Enterprise organizations are coming to understand that their data may be their single, most important asset. The challenge, however, is that most organizations have trouble sorting through their mountains of data to find those elements that will help them create competitive advantage and re-envision business models — let alone to meet regulatory and compliance demands, reduce costs, and ensure data governance.
Data lakes (and other related approaches) were supposed to help organizations solve these problems. But in most enterprises, data lakes have merely become data swamps, adding to the problem rather than solving it. According to Io-Tahoe, the real solution is more fundamental: an organization must understand how data flows through the enterprise and is interrelated to extract value from it.
The company’s solution, which recently became generally available, is a machine learning-driven data discovery platform that it designed to serve as the building blocks for all other data disciplines. It believes that once an organization understands data flows and relationships, it can then better protect, manage, and monetize it. What sets its approach apart, according to the company, is that unlike other systems that rely on meta-data, its platform analyzes the data directly and then uses machine learning to determine data flow and relationships.
Taking a platform-agnostic approach, the solution enables organizations to discover data assets and relationships across organizations and technology silos. Moreover, the company built the solution for business users, providing an intuitive interface that enables non-technical users to discover and leverage value from enterprise data. Once an organization has discovered the full landscape of its data assets, according to the company, it can use its platform for any number of purposes, including discovering sensitive and proprietary data, identifying data redundancies, creating smart data catalogs, ensuring data governance, and feeding information about its data into other systems.
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