The Intersection of “Data Capital” and Advanced Analytics
April 17, 2015
By Mark Hornick-Oracle
(This article was first published on Oracle R Technologies, and R-bloggers)
We’ve heard about the Three Laws of Data Capital from Paul Sonderegger at Oracle: data comes from activity, data tends to make more data, and platforms tend to win. Advanced analytics enables enterprises to take full advantage of the data their activity produces, ranging from IoT sensors and PoS transactions to social media and image/video. Traditional BI tools produce summary data from data – producing more data, but traditional BI tools provide a view of the past – what did happen. Advanced analytics also produces more data from data, but this data is transformative, generating previously unknown insights and providing a view of future behavior or outcomes – what will likely happen. Oracle provides a platform for advanced analytics today through Oracle Advanced Analytics on Oracle Database, and Oracle R Advanced Analytics for Hadoop on Big Data Appliance to support investing data.
Enterprises need to put their data to work to realize a return on their investment in data capture, cleansing, and maintenance. Investing data through advanced analytics algorithms has shown repeatedly to dramatically increase ROI. For examples, see customer quotes and videos from StubHub, dunnhumby, CERN, among others. Too often, data centers are perceived as imposing a “tax” instead of yielding a “dividend.” If you cannot extract new insights from your data and use data to perform such revenue enhancing actions such as predicting customer behavior, understanding root causes, and reducing fraud, the costs to maintain large volumes of historical data may feel like a tax. How do enterprises convert data centers to dividend-yielding assets?
One approach is to reduce “transaction costs.” Typically, these transaction costs involve the cost for moving data into environments where predictive models can be produced or sampling data to be small enough to fit existing hardware and software architectures. Then, there is the cost for putting those models into production. Transaction costs result in multi-step efforts that are labor intensive and make enterprises postpone investing their data and deriving value. Oracle has long recognized the origins of these high transaction costs and produced tools and a platform to eliminate or dramatically lower these costs.
Further, consider the data scientist or analyst as the “data capital manager,” the person or persons striving to extract the maximum yield from data assets. To achieve high dividends with low transaction costs, the data capital manager needs to be supported with tools and a platform that automates activities – making them more productive – and ultimately more heroic within the enterprise – doing more with less because it’s faster and easier. Oracle removes a lot of the grunt work from the advanced analytics process: data is readily accessible, data manipulation and model building / data scoring is scalable, and deployment is immediate. To learn more about how to increase dividends from your data capital, see Oracle Advanced Analytics and Oracle R Advanced Analytics for Hadoop.