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 benefits of data governance


The Path to Healthy Data Governance through Data Security
Companies today are challenged to maintain their data safe and secure from hackers and others with unauthorized access. In his article, TEC business

benefits of data governance  organizations can reap numerous benefits from having an information security governance initiative in place: Effective and auditable compliance with proper data security regulations. Effective data security risk assessment and effective response to data security issue resolution. Proper accountability for all data security–related activities. Operational cost reduction by mitigating data security risks and reducing the number of actual issues to be resolved (unauthorized access, undesired data leaks), a

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Software Functionality Revealed in Detail

We’ve opened the hood on every major category of enterprise software. Learn about thousands of features and functions, and how enterprise software really works.

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Lean IT Governance: The Most Realistic and Attainable Approach to IT Governance


The most realistic and attainable approach to IT governance is a "lean" approach via project portfolio management (PPM). The lean approach requires a light footprint life cycle, rapid implementation of proven practices, and centralized data. It is based on simplicity and achievability, building on what works while establishing headroom for continuous improvement.

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Learning from BPM Leaders: Benefits, Prerequisites, and the Impact of Successful BPM Initiatives


Business process management (BPM) software implementations are now moving into the mainstream. Many early adopters are already using BPM to foster business innovation. You can benefit from the knowledge gained by BPM leaders and innovators, and their prerequisites for BPM success. These findings can serve as best-practice guidelines for your BPM initiative, so you can derive maximum value from your BPM investment.

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The Challenges of Defining and Managing Governance, Risk Management, and Compliance


A broader, more structured approach is needed to effectively manage governance, risk management, and compliance (GRC). Enterprises will then be better able to guide their people, standardize their processes, and unify technology to embed GRC at all organizational levels.

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Data Governance: Controlling Your Organization’s Mission-critical Information


Controlling your company’s key information through data governance is more than just good practice—it can make the difference between success and failure at meeting compliance requirements and at arming your organization with a competitive edge.

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Busting Out of the Inbox: Five New Rules of 1to1® E-mail Marketing


Situating e-mail in a multichannel marketing plan is more complicated than it used to be. Where exactly does e-mail fit in the world of blogs, vlogs, and podcasts—where MSN, Google, and Yahoo! call the shots? Marketers need to understand which strategies and tactics are most effective to ensure that their e-mails will be delivered, opened, and acted upon.

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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.

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The Evolution of a Real-time Data Warehouse


Real-time data warehouses are common in some organizations. This article reviews the basic concepts of a real-time data warehouse and it will help you determine if your organization needs this type of IT solution.

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Re-think Data Integration: Delivering Agile BI Systems with Data Virtualization


Read this white paper to learn about a lean form of on-demand data integration technology called data virtualization. Deploying data virtualization results in business intelligence (BI) systems with simpler and more agile architectures that can confront the new challenges much more easily.

All the key concepts of data virtualization are described, including logical tables, importing data sources, data security, caching, and query optimization. Examples are given of application areas of data virtualization for BI, such as virtual data marts, big data analytics, extended data warehouse, and offloading cold data.

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Increasing Sales and Reducing Costs across the Supply Chain-Focusing on Data Quality and Master Data Management


Nearly half of all US companies have serious data quality issues. The problem is that most are not thinking about their business data as being valuable. But in reality data has become—in some cases—just as valuable as inventory. The solution to most organizational data challenges today is to combine a strong data quality program with a master data management (MDM) program, helping businesses leverage data as an asset.

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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.

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