data warehouse bi  Data Warehouse Info | Implementing Data Warehouse | Data Warehouse Process | Implementing Real Time Data Warehousing | Data Warehouse System Complete | Data Warehouse EDW | Data Warehouse Architecture EDW | Data Warehouse Concepts EDW | Data Warehousing Information Center EDW | Data Integration Paper EDW | Data Warehouse Software EDW | Data Warehousing Analysis EDW | Data Warehouse Community EDW | Data Warehouse Automation EDW | Perspectives on Data Warehousing EDW | Data Warehousing OLAP EDW | Resource 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 equipment. Discover an improved method that can help assure compatibility with anticipated high-density loads, provide unambiguous instruction for design and installation of power and cooling equipment, prevent oversizing, and maximize electrical efficiency.

implementing data warehouse bi  any function related to implementing high density power or cooling. Ability to spread loads: The ability to spread IT equipment physically within a data center is a practical option for most IT equipment today, due to the wide use of fiber-optic cabling. It is not necessary or desirable in many cases to deploy equipment at the full density it is capable of. Blade servers and 1U servers are examples of high density IT equipment that can readily be spread out between racks in order to decrease density. 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 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.

implementing data warehouse bi  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

Reinventing Data Masking: Secure Data Across Application Landscapes: On Premise, Offsite and in the Cloud


Be it personal customer details or confidential internal analytic information, ensuring the protection of your organization’s sensitive data inside and outside of production environments is crucial. Multiple copies of data and constant transmission of sensitive information stream back and forth across your organization. As information shifts between software development, testing, analysis, and reporting departments, a large "surface area of risk" is created. This area of risk increases even more when sensitive information is sent into public or hybrid clouds. Traditional data masking methods protect information, but don’t have the capability to respond to different application updates. Traditional masking also affects analysis as sensitive data isn’t usually used in these processes. This means that analytics are often performed with artificially generated data, which can yield inaccurate results.

In this white paper, read a comprehensive overview of Delphix Agile Masking, a new security solution that goes far beyond the limitations of traditional masking solutions. Learn how Delphix Agile Masking can reduce your organization’s surface area risk by 90%. By using patented data masking methods, Delphix Agile Masking secures data across all application lifecycle environments, providing a dynamic masking solution for production systems and persistent masking in non-production environments. Delphix’s Virtual Data Platform eliminates distribution challenges through their virtual data delivery system, meaning your data can be remotely synchronized, consolidated, and takes up less space overall. Read detailed scenarios on how Delphix Agile Data Masking can benefit your data security with end-to-end masking, selective masking, and dynamic masking.

implementing data warehouse bi  Data Masking: Secure Data Across Application Landscapes: On Premise, Offsite and in the Cloud Be it personal customer details or confidential internal analytic information, ensuring the protection of your organization’s sensitive data inside and outside of production environments is crucial. Multiple copies of data and constant transmission of sensitive information stream back and forth across your organization. As information shifts between software development, testing, analysis, and Read More

Big Data Comes of Age: Shifting to a Real-time Data Platform


New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose-built platforms more capable of meeting the real-time needs of more demanding end users and the opportunities presented by big data. Read this white paper to learn more about the significant strategy shifts underway to transform traditional data ecosystems by creating a unified view of the data terrain necessary to support big data and the real-time needs of innovative companies.

implementing data warehouse bi  Data Comes of Age: Shifting to a Real-time Data Platform New data sources are fueling innovation while stretching the limitations of traditional data management strategies and structures. Data warehouses are giving way to purpose-built platforms more capable of meeting the real-time needs of more demanding end users and the opportunities presented by big data. Read this white paper to learn more about the significant strategy shifts underway to transform traditional data ecosystems by creating a Read More

Data Management and Analysis


From a business perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it is also a resource for acquiring knowledge and supporting the decisions companies need to make in all aspects of economic ventures, including mergers and acquisitions (M&As).

For organizational growth, all requirements and opportunities must be accurately communicated throughout the value chain. All users—from end users to data professionals—must have the most accurate data tools and systems in place to efficiently carry out their daily tasks. Data generation development, data quality, document and content management, and data security management are all examples of data-related functions that provide information in a logical and precise manner.

implementing data warehouse bi  Management and Analysis From a business perspective, the role of data management and analysis is crucial. It is not only a resource for gathering new stores of static information; it is also a resource for acquiring knowledge and supporting the decisions companies need to make in all aspects of economic ventures, including mergers and acquisitions (M&As). For organizational growth, all requirements and opportunities must be accurately communicated throughout the value chain. All users—from end users Read More

BI on the Go: About Functionality and Level of Satisfaction


We thought it would be useful to take another look at what was revealed in our recent survey on mobile BI regarding what’s important for mobile BI users, and of course, how satisfied they are with the mobile BI solutions they work with. Here we discuss functionality and level of satisfaction, and how they affect mobile BI practices and decision making.

implementing data warehouse bi  on the Go: About Functionality and Level of Satisfaction TEC recently published its 2014 Mobile BI Buyers Guide and a related blog post in which some results from a survey on mobile business intelligence (BI) usage, needs, and trends were discussed. We thought it would be useful to take another look at what was revealed from the survey regarding what’s important for mobile BI users, and of course, how satisfied they are with the mobile BI solutions they work with. Let’s take a look at some of our 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 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 expectancy, regulatory requirements, and other strategic factors. This paper discusses how to assess these key factors to help make a sound decision.

implementing data warehouse bi  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, Read More