 Silicone angles, flagship production where we go out to the events, we extract the signal from the noise, we're live at the MIT campus in Cambridge, Massachusetts, we're at the MIT chief data officer information quality symposium. You got big data, big deal. These guys have been dealing with big data for years, dealing with data problems, governance issues, trying to figure out is there a single version of the truth, what does that look like, how do we bring all our disparate data systems together. That's what this conference is about, this conference has been going on for a number of years, well before the big data meme hit the Silicon Valley and of course now the East Coast areas as well. So we're here with the Cube, I'm here with my co-host Jeff Kelly and we're going to be covering two days wall to wall coverage, talking to practitioners, consultants, business leaders, CIOs, technologists, people in the Cambridge area, people that are flying in from all over the country, a lot of people from government, a lot of people from healthcare, a lot of people from financial services, those people who care about data quality and understanding how this rush, this sea of big data, the massive big data, the variety of big data, how they can deal with data governance and data quality issues. How to take real hard problems and bring practical answers to solving these data governance and data quality issues. A lot of people feel as though big data really doesn't need data quality because the actual volume and algorithms will allow you to infer insights from all those masses of data. But the practitioners here that we've talked to have a different philosophy, they're down in the trenches and they're really trying to do a good job of balancing the business need to grow, to find new data sources with the reality of you need some version of the truth that can be reliable. And Jeff Kelly, my co-host is here, Jeff, we've been following this now for quite a long time. We've sort of put this topic of data quality and data governance in the boring but important category. And increasingly it's becoming important as people actually start to implement big data technologies. Two years ago it was all about what is Hadoop, what is big data. Now as enterprises really start to bring these new web scale technologies in and the volumes of data are increasing to massive levels, data quality and data governance is starting to come to the boardroom. What's your take on all this? Absolutely, data quality is critical in big data scenarios just as it was in more traditional data scenarios. But there are some differences and some complexity and some nuance that's added when you start talking about quote unquote big data. When you've got unstructured, semi-structured data, questions like what exactly does data governance look like is a question. In terms of understanding and integrating data sources, maybe you've got traditional data sources, you've got big data sources, you need to integrate those in order to really find insights. So you still need a level of data qualities, especially when you're talking about supporting applications that are really mission critical that touch customers or even internal stakeholders. You still got to have a level of quality to understand and to make sure your view of the customer is accurate. So without question it's relevant in the big data world. So I think for a few years it's been, big data has been focused on kind of the analytic question and those are important in the kind of the sexy part of big data. But ultimately if your data is bad, the analytics aren't going to provide you much insight. So some of the things are going to be unpacking today on theCUBE. Really what are the criteria of big data quality and information quality? How do you know you have information quality? What's the business case toward information quality? Information quality, we heard Dat Tran today talk about information quality and data governance issues are not a project. They're an ongoing initiative. It's an organizational culture. We hear a lot about data driven cultures. Today we're going to hear a lot and today and tomorrow about data quality driven cultures. But how do you justify that to your senior management? How do you go to senior management and say look there's not a beginning or an end to this project, but we have to do this because it's going to improve the business. How do you justify that? So we're going to unpack that. What are some of the use cases? What's the minimum level of data quality and information quality that's acceptable in various use cases in various industries? These are some of the things that we're going to be covering today. And Jeff, what's some of the things you're going to look for over the next two days? Well, I think you just hit on one. Understanding what does data quality look like in big data scenarios? And as you mentioned, that varies by use case. So it will be interesting to get the take of, we're going to have some healthcare practitioners on, some folks from the financial services industry, the Chief Data Officer Derek Strauss from TD Ameritrade will be on, for example. So we're going to talk to practitioners in a few different industries to try to understand what big data quality means for them and in their particular use cases. Because as you said, depending on particularly on how you're trying to use the data and the insights you're trying to glean, the level of data quality that is required could vary. We're also going to look into the organizational issues. What is a Chief Data Officer? Should you have a Chief Data Officer? Where should that Chief Data Officer sit in the organization? Who are the other stakeholders that the Chief Data Officer needs to interact with? Not only just IT, but other stakeholders in the organization. So there are a number of organizational issues that we're going to be unpacking today. Again, this is theCUBE, Silicon Angles flagship production. We are here at MIT, we're live. I'm Dave Vellante, he's Jeff Kelly. We are coming right back with our first guest, the keynote speaker today is Dat Tran. He's the Deputy Assistant Secretary for Data Governance Analysis at the Department of Veterans Affairs. The number two largest department in the government, biggest agency behind the DOD. He's got an awesome perspective. He just gave a great keynote, very transparent on some of the challenges that they're facing and how the VA is applying those challenges and how they apply to enterprises, not only in government, but also in commercial. So keep it right there. We're right back. This is Dave Vellante with Jeff Kelly. This is theCUBE, right back.