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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 analyses  access the same underlying data volumes. However, most importantly, analysts can get on with their analyses instead of getting bogged down with overhead of cube creation—saving the company time and money. 6.    What is your take on the evolution of the data warehouse? What are the main challenges companies still have to face in doing big data management and analysis? Hadoop seems to becoming the de facto technology for “Big Data” processing. While the advantages may be clear (no license fees, Read More
Discrete Manufacturing (ERP)
The simplified definition of enterprise resource planning (ERP) software is a set of applications that automate finance and human resources departments and help manufacturers handle jobs such as or...
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Documents related to » data analyses


Asset Data for Accurate Lifecycle Management
Among the areas where modern enterprise asset management (EAM) systems provide substantial benefits is the driving out of inefficiencies in business processes

data analyses  Along with the responsible data capture forced by these policy options, configuring and managing the EAM in line with RCM thinking will also allow visibility of exceptional failures. Due to the way that RCM is (by necessity) carried out, there is the possibility that some failures may be missed. Modern methods of execution have expanded the original default method of team-based analyses to include expert analysis sources outside the team, but there always remains the possibility that the analysis will Read More
Data Quality: Cost or Profit?
Data quality has direct consequences on a company's bottom-line and its customer relationship management (CRM) strategy. Looking beyond general approaches and

data analyses  profile. As a result, data becomes corrupted and is then misinterpreted. Having a customer in a database two or more times may create the impression that they are different customers. Businesses may lose money if, for example, they do a mailing campaign based on this customer base. Multiple letters sent to the same customer will double the cost of mailing and fulfillment and reduce company credibility in the eyes of customers. The use of poor quality databases can also lead to the misinterpretation of a Read More
Data Quality Basics
Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at

data analyses  Quality Basics Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue. 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 analyses  data from epaCUBE’s Product Data Management suite to ensure information is accurate and in sync with your operational pricing system. Margin Manager is designed to handle advertising performance and off-invoice allowances as well as 2-way and 3-way contracts. This powerful tool also enforces user-defined business rules to convert allowances from the “buy” to the “sell” side. In addition, epaCUBE’s Margin Manager provides visibility and control of manufacturer rebates and accurately tracks Read More
The Fast Path to Big Data
Today, most people acknowledge that big data is more than a fad and is a proven model for leveraging existing information sources to make smarter, more

data analyses  Fast Path to Big Data Today, most people acknowledge that big data is more than a fad and is a proven model for leveraging existing information sources to make smarter, more immediate decisions that result in better business outcomes. Big data has already been put in use by companies across vertical market segments to improve top- and bottom-line performance. As unstructured data becomes a pervasive source of business intelligence, big data will continue to play a more strategic role in enterprise Read More
Next-generation Data Auditing for Data Breach Protection and Risk Mitigation
Data breaches and leaks are on the rise—and the consequences, from theft of identity or intellectual property, can seriously compromise a company’s reputation

data analyses  generation Data Auditing for Data Breach Protection and Risk Mitigation Data breaches and leaks are on the rise—and the consequences, from theft of identity or intellectual property, can seriously compromise a company’s reputation. Stolen laptops, hacking, exposed e-mail, insider theft, and other causes of data loss can plague your company. How can you detect (and respond!) to breaches and protect your data center? Learn about the functions and benefits of an automated data auditing system. Read More
Data Quality Strategy: A Step-by-Step Approach
To realize the benefits of their investments in enterprise computing systems, organizations must have a detailed understanding of the quality of their data—how

data analyses  what goes into a data quality strategy? This paper from Business Objects, an SAP company, explores the strategy in the context of data quality. 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 analyses  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
Enterprise Data Management: Migration without Migraines
Moving an organization’s critical data from a legacy system promises numerous benefits, but only if the migration is handled correctly. In practice, it takes an

data analyses  Data Management: Migration without Migraines Moving an organization’s critical data from a legacy system promises numerous benefits, but only if the migration is handled correctly. In practice, it takes an understanding of the entire ERP data lifecycle combined with industry-specific experience, knowledge, and skills to drive the process through the required steps accurately, efficiently, and in the right order. Read this white paper to learn more. Read More
Metagenix Reverse Engineers Data Into Information
Metagenix’ MetaRecon reverse engineers metadata information by examining the raw data contained in the source(s) rather than depending on the data dictionaries

data analyses  Reverse Engineers Data Into Information Metagenix Reverse Engineers Data Into Information M. Reed - February 15, 2001 Event Summary Metagenix, Inc. has designed its flagship product, MetaRecon to, as they put it, Decipher Your Data Genome . The product reverse engineers all of the metadata ( data about data ) from data sources and generates information that is very helpful to developers in designing specifications for a new data store, and assists greatly in preparing for cleansing and 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 analyses  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
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 analyses  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

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