IBM on Tuesday unveiled its Integrated Analytics System, a unified data system that enables customers to deploy advanced analytics capabilities across private, public or hybrid cloud environments.
It supports a wide range of data types and platforms, including the IBM Db2 Warehouse On Cloud Hadoop and IBM BigSQL. It's built with the IBM common SQL engine, so users can easily move workloads to public or private cloud environments. And with the IBM Data Science Experience and Apache Spark embedded on the system, machine learning processing is dramatically simplified. All of this cuts down on the time and money spent on steps like moving data, data cleansing and data discovery, explained Rob Thomas, general manager of IBM Analytics.
A "fundamental thesis we have at IBM when it comes to the topic of data science," he said, is that "it's most efficient if you bring the analytics, the machine learning, to the data as opposed to the other way around."
For example, a financial services company may want to leverage data from several sources to perform a risk management assessment. They could load their client's information and their stock portfolio information into the Integrated Analytics System, where it's encrypted. With common SQL, they can federate data sitting on a public cloud, such as macroeconomic data, and unstructured data, such as Nasdaq data, sitting in a Hadoop environment.
"While keeping customer data and stock data secure on the analytics system, you're federating and leveraging other data sets outside of that system," Thomas explained.