Advanced ETL with Pentaho Data Integration

When evaluating a data integration tool, you need to ask whether it handles dimension and fact transformations in a robust manner. You also need to determine how it handles error processing, and captures changes to dimensional data across time. Does it include reusable objects? What late-arriving fact functionality does it provide? And which data stores does it actually connect to?

Featured Software Research:

How Analytics Bring Organizations Closer to Their Customers

Social media is providing organizations with a plethora of data about their customers. This paper explains how to leverage business intelligence software and advanced analytics to change the way business is conducted. The end goal should be to personalize marketing messages in a way that allows organizations to narrowly target specific customers based on the knowledge of the customer they already have. Read More

Analytics: A Blueprint for Value in Mid-Market Organizations

  • Source: IBM
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While most mid-market companies have an analytics foundation designed to handle structured data, few have evolved to more dynamic environments required for big data and a pervasive and prescriptive use of advanced analytics that are essential to survive in a digital world. So, what should mid-market companies do now to improve their capabilities to convert data-driven insights into meaningful results? In this report, we explore how they can tap into their strengths, shore up their weaknesses, and learn from analytics leaders. Read More

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A Road Map to Data Migration Success

  • Source: SAP
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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. 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. 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 are automated data cleansing, systematic management of data processes, and margin optimization. Real dollars can be reclaimed in the supply chain by making certain that every byte of product information is accurate and synchronized, internally and externally. Read More
 
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