IBM’s Watson project has always been about the largest base of industry offerings across the board. It provides solutions to industries ranging from health, education, customer engagement to IoT and financial services. With Watson on the IBM Cloud, users can maintain their ownership of data, IP, and insights. Since Watson understands a given industry’s language, it can comprehend datasets fully and its AI can extract meaningful insights from a variety of data types.
IBM Watson was initiated with a vision for masses to be able to leverage the power of data science. In 2016, IBM took a step forward in furthering this vision by introducing the Watson Data Platform. Watson Data Platform is a set of features available on the IBM Cloud Platform. Technologies like machine learning and predictive modeling have such potential to cause disruption and enable change and innovation that IBM only saw fit, and felt that this power should be available to all.
The Watson platform enables machine learning steeped in artificial intelligence. On 2nd November 2017, IBM announced new offerings in its Watson Data Platform which primarily included data cataloging, data refining, and analytics engine. It is estimated that by 2018, around 75 percent of developers will require AI services in their apps and this will only lead to increase in difficulties related to making sense of huge and complex data sets across the enterprise while simultaneously ensuring data security. The primary offerings in the expansion include:
Instead of spending majority of the time searching for data, data professionals can save value on time by making use of automated and simplified data discovery and governance. All data assets can be accessed from cloud platforms in one place and access can be controlled using regulatory compliance and automatic policy enforcement.
• All structured and unstructured data across the enterprise platforms are condensed to the form of a searchable index.
• A 360-degree view from all platforms in one place and data governance regarding access is ensured.
• Machine learning assigns meta tags to organize data into searchable stores.
• The tool is enabled by the open standards base of the Watson Data Platform and close to thirty connectors bringing together a 360-degree view of all existing data within the enterprise.
Analytics ready, quality information is produced by data refinery saving a lot of prep time in the process.
• More than 100 built-in functions help discover, standardize and cleanse data thereby identifying and uncovering patterns ready for insights.
• Data scientists can work in real-time with business teams using connectors to over 25 data sources to tap into data in the cloud.
• Anomalies and data types are detected using statistical metrics and charts and graphs then help understand data distribution.
• Makes a combined use of Apache Spark and Apache Hadoop service to enable users to understand the value of each dataset.
• Data scientists can work with developers to build models in real-time without having to worry about the infrastructure intricacies behind it.
This platform ultimately helps build smart applications with quick visualization, sharing and insights generated from real-time data. Embedded intelligence-based applications help make better insight based decisions. Using this platform for integrated data and analytics, users can innovate and expand their businesses.