Manufacturing technology professionals have been working with data of many types for years. Our sensors, instrumentation, and control systems yield terabytes of data. Then we bury them in historians or other databases on servers we know not where.
Companies are popping up like mushrooms after a spring rain with a variety of approaches for handling, using, analyzing, and finding all this data. Try on this one.
Io-Tahoe LLC, a pioneer in machine learning-driven smart data discovery products that span a wide range of heterogeneous technology platforms, from traditional databases and data warehouses to data lakes and other modern repositories, announced the General Availability (GA) launch of the Io-Tahoe smart data discovery platform.
The GA version includes the addition of Data Catalog, a new feature that allows data owners and data stewards to utilize a machine learning-based smart catalog to create, maintain and search business rules; define policies and provide governance workflow functionality. Io-Tahoe’s data discovery capability provides complete business rule management and enrichment. It enables a business user to govern the rules and define policies for critical data elements. It allows data-driven enterprises to enhance information about data automatically, regardless of the underlying technology and build a data catalog.
“Today’s digital business is driving new requirements for data discovery,” said Stewart Bond, Director Data Integration and Integrity Software Research, IDC. “Now more than ever enterprises are demanding effective, and comprehensive, access to their data – regardless of where it is retained – with a clear view into more than its metadata, but its contents as well. Io-Tahoe is delivering a robust platform for data discovery to empower governance and compliance with a deeper view and understanding into data and its relationships.”
“Io-Tahoe is unique as it allows the organization to conduct data discovery across heterogeneous enterprise landscapes, ranging from databases, data warehouses and data lakes, bringing disparate data worlds together into a common view which will lead to a universal metadata store,” said Oksana Sokolovsky, CEO, Io-Tahoe. “This enables organizations to have full insight into their data, in order to better achieve their business goals, drive data analytics, enhance data governance and meet regulatory demands required in advance of regulations such as GDPR.”
Increasing governance and compliance demands have created a dramatic opportunity for data discovery. According to MarketsandMarkets, the data discovery market is estimated to grow from $4.33 billion USD in 2016 to $10.66 billion USD in 2021. This is driven by the increasing importance of data-driven decision making and self-service business intelligence (BI) tools. However, the challenge of integrating the growing number of disparate platforms, databases, data lakes and other silos of data has prevented the comprehensive governance, and use, of enterprise data.
Io-Tahoe’s smart data discovery platform features a unique algorithmic approach to auto-discover rich information about data and data relationships. Its machine learning technology looks beyond metadata, at the data itself for greater insight and visibility into complex data sets, across the enterprise. Built to scale for even the largest of enterprises, Io-Tahoe makes data available to everyone in the organization, untangling the complex maze of data relationships and enabling applications such as data science, data analytics, data governance and data management.
The technology-agnostic platform spans silos of data and creates a centralized repository of discovered data upon which users can enable Io-Tahoe’s Data Catalog to search and govern. Through convenient self-service features, users can bolster team engagement through the simplified and accurate sharing of data knowledge, business rules and reports. Here users have a greater ability to analyze, visualize and leverage business intelligence and other tools, all of which have become the foundation to power data processes.
