The journey to predictive analytics ‘nirvana’
https://siliconangle.com/2016/01/21/the-journey-to-predictive-analytics-nirvana-rmwisdom16/
#theCUBE #RMWisdom16 #RapidMiner #Hortonworks #SiliconANGLE
by Marlene Den Bleyker | Jan 21, 2016
Hortonworks, Inc., one of the leading providers of open-source solutions, is partnering with banks to help improve the Apache Hadoop open-source software framework and ecosystem. These partnerships are driving Hadoop to be a true application-level ecosystem that enables growth in areas such as data management, aggregation and governance capabilities.
Riding the wave of Big Data
Vamsi Chemitiganti, GM of financial services at Hortonworks, joined Dave Vellante, cohost of theCUBE, from the SiliconANGLE Media team, at RapidMiner Wisdom 2016 on New York City to discuss how the financial services industry is gaining value from its data with Hadoop.
Hortonworks has been riding the Big Data wave for a long time, and Chemitiganti said that it is an interesting industry to be in, noting the Hadoop and Big Data movement. He told Vellante,
“Banking being in the forefront with the predictive analytics and real-time decisions, as well as the historical space with rest data aggregation and anti-money laundering, it’s really exciting times.”
Data defense vs. offense
As Big Data evolves, Chemitiganti said that banks are using data in the defensive dimensions. The industry is trying to assess risk in different areas, from issues with product offerings they offer to risks in a tumultuous financial market.
He also noted that data is useful for a liquidator in terms of the web of connective financial institutions. According to Chemitiganti, banks are on the offensive as well, using the data and real-time interactions to drive more of a responsive experience to consumers to transform their business.
Stuck on data janitorial work
Where is the banking industry in terms of predictive analytics? Chemitiganti explained, “We are at 20 to 30 percent of the journey to Nirvana, which would essentially be to plug in predictive models that do clustering, segmentation, classification and also maybe deep learning at scale.”
He noted that the challenges lie in the large portion of the process being spent doing “data janitorial work” by data scientists and that productive work should be creating models and deploying them, detecting credit card fraud, anti-money laundering projects and things that result in real business value.
Banks are using Hadoop and Big Data to solve many problems. Chemitiganti would like to see banks extend the use of Hadoop and said they are only limited by their imagination.
@theCUBE @Hortonworks @RapidMiner, Inc. @SiliconANGLE theCUBE
#RMWisdom16
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The journey to predictive analytics ‘nirvana’
https://siliconangle.com/2016/01/21/the-journey-to-predictive-analytics-nirvana-rmwisdom16/
#theCUBE #RMWisdom16 #RapidMiner #Hortonworks #SiliconANGLE
by Marlene Den Bleyker | Jan 21, 2016
Hortonworks, Inc., one of the leading providers of open-source solutions, is partnering with banks to help improve the Apache Hadoop open-source software framework and ecosystem. These partnerships are driving Hadoop to be a true application-level ecosystem that enables growth in areas such as data management, aggregation and governance capabilities.
Riding the wave of Big Data
Vamsi Chemitiganti, GM of financial services at Hortonworks, joined Dave Vellante, cohost of theCUBE, from the SiliconANGLE Media team, at RapidMiner Wisdom 2016 on New York City to discuss how the financial services industry is gaining value from its data with Hadoop.
Hortonworks has been riding the Big Data wave for a long time, and Chemitiganti said that it is an interesting industry to be in, noting the Hadoop and Big Data movement. He told Vellante,
“Banking being in the forefront with the predictive analytics and real-time decisions, as well as the historical space with rest data aggregation and anti-money laundering, it’s really exciting times.”
Data defense vs. offense
As Big Data evolves, Chemitiganti said that banks are using data in the defensive dimensions. The industry is trying to assess risk in different areas, from issues with product offerings they offer to risks in a tumultuous financial market.
He also noted that data is useful for a liquidator in terms of the web of connective financial institutions. According to Chemitiganti, banks are on the offensive as well, using the data and real-time interactions to drive more of a responsive experience to consumers to transform their business.
Stuck on data janitorial work
Where is the banking industry in terms of predictive analytics? Chemitiganti explained, “We are at 20 to 30 percent of the journey to Nirvana, which would essentially be to plug in predictive models that do clustering, segmentation, classification and also maybe deep learning at scale.”
He noted that the challenges lie in the large portion of the process being spent doing “data janitorial work” by data scientists and that productive work should be creating models and deploying them, detecting credit card fraud, anti-money laundering projects and things that result in real business value.
Banks are using Hadoop and Big Data to solve many problems. Chemitiganti would like to see banks extend the use of Hadoop and said they are only limited by their imagination.
@theCUBE @Hortonworks @RapidMiner, Inc. @SiliconANGLE theCUBE
#RMWisdom16
