Smart data discovery
Traditional data discovery based on data warehouse and manual manipulation of data, or data discovery based on Big Data technology and BI, can be expensive, complex and extremely costly and time intensive to implement. This is primarily because the vendors behind the software has not developed something truly innovative, but has chosen to re-purpose or further develop already existing technology, and they rely on selling quite a few hours along with the solution. Some history: Hadoop 0.1.0 was released 11 years ago, the first paper on using semantics in IT was written by Robert W. Floyd in 1969.
IntOp aims to be one of the first in the market to develop an integrated solution that deliver end-to-end capabilities to enable Smart Data Discovery. IntOp Engine, and our IntOp Fetch Smart Data Discovery front end for collaborative analytics and information navigation enable any user to become a data scientist. IntOp delivers the full range of functionality from data ingestion via digitization, ML/AI, contextualization and data delivery and presentation.
Smart Data Discovery and context
Gartner Group defines Smart Data Discovery as a technology that “Automatically finds, visualizes and narrates important findings such as correlations, exceptions, clusters, links and predictions in data that are relevant to users without requiring them to build models or write algorithms. Users explore data via visualizations, natural-language-generated narration, search and natural-language query technologies.”
In February 2017, Gartner released a report called “Augmented Analytics Is the Future of Data and Analytics”. In this, they launch five business cases and 15 critical capabilities that vendors will be scored towards to be placed in the Magic Quadrant for Business Intelligence and Analytics. Most of the vendors in this quadrant will only be able to deliver the functionality either in the front end or backend of the data pipeline required to deliver Smart Data Discovery.
The key element that differentiates IntOp’s technology from competing technologies, is that we are able to apply contexts top-down. This is done by enabling our technology to ingest and contexts that are identical to how the department, user or manager of any given corporation understands them. By doing this, any contexts that are connected to the information, will be instantly useful and intuitively understood by any and all users in that corporation. This cuts down machine time, time and effort used and errors due to programmatic dependencies.
By empowering everyone with fast Smart Data Discovery based on already existing contexts, everyone becomes a self-dependent data scientist. This in turn will make a positive impact on the financial results and improve work environments.
October 26th, 2017
Øystein Drivflaadt, CTO, IntOp