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 bi expensive data warehouse

Browse RFP templates

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Compare Software Solutions

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Business Intelligence (BI) and Data Management

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Documents related to » bi expensive data warehouse

About Big Data


There may not be a consensus with respect to just how big "big data" is, but not many people will disagree that managing these huge amounts of data represents a challenge. TEC research analyst Jorge Garcia discusses the key issues surrounding big data, the different ways to manage it, and the major vendors offering big data solutions.

bi expensive data warehouse  required by a traditional BI deployment, thus reducing data latency and speeding up the decision-making process. Some of the advantages of deploying a big data solution include: reducing the decision-making process by reading, analyzing, and giving results faster than traditional solutions collecting information, whether structured, semi-structured, or nonstructured, from disparate sources, and being able to manage it performing data discovery tasks, allowing you to build test scenarios, which is 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 involved in order to gain position. But is the analysis of extensive amounts of data really a key component for the corporate business world?

bi expensive data warehouse  help SAP complete the BI information cycle with competitive advantages. Mobility capabilities will be a very important part of the next generation of SAP’s analytical applications. IBM and Netezza On September 20 2010, IBM announced that it was about to acquire Netezza , a company based in Marlborough, Massachusetts (US). In a cash transaction worth about $1.7 billion (USD), IBM made an important move to establish itself as a big player in the big data field. Netezza holds a privileged position in the Read More

Access to Critical Business Intelligence: Challenging Data Warehouses?


There is a perception that if business users are given access to enterprise databases and raw query tools, they will create havoc in the system, which is a possibility—unless the business intelligence (BI) product developer understands the potential problem and addresses it as a business-critical factor.

bi expensive data warehouse  deep ingrained belief that BI cannot be conducted without a data warehouse (DW). Indeed, when companies are dealing with a deluge of data, it helps to have a DW, since it offers large corporations the ability to leverage information assets to support enterprise reporting and analysis. DWs also provide a technical solution to the problem of multiple systems, separate data stores, and rapidly expanding historical data, since information is extracted from various transaction-based systems, such as 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 technology selection and access to historical 'legacy' data are also discussed.

bi expensive data warehouse  and a number of BI tools for the individual users. A reasonable starting point for this set would be in the range of $100,000. Costs vary greatly based on vendor selection, number of seats purchased, software maintenance fees, and many other factors. In addition, consulting services drive the cost up immensely. A full blown, large-scale data warehouse in the terabyte range with hundreds of user licenses could cost tens of millions of dollars to implement and maintain. Conclusions Data Warehousing has Read More

Achieving Business Intelligence (BI) in Midsize Companies


Like many of today’s IT decision makers, you may be considering a business intelligence (BI) solution for your midsize company. But how do you go about adding BI without disrupting your company? Without breaking the bank? Without having to add staff members with specialties you’ve never even heard of before? This paper helps answer those questions, with practical advice for bringing BI into your midsize company.

bi expensive data warehouse  should implement a right-sized BI solution without needing expensive consulting services or specialized new hires. Here are some tips for doing so. Software Appliances Look for a BI solution that could accurately be described as a software appliance. I''ve used that term before, as a synonym for prepackaged; what exactly does it mean? Consider your corporate firewall. There are a couple of ways you can go when choosing a firewall—a dedicated firewall software package, which often runs on an OS such as Read More

Mobile BI: Features, Challenges, and Opportunities


What does your organization need to consider when adopting a mobile business intelligence (BI) or business analytics strategy? What are the enablers, challenges, and opportunities of a mobile BI strategy implementation? In this report, we explore considerations for deploying a mobile BI solution, how to leverage this type of platform to best advantage, and why a mobile BI/analytics solution can be a valuable asset for your company.

bi expensive data warehouse  opportunities of a mobile BI strategy implementation? In this report, we explore considerations for deploying a mobile BI solution, how to leverage this type of platform to best advantage, and why a mobile BI/analytics solution can be a valuable asset for your company. 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 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 Data Warehouse in detail, including functionality, distinguishing characteristics, and Teradata's role in the competitive data warehouse space.

bi expensive data warehouse  this report TEC Senior BI analyst Jorge Garcia looks at the Teradata Data Warehouse in detail, including functionality, distinguishing characteristics, and Teradata''s role in the competitive data warehouse space. Read More

Data Center Projects: Advantages of Using a Reference Design


It is no longer practical or cost-effective to completely engineer all aspects of a unique data center. Re-use of proven, documented subsystems or complete designs is a best practice for both new data centers and for upgrades to existing data centers. Adopting a well-conceived reference design can have a positive impact on both the project itself, as well as on the operation of the data center over its lifetime. Reference designs simplify and shorten the planning and implementation process and reduce downtime risks once up and running. In this paper reference designs are defined and their benefits are explained.

bi expensive data warehouse  DCIM, data center, data center infrastructure management, DCIM management, DCIM software, DCIM software tools, IT, IT infrastructure, APC by Schneider Electric, facility operations and maintenance, data center life cycle, data center facility operations, data center PUE, PUE, data center reference design Read More

Achieving a Successful Data Migration


The data migration phase can consume up to 40 percent of the budget for an application implementation or upgrade. Without separate metrics for migration, data migration problems can lead an organization to judge the entire project a failure, with the conclusion that the new package or upgrade is faulty--when in fact, the problem lies in the data migration process.

bi expensive data warehouse  data and the system''s ability to deliver the data on the business'' schedule. Data politics meaning, issues about who owns certain data may arise, causing unnecessary delays in obtaining the appropriate permission to access and cleanse certain data. And, of course, a good strategy should allow flexible interfaces to various data sources that can evolve over time. Move Data to a Continuously Changing Target Implementing an application is an iterative process, with business and IT staff making frequent Read More

Developing a Universal Approach to Cleansing Customer and Product Data


Data quality has always been an important issue for companies, and today it’s even more so. But are you up-to-date on current industry problems concerning data quality? Do you know how to address quality problems with customer, product, and other types of corporate data? Discover how data cleansing tools help improve data constancy and accuracy, and find out why you need an enterprise-wide approach to data management.

bi expensive data warehouse  Data Cleanse Summary About BI Research About Business Objects Data Quality: What’s The Problem? Data quality has always been an important issue for companies, and this is even more the case today. Business and legislative pressures coupled with the explosion in the amount of data created by organizations is leading to increased corporate attention on improving the quality and accuracy of business data. This paper reviews current industry problems concerning data quality, and takes a detailed look at 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 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.

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Data Mining with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses


Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has been slow due to their lack of business intelligence (BI) functionality, proactive information distribution, robust security, and other necessities. Now there’s an integrated enterprise BI system that can deliver data mining and predictive analysis. Learn more.

bi expensive data warehouse  with MicroStrategy: Using the BI Platform to Distribute Data Mining and Predictive Analytics to the Masses Data mining and predictive analysis applications can help you make knowledge-driven decisions and improve efficiency. But the user adoption of these tools has been slow due to their lack of business intelligence (BI) functionality, proactive information distribution, robust security, and other necessities. Now there’s an integrated enterprise BI system that can deliver data mining and predictive Read More

2012 Business Data Loss Survey results


This report on the Cibecs and IDG Connect 2012 business data loss survey uncovers the latest statistics and trends around enterprise data protection. Download the full results now.

bi expensive data warehouse  data protection,data backup,2012 data statistics,data loss,business data backup 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.

bi expensive data warehouse  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 Read More

Data Migration Management: A Methodology to Sustaining Data Integrity for Going Live and Beyond


For many new system deployments, data migration is one of the last priorities. Data migration is often viewed as simply transferring data between systems, yet the business impact can be significant and detrimental to business continuity when proper data management is not applied. By embracing the five phases of a data migration management methodology outlined in this paper, you can deliver a new system with quality data.

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