From Data to Insight

ORACLE AMERICA

Robin Potharst
http://https://www.oracle.com/big-data/big-data-discovery/capabilities.html
Analytics Search Visualization Collaboration GIS Cloud Geospatial Maps

Five Phases of Value Oracle Big Data Discovery addresses the issues that have stymied big data analytics. With it, companies finally are able to leverage their raw data in Hadoop to provide demonstrable value. Oracle Big Data Discovery delivers value through a five-phase process: find, explore, transform, discover and share. + Find: Pinpoint Relevant Data A retail analyst who wants to improve the results of a marketing campaign has lots of potential data to sift through—customer tweets, loyalty program details, contact center complaints and more. However, determining which of that data is timely and trustworthy isn’t easy.Using the intuitive interface of Oracle Big Data Discovery, the analyst can navigate a rich catalog of all the raw data in a Hadoop cluster and quickly identify what’s relevant. Searching the data is as easy as shopping online. + Explore: Understand Data Potential Understanding the potential value of data consumes a lot of analysts’ time. For instance, an analyst for an auto manufacturer seeking to streamline its manufacturing processes would likely endure many false starts when exploring the mass of information related to the engine-building process, from poorly scheduled lunch breaks to disconnects between suppliers. Oracle Big Data Discovery dramatically speeds the data exploration process. Analysts can sort by information potential, with the most interesting attributes appearing first. In addition, analysts can easily experiment with different combinations of data to understand correlations, so they can rapidly determine whether the data set is worthy of more attention. The system also helps them quickly get a handle on data quality and other key elements, preventing time and money from being wasted on projects with limited potential. + Transform: Intuitive, User-Driven Data Wrangling Typically, data in Hadoop needs to be manipulated and prepared before it can be used for analytics. With Oracle Big Data Discovery, analysts use an intuitive spreadsheet-like approach to transform big data for use in analytics. At the same time, the data can be enriched to infer location and language or detect topics, themes and sentiment buried in the raw text. Rather than spending 80 percent of their time on data preparation, analysts can quickly transform even massive volumes of big data, making it available for the entire enterprise and freeing them to spend the bulk of their time on analytics. + Discover: Unleash Creativity Discovering big data insights requires creativity, which can be difficult to hire for or develop in-house. With Oracle Big Data Discovery, enterprises can get more out of their analytics talent through tools that automatically blend data for deeper perspectives and help analysts see new patterns in rich, interactive data visualizations. For example, if a telecom analyst wants to investigate the reasons for customer churn, he can use Oracle Big Data Discovery to mash up or join different data sets. This will reveal a whole new perspective; for example, it might show that customers in a certain geographic region using a certain handset are canceling their accounts because of a technical glitch that is disrupting service. The data also can be filtered with keyword search and guided navigation, providing the consumer-like experience that users increasingly demand today from even the most complex enterprise technology. + Share: Drive Collaboration Oracle Big Data Discovery fulfills the promise of democratizing big data analytics by enabling the results to be shared and published. Suddenly, the information can become a focal point of enterprise collaboration and collective discovery. Entire teams can share projects, bookmarks and galleries of snapshots, enabling them to collaborate and iterate. Analysts, meanwhile, can publish their data transformation and enrichment results back to Hadoop, securing the work they’ve done to maximize the value of the data

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