November 13, 2018 Press Release

Io-Tahoe Brings AI-Driven Data Discovery and Catalog to London’s Largest Data and Analytics Conferences

LONDON, November 13, 2018 – Io-Tahoe, a pioneer in artificial intelligence (AI)-driven data discovery and catalog products, announced it will attend two major conferences on Big Data this month in London.

Io-Tahoe will join more than 100 other leading global data solution providers and consultants at the Big Data LDN (London), which begins today. Io-Tahoe will showcase its AI-driven data discovery and catalog software from booth 133. Big Data LDN is a free to attend, two-day combined conference and exhibition focusing on how to build dynamic, data-driven enterprises. Delegates will learn from pioneers, experts and real-world case studies, discovering new tools and techniques, enabling them to deliver business value from successful data projects.

Later in the month, Io-Tahoe will also exhibit at the one-day Big Data Analytics Conference – the UK’s premier event dedicated to building world-class analytics for enterprises, being held at the Park Plaza Victoria.

“AI-driven data discovery and cataloging accomplishes what organizations simply cannot achieve manually or without automation,” said Lola Bhadmus, vice-president of marketing and communications at Io-Tahoe. “During these London conferences, we will demonstrate the significance of automatically discovering the hidden and implied data relationships; and show why this is the foundation to gain consistently greater insights from enterprise data.”

Conference attendees at both events will be able to view a demonstration of Io-Tahoe’s AI-driven data discovery and catalog product. Io-Tahoe’s automated data catalog enables data and business professionals to easily create, maintain and search a business glossary of data sources and critical data relationships identified through Io-Tahoe’s relationship discovery. Population of the data catalog is automated by using artificial intelligence and leveraging the discovery functionality and natural language analysis to automatically tag data. This automation facilitates smarter crowd sourcing allowing data owners, stewards and subject matter experts to define and govern multiple data rules.

As the Big Data London website notes, “Big Data is much more than simply a matter of size – it presents an opportunity to discover key insights and emerging trends in data, make business more agile, board room decisions better informed, and answer questions that have previously been considered unanswerable.” The use of AI to help discover patterns across those immense amounts of data as a way of discovering those answers is seen as key to the success of today’s analytics: AI has emerged as a key factor in helping companies gain continuous control of their data, as a major element of a successful data governance strategy. This is especially vital, given today’s focus on the necessity for companies to be able to track all of their data, regardless of where it resides.

# # #

About Io-TahoeIo-Tahoe ( is an enterprise AI-driven data discovery and catalog product that enables enterprises to accelerate to next-generation data management practices, radically improving data governance and regulatory compliance while driving significant advancements in business analytics and technological transformation. Io-Tahoe’s discovery goes beyond traditional metadata, leveraging ML and AI to look at the data themselves, automatically discovering critical, implied and often unknown relationships across an inherently heterogeneous and complex enterprise landscape.

Io-Tahoe has been custom-built by a team with a deep understanding of data challenges, giving it first-hand insight and appreciation into our customers’ diverse and complex data needs. Io-Tahoe is particularly valuable to businesses with large numbers of customers and diverse data sets, such as those in the financial services, utilities, retail, transportation, insurance, healthcare and manufacturing industries.

Follow Io-Tahoe on Twitter at:
Follow Io-Tahoe on LinkedIn at: