Presentation will explore how companies can remove the limitations of scale and complexity of doing BI on Big Data
LOS GATOS, Calif. – Mar 05, 2018 – Kyvos Insights, a big data analytics company, today announced that it will present a session at the Gartner Data and Analytics Summit 2018 conference in Grapevine, Texas from March 5-8, 2018. The presentation, “BI on Big Data – With Instant Response Times” will explore how companies are successfully addressing challenges related to data complexity. The Gartner Data and Analytics Summit brings together data and analytics leaders who are fueling digital transformation, creating monetization opportunities, improving the customer experience and reshaping industries.
The “BI on Big Data – With Instant Response Times” session will examine how companies can empower their business teams to get insights from their data. Paritosh Rohilla, senior solutions architect (corporate data lakes), at Verizon and Ajay Anand, vice president of products at Kyvos Insights, will discuss how organizations have set up data lakes to bring in information from diverse sources, but ultimately created a new set of challenges as business users are not able to fully utilize the data due to complexity and slow query response times. Rohilla and Anand will describe Verizon’s approach to empowering its business teams to get insights from data at any scale, instantly and with no learning curve.
The session will occur on Wednesday, March 7, at 12:30 p.m. Central time. Attendees are also invited to learn more about Kyvos and its work with many of the world’s most successful companies – including Fortune 100 stalwarts in manufacturing, communications, technology and finance – by visiting the Kyvos booth, #809.
“Numerous organizations today deploy data lakes to capture information from diverse sources only to discover that complexity and slow query response times prevent them from fully utilizing the data,” said Anand. “We are excited to share how our solution can help organizations to overcome this complexity and take advantage of the insights available from the analysis of vast amounts of information in data lakes.”
