Garbage In, Garbage Out: Getting Good Data Out of Your BI Systems

A big topic in business intelligence (BI) is the importance of “data quality”–cleaning data so that errors and inconsistencies are eliminated as much as possible. But what many users don’t realize is that this is just a part of a larger problem: How do you get the most out of business-relevant data floating around, inside and outside the organization? Discover the importance of clean data, and learn how to achieve it.

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11 Criteria for Selecting the Best ERP System Replacement

An enterprise resource planning (ERP) system is your information backbone, reaching into all areas of your business and value chain. That’s why replacing it can open unlimited business opportunities. The cornerstone of this effort is finding the right partner. And since your long-term business strategy will shape your selection, it’s critical that your ERP provider be part of your vision. Read More

Linked Enterprise Data: Data at the heart of the company

The data silos of today's business information systems (IS) applications, and the pressure from the current economic climate, globalization, and the Internet make it critical for companies to learn how to manage and extract value from their data. Linked enterprise data (LED) combines the benefits of business intelligence (BI), master data management (MDM), service-oriented architecture (SOA), and search engines to create links among existing data, break down data walls, provide an open, secure, and long-term technological environment, and reduce complexity—read this white paper to find out how. Read More

Credit Risk Management: Collateral, Covenants and Risk Review

  • Source: IBM
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If your organization is still managing credit risk manually, you could be leaving your company open to experience significant losses and complications that can harm its financial well-being. Multiple systems, piles of paper, inconsistent or out of date information- all could end up costing your company dearly. Today, the best way to ensure that credit risk is being appropriately monitored and managed is with the adoption of an automated system. With the implementation of an automated system, accuracy of data and efficiency of execution are significantly improved, and risk is monitored in a superior and more effective manner.

In this white paper, IBM highlights the benefits that an automated system for credit risk management can bring to your organization, including a reduction of human error on multiple levels, a marked increase in compliance, the capability to quickly track a data trail, and the capacity to effortlessly update and upgrade across multiple accounts and systems. Automation means valuations are up to the minute, and that transparency is increased. Document imaging, data entry, and automated workflow can resolve many problems previously encountered with manual procedures.

IBM’s Business Analytics offers a comprehensive 5-point action plan involving the importance of automating collateral processes, how to consolidate diverse systems and data, the benefits of automating collateral and covenant monitoring to reduce risk with the latest available information, and how automation supports release processes for minimized risk. Credit risk management can be modernized and refined as a result of changing towards an automated system. Read More

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Business Intelligence: A Guide for Midsize Companies

  • Source: SAP
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Business intelligence (BI) is not a new concept. What’s new is that BI tools are now accessible for midsize companies. Managers can use BI to analyze complex information to support their decision-making processes, combining data from a variety of sources to get an integrated, 360-degree view of the company. Find out how to select the right BI software, the right vendor, and the right approach to implementing BI. Read More

Garbage In, Garbage Out

We have all heard the phrase: “garbage in, garbage out.” When any company evaluates a customer relationship management (CRM) system, this is always one of the first expressions to come up. And yet we see company after company for which this is the exact problem. How can you make sure that this doesn’t happen to you? 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. Read More
 
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