Data exchange (sharing and compiling data) between industries is on the rise, meaning this trend is set to continue. The sharing of this intelligence represents an opportunity for some companies to better understand their audiences and improve customer experience, and for others to unlock new revenue streams.
Making a success of big data analytics is a bit like constructing a skyscraper. Foundations need to be laid and the land prepared for construction, or else the building will rest on shaky ground. Download your free book, "Driving Growth & Innovation with Big Data" The success of any analytics project depends on the quality and relevance of the data it’s built upon. The issue today is that companies collect an exponentially large volume and variety of information in many...
There are many different ways to build a recommendation engine and most will combine multiple techniques or approaches. In this article, I want to cover just one approach, association rules, which are fairly easy to understand and require minimal skills in mathematics. If you can work with simple percentages, there’s nothing more complex than that here.
While you can run your business on the data stored in Oracle Autonomous Data Warehouse, there’s lots of other data out there which is potentially valuable. Using Big Data Cloud, it’s possible to store and process that data, making it ready to be loaded into or queried by Autonomous Data Warehouse Cloud. The point of integration for these two services is object storage.
In this machine learning use case, an Oracle customer discovered the right solution for their motherboard failures. They turned to a machine-learning pattern recognition algorithm called Multivariate State Estimation Technique (MSET).
The rewards of big data can be compelling. At the same time, you'll want to consider machine learning challenges before you start your own project. In this article, we cover challenges which include addressing the skills gap, knowing how to manage your data, and operationalizing your data.
New advancements enable us to do more with our big data than we’ve ever been able to before. But there are two other advances that are playing a huge role in this revolution - open-source software and cloud computing.