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Big Data: Business Value Driver or Detour?
Businesses are looking to augment traditional transactional and reference data with the broad-ranging intelligence potentially available from external big data sources. With big data tools and techniques, virtually unlimited volumes of raw data can be accommodated, quickly and redundantly, distributed across as many server nodes as required, with various mapping and reduction iterations that apply structure as the data is being read.
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The MDS Desktop User Interface: Build or Buy?
Microsoft Master Data Services (MDS) is a powerful master data management platform, but it lacks good options for a user interface that is similarly “enterprise-grade”. As a result, companies that want to use MDS as an enterprise MDM platform may be looking at a choice between building a custom UI on top of MDS (or getting a system integrator to build one for them), or buying an off-the-shelf solution, such as Master Data Maestro. This paper will help you understand what is involved in making a build-versus-buy decision when it comes to the MDS UI.
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The MDS User Interface: Path to Optimal MDM Value
As a follow-on to our previous paper, The MDS Desktop User Interface: Build or Buy?, this paper will help you evaluate the build-versus-buy decision. This paper looks at the decision from the detailed perspective of the specific UI requirements that must be met to fully and effectively support the work of your power data stewards, and presents comparative build vs buy cost estimates for meeting these requirements. This will give you a solid understanding of exactly what you would have to build to deliver the required UI functionality on top of MDS.
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Automating MDM Processes: Build vs Buy
Microsoft Master Data Services (MDS) is a powerful master data management (MDM) platform, but it lacks an enterprise-class workflow capability to support the processes of creating and managing master data. While basic workflows can be manifested through the use of business rules and notifications in MDS, this approach quickly breaks down under even modestly complex workflow requirements. What is needed is an enterprise-class workflow capability that is scalable, flexible, and tightly integrated with the MDM solution.
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Enterprise CRM Consistent, Coordinated, Current
Customer relationship management (CRM) software helps organize and automate customer-focused business processes that foster customer loyalty. This requires a single “360° view” of the customer that crosses all phases of the customer relationship – in other words, a robust customer data integration (CDI) strategy. Enterprise growth can require the assimilation and integration of customer data in a variety of systems. All of these, along with the CRM system, must draw from the same “master” customer record in order to achieve the ultimate goals.
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Maximizing the Currency of Customer Data across the Enterprise
In order to fully leverage the value of customer-related data, that data must be up-to-date, accurate, complete and consistent across all enterprise systems. CRM solutions such as Microsoft Dynamics CRM can provide a powerful and highly sophisticated platform for organizing, automating and streamlining the sales and marketing business processes that nurture customer satisfaction and loyalty. But CRM systems are not the only enterprise systems that consume and contribute to customer information, or drive customer interactions.
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Enterprise CRM Coordination through Master Data Maestro
The Maestro Dynamics CRM Adapter enables organizations with Dynamics CRM to use Maestro as a master data management (MDM) hub to implement customer data integration (CDI) across a growing or complex organization. One CDI hurdle for global organizations is the management of customers across geographies with different regulatory policies and local customs, and different ERP systems, which may drive the selection of different CRM solutions. Another challenge is managing a CDI strategy across disparate business units.
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Beyond Matching - Enterprise Golden Record Management
Through viewing examples of contrasting data scenarios, it can be seen that data duplication problems vary greatly. The accuracy of a solution must be measured according to the problem it is solving. For the complex data problem, technical approaches to fuzzy matching algorithms are explored and demystified. The solution for data duplication extends beyond matching. Worrying about one technology (such as the matching algorithm), combining multiple piecemeal solutions, or misapplying solutions leads to suboptimal results at a high cost.
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The Case for Master Data Management
A white paper prepared by industry thought-leaders BPM Partners
According to the research and advisory firm TowerGroup, fifty percent of enterprises maintain master data separately in eleven or more source systems. Eighty percent of enterprises plan on centralizing master data. This means large organizations face a growing problem that already undermines the accuracy of the reporting and analysis of operations. Master data is being categorized, rolled up, and filtered inconsistently and erroneously across the enterprise.
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Evaluation Criteria for a Master Data Management Solution
With each year of rapidly evolving business requirements, companies have grown their information system landscapes into complex topographies of ERP, CRM, Business Intelligence, Financial Reporting, Planning and other operational systems. While each is required for successful business operations, ensuring cross-system accuracy and consistency has become critical. That is where master data management comes in. The first step in evaluating a master data management solution is to make sure that the solution focuses on key areas.
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The Human Factor of Master Data Management
You won't find a CIO or business analyst anywhere who will disagree with the importance of leveraging all of a company's diverse data sources in order to drive successful business intelligence (BI) and business performance management (BPM) initiatives. But not all of the data required to run a business exists in these systems. In fact, the business's primary source of operational intelligence resides in the human knowledge repository of the company's workers, and the real-time integration of that human intelligence is required if operational BI is to become a reality.