What Are You Predicting in 2016?

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What if you could use data to predict outcomes in 2016?

We all see how data is generated in huge quantities – weblogs, phones, Internet of Things (IoT), email transactions, sales transactions, etc.  You may be less familiar with the breadth of data that can be purchased to supplement internal data sources. Furthermore, you may not be familiar with the low-cost tools that are available for building predictive algorithms.

Amazon Machine Learning is a great example. You can start with a spreadsheet where the columns are data points you suspect will predict an outcome (the last column). This Machine Learning service will walk you through the process and tell you how well your model performs. Using Spark and its machine learning libraries, you can prepare your data and build more sophisticated models. The newest tool is SparkR. As the name implies, it combines the power of Spark with the power of R. This means you can use R, a typical open-source statistics package, at scale. R opens the door to a rich library of proven models.

As we look forward to 2016, we see companies building competitive advantage by harnessing their existing data sources, supplementing with outside sources, and using analytics to predict outcomes.

  • A manufacturer will analyze property deed transactions to predict order volumes and manage inventory
  • A mortgage services company will leverage historical results to predict portfolio value and offer clients new services
  • A consumer marketing firm will predict possible responses to new campaigns and promotions
  • A loyalty app provider will provide patrons personalized recommendations
  • An equipment services company will predict maintenance needs to stave off downtime

Each of these use cases employs proven techniques. The difference is the cost of ownership. These new tools make it much easier to scrub and integrate multiple data sources. They reduce the specialization required to build models. Operating in a scalable cloud environment, they perform at enormous scale and low costs. These are prime examples of Big Data and the subsequent ROI. Relus can help you get started in your evaluation and implementation of predictive data analytics.

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