>> Announcer: Live from Las Vegas, it's The Cube, covering Interconnect 2017, brought to you by IBM. >> Okay, welcome back everyone. Here, live in Las Vegas for IBM Interconnect 2017, this is SiliconANGLE's The Cube's coverage. Three days, a lot of great interviews, more in day two here. I'm John Furrier, my co-host Dave Vellante, our next guest is Tanya Seajay, founder and CEO of Orenda Software Solutions. Welcome to The Cube. >> Thank you so much. >> So, your company does a lot of cool things with data. One of the things, obviously, in the news, you can't read a story these days without hearing something about Trump, Uber, bad behavior. >> Dave: Fake news. >> Fake news, there's always scandal. It's the internet, for crying out loud. Everything's going on, but reputation now is measurable and data is out there and companies now as they go on to digital as a medium end to end for marketing and engaging customers, they got to be careful. What's your take? What's going on in this marketplace? >> There's a couple of things that are happening simultaneously. One is, we talked about this just briefly, the Edelman Trust Barometer. It's a global survey that's done every year, and it started I believe in 2010. In 2017, the findings were that we are in a trust crisis globally, and you would have heard that from Marc with Salesforce today. That's what he was referencing. At the same time, PricewaterhouseCooper came out with another survey across North America, and it was that we are in the midst of a trust economy and trust is growing. So, at one point, we used to make our buying decisions on whether or not a product was convenient or a good sale price, those kinds of things. Now, we want to know whether or not we trust the brand, whether or not we trust the CEO, and whether or not the companies have purpose. So, our buying decisions are changing, so not only are we in a trust crisis but we are also a trust economy. So, measuring trust is exceptionally important and a value to all brands globally. >> This purpose thing is interesting. We've been seeing the same thing, and we just had South by Southwest, Intel. We were headlining the Intel AI Lounge, and they had this program, AI for Social Good, which has got a great program. It's on our YouTube channel, youtube.com/siliconangle, folks that are watching, but there's a counterculture going on right now, we're seeing in this world. The younger audience is coming in, the new generation, the digital natives. They're living in a digital world 100%, so there seems to be a counterculture of anti-what it was, pre-now, internet, what it was before, trolling, all this stuff's been around for a while. But you're starting to see people really focus in on good and mission purpose. There's an element where there's a new generation saying, we want to apply tech for good, and you're seeing it with equality, they mentioned a lot of things on the stage today. But beyond that, it's kind of this post-9/11 generation where, like, hey what are you, all you old people bickering about? Just do social good. I mean, do you seeing that too? We're seeing a lot of it across the board. Can you share any stories in this area? >> Yeah, social good is really important in terms of giving back to your community, and in the communities where you do business, you want to have that connection. So, when we were creating Orenda, the software that measures trust, it also measures a few other things. We went back into about 30 years of research in social science and selected, there are six key factors to a healthy relationship, and what we were calling corporate social responsibility is now just more or less social good. So, you want to do things that are good to the communities that you do business in, and there's also the exchange of benefits. I do something for you, you do something for me, which brings in the more collaborative systems and partnership ecosystems that exist. >> It's a community model, too. With open source growing, connected internet, everyone's connected to each other. That's a community framework. >> That's right. >> And that's kind of the, seems to be the trend. >> It is a trend, and at one point, companies used to market to their customers. Now, you see something quite different. Customers are empowered and they're engaging through content so the exchange is continuous. One of the examples we have is with Apple. So, every time your heart beats, someone is talking about Apple. It is so huge. >> The velocity, you mean the velocity. >> Yeah, just the velocity. There's so much information coming out. We were following 25 different companies in December, and we pulled in five million data points. So, that's the amount of information that is coming at us and at brands at any particular time. What we need to do was turn that into insights in real time. If not, it's useless. >> It's interesting you mention Apple. So, we have a data science group within SiliconANGLE, The Cube. We call it our cognitive beta program. We haven't released it yet, but we're looking at all the Twitter data and we can actually see all the tweets. And then, we can extrapolate the users and obviously get all the data, which phone they're using, tweeting from. And that came out, you saw Trump was on an Android, an iPhone. And here at this show, based on the data that we have, 76% of this audience, here and online, is iPhone over Android. So, you say, okay, big deal, ho-hum. Actually, demographically, it matters. Now, some shows, the more geeky shows, you'll see Android over iPhone, so it's a small little data point. But you can almost, like that movie Contact, where you open up one door, you can get all those different insights. So, a small data point like that could add to other data. >> It could, and it's unlocking it, like you said, that is the most important part. You can get all this data. You can get it continuously. But unlocking it and telling everybody what it means to them, and it can mean something different depending on what kind of solution or problem that you're trying to overcome. But yeah. >> Yeah, and the other concepts we follow a lot in the big data world is data at rest and data in motion. And Dave and I were just at breakfast this morning, talking about content and motion brands and motion. So, your company really is measuring the brand in motion, right? >> That's right. >> So, this is kind of a cool new cutting edge coolness. >> It's really cool. >> Explain what's going on there. What's the cutting edge tech? What are some stories? Good, bad, and the ugly? >> One of the interesting things that we just did is we were following five of the biggest banks in Canada, and at the same time, CBC, which is the national broadcasting company, did this go public article and it was extremely negative. And we were tracking them, so we were able to show in real time the trust levels dropping. And in correlation to that, we looked at the stock prices of those companies, and they were also dropping. So, to be able to demonstrate that the brand itself, the reputation, particularly trust, was what the issue was, and that makes a lot of sense. It's money, it's banks, it's trust. That's what's going to be impacted the most. But being able to correlate that, it's a piece of information that we haven't been able to use before. >> So, that's insight. So now, the actionable insight is, wow we should send someone in there digitally, parachute into the virtual news cycle, and provide content or perspective. I'm saying, they can get in, stop that bleeding. >> Get in and stop the bleeding. And the other thing is that they were five national banks, but only one of them was taking the hit for it. They were the actual face of the issue. So, to be able to say, we're all being hit by this particular news story, yes, but you're being hit the most. >> It's a classic public relations problem. If you don't react, then it gets settled in, it becomes a matter of fact. >> Yeah, so you need to be able to deal with that escalation in real time. >> So, what do you guys do that's different than, a lot of sentiment analysis and it's kind of an overcrowded space. >> Tanya: It's a busy space, yeah. >> What's unique in what you guys do? >> What's unique is the actual social science on top of that. So, there is positive, negative, which gives you a little bit of information. What we did is just put on a whole other filter, and we use social science to do that. So, in order to show the brand momentum that needs to exist for a more resilient company, we said we need to know whether or not trust is increasing or decreasing, commitment with the brand or loyalty to the brand is increasing or decreasing. This is really important information. Positive, negative just doesn't tell you enough. So, when you are doing your messaging from a public relations point, you know to talk about integrity if there's a trust issue that you're dealing with. If it's satisfaction, then it's something that you want to do better in terms of a particular product. So, you get to focus on what the actual problem is, so that's how we're absolutely unique, is that we're able to measure emotion in a very different way, through social science and key factors that need to exist for a healthy brand. >> And the secret sauce behind the tech is what? Is it some cognitive, it's data science? >> We do a couple of things. So, one of the reasons why we partnered with IBM and are using Watson, the APIs, is that we built our own algorithms and we have it interact with a huge dictionary of words that we use. And we had to be able to customize that because the way we use language is always different. The way we talk about oil gas is different than we would talk about Coca-Cola, say. So, we had to be able to customize the dictionary so that if we use the word recall with a car manufacturer, that's extremely negative. But recall within the healthcare system is probably neutral. So, we had to be able to make those differences. So then, we also use AI. We use the Personality Insights tool within Watson, so we can take a whole customer buying group, look at them as an individual's huge amounts of data, millions and millions of data points, and say this is what this particular customer group or stakeholder group, this is what they need as a group, this is what they value, these are their key personalities. So again, you just get that deeper insight into who's buying your product. >> And the data sources? Talk about where the data comes from. >> The data comes from social media, and why that's really important is because within public relations and communications, there's always been focus groups, right, where you try to pull out insights into our brand from focus groups or surveys. >> Weeks and weeks and weeks of research. >> Right? Weeks and weeks of research. And you still have just a certain amount of data that you get to deal with. This, we treat social media as a huge focus group with tremendous amounts of data, tremendous amounts of insights, and we can pull it out in real time. So, if there's an issue that is escalating, we can say this is what your customer base is saying about you, this is how the impact is. We don't have to go through months of research to deal with an issue we need to deal with within 10 minutes, usually. >> So, Twitter's obviously a huge source of data, is that correct? >> Twitter's huge. >> 'Cause it's so real time and there's so much of it. What other sources? Is that the primary or a primary source? >> Facebook is interesting. You can get public information, but you can't private. Instagram is another. Blogs are a great source of information as well. Almost any online information where there's engagement, so there's a conversation that's taking place. If it's static, it doesn't, it doesn't really have an impact on you, right? >> Is there third party data sources that you use that other people use as well? Is it Twitter Firehose? Is it RSS feeds? Is there like a syndicate of data sources? >> We use GNIP, so that's owned by Twitter. Yeah, that's what we use. >> But for blogs, how do they get the blogs? >> You scrape them. >> So you scrape them. So RSS feeds and. >> Yeah, and I really enjoy the fact that a lot of governments are going into open source data, so the more we get, the better it is. We have a couple of relationships, partnerships with national media sources as well, so we're able to use that and go back into time, thankfully, from their end. >> Tanya, what's the coolest or weirdest discovery you've made with the data? Because as you get all this gesture data, I'm sure there's some things that just, whoa. >> One of the funnest for me, I'm a bit of a political nerd, and so I really, really enjoy politics. And when we were building out Orenda, we used the federal election in Canada, and yes we did do some with the US election too, but it was so much data, it was. (John and Dave laugh) >> John: Big tsunami. >> Yeah, thanks a lot, John. >> It's not stopping by the way either. It's continuing to go on. >> But yeah, the funniest moment, that one, just as an aside, was the whole, would you rather have Trump or a mozza stick as president, which was, really gained popularity. But for the federal election, what we did was follow the four federal candidates, and we were able to show when we stopped as a nation talking about Thomas Mulcair as the next leader and when we started talking about Justin Trudeau. And we were able to predict that Justin Trudeau's brand was building momentum, weeks before the polls came out and said that the machine changed. >> This year's contender. Alright. Well, Tanya, thanks so much for coming on The Cube. Really appreciate it. I love what you guys do. I think that's, you're on the cutting edge of really compelling social science, and as the culture deals with autonomous driving cars and smart cities, I think this is going to be an ongoing field of study of understanding the relationship between data and humans with respect to societal changes. So, again, this is I think one small use case of really an exploding area. So, thanks for sharing. It's The Cube here live in Las Vegas. For more Interconnect coverage, after this short break, I'm John Furrier with Dave Vellante. We'll be right back. Stay with us.