Methods and Tools Operationalizing Data Governance

  • Source: SAP
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Data governance is about standardizing principles and implementing best practices. This white paper looks at common challenges to effective information use, and the practical lifecycle associated with defining and then implementing observance of data policies through defined business rules, inspection, reporting, and monitoring.

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Usability as an ERP Selection Criteria

  • Source: IFS
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Enterprise resource planning (ERP) software is often criticized for being complex and difficult to use—which puts up a barrier to receiving potential benefits. Systems with integrated search functionality and Web-like interfaces can make ERP solutions easier to use. Learn how to evaluate ERP software for its usability, so you can avoid investing in platforms that aren’t evolved toward usable and efficient interfaces. Read More

How to Use Projects to Master Asset Management

  • Source: IFS
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IFS co-founder Ulf Stern and senior advisor Anders Wilhelmsson discuss how project management functionality that is tightly integrated with enterprise asset management (EAM) and asset lifecycle management (ALM) tools is essential to executives of asset-intensive industries. Lacking this integration, valuable asset data is lost and asset utilization cannot be maximized. Read More

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Enterprise Information Management: In Support of Operational, Analytic, and Governance Initiatives

  • Source: TDWI
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In most organizations today, corporate information is managed by many tools and teams, but there’s a trend toward enterprise information management (EIM), a practice that coordinates teams and integrates tools. EIM is a best practice for creating, managing, sharing, and leveraging information in an enterprise, holistic manner that’s aligned with strategic, data-driven business objectives. See how you can achieve this. Read More

Governance from the Ground Up: Launching Your Data Governance Initiative

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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 decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost. Read More
 
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