Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced it has been named a Leader in the use of artificial intelligence (AI) and machine learning (ML) for data management in a new research report and decision guide from Enterprise Management Associates (EMA). The research report, which names Io-Tahoe a Leader, says companies which deploy AI-enabled analytics and data management solutions can potentially save up to $5,000,000 a year. EMA research also finds that they can create more value through enhancements such as increased speed of innovation; the report claims that 83 per cent of the companies surveyed are already seeing cost savings, along with a significant reduction in annual person-hours required to complete analysis of the data. "AI enablement signifies a major shift from passive to active use of metadata," said John Santaferraro, EMA's Research Director, Analytics, Business Intelligence, and Data Management. "The passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services."
Io-Tahoe, a machine learning-driven smart data discovery company recently announced the launch of its smart data discovery platform at the Gartner Data & Analytics 2018 Summit, where it will showcase the product. The new version includes the addition of Data Catalog, a new feature designed to allow data owners and stewards to use a machine learning-based smart catalog to create, maintain and search business rules. Also, it would help define policies and provide governance workflow functionality. It reportedly 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.
BOULDER, Colo., September 12, 2019 /PRNewswire-PRWeb/ -- Enterprise Management Associates (EMA), a leading IT and data management research and consulting firm, today announced the leading innovators in the use of artificial intelligence (AI) and machine learning (ML) in metadata services, data integration, and data preparation. Two EMA research reports also identify the value of using AI and ML in data management, with savings up $5,000,000 annually. Due to the measurable value created by AI enablement, leading vendors create more value for their customers compared to legacy data management technology. Passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services. In alphabetical order, here are the Top 3 vendors for the use of AI and ML in metadata services platforms: Informatica, Reltio, and Unifi.
Like most Fortune 50 firms, General Electric relies on an abundance of computer systems to power its enterprise. And like most firms that size, synching up and aligning the data emitted by different systems is major challenge. But thanks to an innovative data discovery solution powered by machine learning, GE found a solution. GE's Hadoop-based data lake contains 500 PB of data that originated from about 120 different systems, according to Diwakar Goel, the VP and Chief Data Officer of GE Digital and Finance. Data is sourced from a variety of ERP packages, accounting systems, and other applications, such as Ariba, Concur, and Salesforce.com.
Data, data, and data; in this intelligent world of analytics, we are surrounded by data. From tracking the customer buying path to making decisions based on business intelligence, data seems to be at the forefront of everything that organizations are doing. In the race to be at the top of data analytics, organizations are implementing measures that position them in a favorable spot. The key to extracting the most from your data is to have pertinent data governance policies in place. With the requirement for data governance, it is even better to have real-time governance of data so that analytics flow smoothly without the need for consistently overlooking data.