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Big data analytics is about applying advanced methods and technologies to derive insights from very large and diverse data sets that often include varied data types and streaming data. This TDWI Checklist Report discusses steps for realizing value from big data and analytics for better customer intelligence.
Tools from new vendors incorporating new big data technologies (such as Hadoop) for distributed and parallel computing and NoSQL data management are rapidly being evaluated and brought into organizations. At the same time, numerous vendors of traditional platforms are offering the same or similar new technologies that are more robust and have more enterprise-ready capabilities than the new technologies can provide. This Checklist Report offers several important items to consider for your data management infrastructure, whether your implementation is on-premises, in the cloud, or a hybrid.
Download this report to learn how business-side leadership can step up to ensure that chaos does not reign in the era of less direct IT involvement, and provide direction for the adoption of new technologies and practices for the betterment of the enterprise as a whole.
This TDWI Checklist Report examines the user best practices and vendor tool functions for analyzing streaming data, with a focus on those that enable new applications in continuous operational intelligence.
Master data management (MDM) can be practiced many different ways, with various user conventions and a broad array of vendor-built technologies. However, this report focuses on a specific practice called unified MDM, and examines the characteristics typical of business programs and technical solutions for unified MDM.
Users must start planning today for active data archiving. To help them prepare, this TDWI Checklist report will drill into the desirable attributes, use cases, user best practices, and enabling technologies of active data archiving.
Following TDWI’s previous Checklist Report on this topic, How to Gain Insight from Text, this Checklist discusses the relevance of text analytics for improving user experiences with the semi-structured and unstructured textual big data now at their disposal.
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