Connect Art of Business with Science of Data
The amount of data generated is increasing exponentially owing to the convergence of various technological forces like cloud, social platforms, IoT, mobiles, etc. However, in today’s day and age, leveraging conventional methods of managing, transforming and analyzing the data, that is available both, internally (within the enterprise) and externally (social, image, unstructured, etc), is no longer sufficient. It requires a transformative approach that can be deployed in complex AI pipelines, Master Data Management systems, big data procedures such as Data Lakes, etc where one applies reasoning to learning and delivers knowledge based analytics. Capabilities in Statistical AI, viz.,ML, etc are now table stakes. What we need is more than that – AI Plus or Smart AI, which is leveraging Semantics (Symbolic AI) as well, to drive intelligence by adding a layer of reasoning, over and above, the regular ML techniques.
Business users find it challenging to understand and gain confidence in the predictions machine learning computations deliver. They do not have an integrated decision making system that combines BI, Real Time Alerts and Predictions, with “what-if’s” to take business decisions in right time. They need platforms that host ML models for live scoring and evaluation and integrate knowledge graphs for informed decision making – AI Dashboards. The platforms need to take data from various sources: - Relational, NoSQL, Graph databases and files and streams with various formats such as comma/tab delimited, XML, JSON, semantic RDF triples, etc. And support harmonization of data with disparate schemas to feed AI Pipelines to deliver predictions using machine learning, extracted knowledge using natural language processing for text and unstructured data as well as social, geo spatial and network security analytics using graph analysis. Harnessing deep insights from the data also requires interpreting and analyzing data using subject specific schemas of relationships and meaning (Ontology). Domain ontologies can be loaded into the same platform as just another data set and the runtime enables the ontology to be applied on the source data yielding rich insights along with relationship and context information which otherwise is buried in the data to develop Knowledge Graphs.