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Augmented analytics tools: a complete guide
Sponsored by ComputerWeekly.com
Augmented analytics tools are designed to simplify the BI and analytics process for business users and citizen data scientists. They've become featured elements in new software releases from BI vendors of all stripes looking to keep up with rivals in automating data discovery, preparation and analysis functions. This e-guide offers insights into the technology's potential uses and critical issues.
Table Of Contents
- 5 augmented analytics examples in the enterprise
- AI at the core of next-generation BI
- Augmented BI tools speed insights with less manual labor
- The benefits and challenges of augmented data discovery tools
- Why data literacy skills still matter with augmented analytics
- Everything you need to know about augmented analytics vendors
- How the rise of augmented analytics tools affects BI vendors
- How do augmented analytics and BI tools differ?
- Automated tools broaden the future scope of data science
- Augmented data management draws more enterprise interest
- NLP makes augmented data discovery a reality in analytics
- 4 ways natural language querying in BI tools can benefit users
- NLP progress in BI and analytics slowed by language barriers
- Augmented analytics tools, NLP search, graph are trending
- Beyond customer sentiment: How to put NLP technology to work
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