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Effective analytics depends on preparation of data
Sponsored by SearchDataManagement
In simple terms, successful analytics depends heavily on how well the information is prepared. Except it's not that simple. Many data scientists believe preparation of data for analysis is the biggest challenge they face, and it's all the more formidable in the era of big data. Beyond pulling data from their own internal sources, organizations must reach into the universe of websites, sensors, customer email messages and social networks bombarding them from all directions. To handle this information onslaught, data scientists potentially can find solace in an emerging class of self-service tools to aid them in the preparation of data.
In the first part of this handbook, consultant David Loshin explains how business users and data scientists can use data preparation tools to get more comprehensive and customized views of data. Next, reporter Jack Vaughan examines how self-service data preparation tools with machine learning helped a BI team at one company streamline report creation and business users at another company to load data, put integrations together and see the effects immediately without burdening the IT staff. In the final segment, Vaughan returns to address the long-running data warehouse controversy and the concept of using data curation to smooth the process of data discovery, cleaning, transformation and integration.
Table Of Contents
- Business analysts empowered to integrate and prepare data
- Self-service data prep courts machine learning
- Monolithic data models eat dust
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