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Why Vantara? Hear it from the Source: Brian Householder

Author: Hu Yoshida, Chief Technology Officer, Hitachi Vantara

Brian Householder, Hitachi Vantara’s President and COO sat down with Dave Vellante of the CUBE at Pentaho World to answer questions about Vantara and our operating model. Prior to his current role, Brian held a similar role at Hitachi Data Systems (HDS), which integrated Hitachi Data Systems with the Pentaho organization and Hitachi Insight with our IoT assets to form Hitachi Vantara. Brian joined Hitachi Data Systems in 2003 and has held multiple positions of responsibility in areas such as strategic planning, worldwide marketing, business development, partner enablement, acquisitions, and as COO. Householder helped lead many key initiatives at HDS, including the company strategy and direction, BlueArc, Shoden, Archivas and Pentaho acquisitions, and improvements to our marketing and partner enablement capabilities. Brian is the key executive in Hitachi Vantara who has been most influential in defining Hitachi Vantara, providing a bridge from where we were to what we are now and what we hope to be.

Brian opened up the interview with a simple explanation of what Hitachi Vantara is. We are the data arm for Hitachi. The mission of Vantara is to help our customers deliver edge to outcomes, where ever our customer’s data happens to be created, whatever environment it happens to be in, we can help customers deliver the outcomes that they need for their business.

Next, he explained how Hitachi Vantara was chosen as the name of this new company. Hitachi is always front and centre. Vantara is a suggestive name, suggesting the advantage we can provide customers with their data, providing a vantage point that helps customers see across their environment, and V which gives a nod to our virtualization heritage. Vantara gives us the opportunity to teach customers what we really can do, which is much broader than infrastructure.

Dave Vellante asked about our focus on Pentaho, open source and software and how we plan to go to market. Brian said that our strategy is all about being open, having a customer use what technology they want is very critical for us. We know that customers want to go to open source and we want them to embrace us. We want to foster the open source developer community and add value beyond what is happening in the open source community. While the open source contributions are amazing, when you talk about the ability to scale that is where we can add value and that is where the commercial piece comes into play.

Brian emphasized that we don’t want to own the customer’s data. Our content and analytic platforms provide the customers with the ability to control their data, no matter where it resides. There are other companies out there who would like to own your data even though they may not say so. We will partner and work with customers to build models to solve problems. The knowledge of how to solve problems is shared but the algorithms that the customers develop is their data.

Dave asked about our edge to outcomes strategy, How will the edge evolve and how can Hitachi Vantara add value? Brian recognizes that there is this pendulum swing from centralized mainframes to distributed open system to centralised cloud and now to distributed edge with IoT. That is a direction that we are on now, and we need to be. The biggest thing we look for is follow the data.  Wherever the data is created, is where some of the processing will have to occur. Processing and analytics will need to be done on the edge depending on what the real-time requirements are or what you are asking the edge to do. Then you begin matching that in terms of bringing those data points into the broader ecosystem of what is happening. Instead of bringing the data to the analytics, we believe we need to bring the analytics to the data where it is created. There needs to be a multi-tier model, where you have an edge, then one or more aggregation points, and then an overall aggregation and analytics platform be it private or public cloud, depending on how much data is out there, what you are looking to do, and how quickly you need to get the data into a deep learning model.
Attached is a 15 minute Youtube video of Brian’s interview with Dave Vellante. It is well worth watching to hear from the source.

 
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