>> Narrator: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello everyone, welcome to this special CUBE interview. We are here at theCUBE Virtual covering AWS Summit Virtual Online. This is Amazon's Summits that they normally do all around the world. They're doing them now virtually. We are here in the Palo Alto COVID-19 quarantine crew getting all the interviews here with a special guest, Vice President of Machine Learning, we have Swami, CUBE Alumni, who's been involved in not only the machine learning, but all of the major activity around AWS around how machine learning's evolved, and all the services around machine learning workflows from transcribe, recognition, you name it. Swami, you've been at the helm for many years, and we've also chatted about that before. Welcome to the virtual CUBE covering AWS Summit. >> Hey, pleasure to be here, John. >> Great to see you. I know times are tough. Everything okay at Amazon? You guys are certainly cloud scaled, not too unfamiliar of working remotely. You do a lot of travel, but what's it like now for you guys right now? >> We're actually doing well. We have been I mean, this many of, we are working hard to make sure we continue to serve our customers. Even from their site, we have done, yeah, we had taken measures to prepare, and we are confident that we will be able to meet customer demands per capacity during this time. So we're also helping customers to react quickly and nimbly, current challenges, yeah. Various examples from amazing startups working in this area to reorganize themselves to serve customer. We can talk about that common layer. >> Large scale, you guys have done a great job and fun watching and chronicling the journey of AWS, as it now goes to a whole 'nother level with the post pandemic were expecting even more surge in everything from VPNs, workspaces, you name it, and all these workloads are going to be under a lot of pressure to do more and more value. You've been at the heart of one of the key areas, which is the tooling, and the scale around machine learning workflows. And this is where customers are really trying to figure out what are the adequate tools? How do my teams effectively deploy machine learning? Because now, more than ever, the data is going to start flowing in as virtualization, if you will, of life, is happening. We're going to be in a hybrid world with life. We're going to be online most of the time. And I think COVID-19 has proven that this new trajectory of virtualization, virtual work, applications are going to have to flex, and adjust, and scale, and be reinvented. This is a key thing. What's going on with machine learning, what's new? Tell us what are you guys doing right now. >> Yeah, I see now, in AWS, we offer broadest-- (poor audio capture obscures speech) All the way from like expert practitioners, we offer our frameworks and infrastructure layer support for all popular frameworks from like TensorFlow, Apache MXNet, and PyTorch, PowerShell, (poor audio capture obscures speech) custom chips like inference share. And then, for aspiring ML developers, who want to build their own custom machine learning models, we're actually building, we offer SageMaker, which is our end-to-end machine learning service that makes it easy for customers to be able to build, train, tune, and debug machine learning models, and it is one of our fastest growing machine learning services, and many startups and enterprises are starting to standardize their machine learning building on it. And then, the final tier is geared towards actually application developers, who did not want to go into model-building, just want an easy API to build capabilities to transcribe, run voice recognition, and so forth. And I wanted to talk about one of the new capabilities we are about to launch, enterprise search called Kendra, and-- >> So actually, so just from a news standpoint, that's GA now, that's being announced at the Summit. >> Yeah. >> That was a big hit at re:Invent, Kendra. >> Yeah. >> A lot of buzz! It's available. >> Yep, so I'm excited to say that Kendra is our new machine learning powered, highly accurate enterprise search service that has been made generally available. And if you look at what Kendra is, we have actually reimagined the traditional enterprise search service, which has historically been an underserved market segment, so to speak. If you look at it, on the public search, on the web search front, it is a relatively well-served area, whereas the enterprise search has been an area where data in enterprise, there are a huge amount of data silos, that is spread in file systems, SharePoint, or Salesforce, or various other areas. And deploying a traditional search index has always that even simple persons like when there's an ID desk open or when what is the security policy, or so forth. These kind of things have been historically, people have to find within an enterprise, let alone if I'm actually in a material science company or so forth like what 3M was trying to do. Enable collaboration of researchers spread across the world, to search their experiment archives and so forth. It has been super hard for them to be able to things, and this is one of those areas where Kendra has enabled the new, of course, where Kendra is a deep learning powered search service for enterprises, which breaks down data silos, and collects actually data across various things all the way from S3, or file system, or SharePoint, and various other data sources, and uses state-of-art NLP techniques to be able to actually index them, and then, you can query using natural language queries such as like when there's my ID desk-scoping, and the answer, it won't just give you a bunch of random, right? It'll tell you it opens at 8:30 a.m. in the morning. >> Yeah. >> Or what is the credit card cashback returns for my corporate credit card? It won't give you like a long list of links related to it. Instead it'll give you answer to be 2%. So it's that much highly accurate. (poor audio capture obscures speech) >> People who have been in the enterprise search or data business know how hard this is. And it is super, it's been a super hard problem, the old in the old guard models because databases were limiting to schemas and whatnot. Now, you have a data-driven world, and this becomes interesting. I think the big takeaway I took away from Kendra was not only the new kind of discovery navigation that's possible, in terms of low latency, getting relevant content, but it's really the under-the-covers impact, and I think I'd like to get your perspective on this because this has been an active conversation inside the community, in cloud scale, which is data silos have been a problem. People have had built these data silos, and they really talk about breaking them down but it's really again hard, there's legacy problems, and well, applications that are tied to them. How do I break my silos down? Or how do I leverage either silos? So I think you guys really solve a problem here around data silos and scale. >> Yeah. >> So talk about the data silos. And then, I'm going to follow up and get your take on the kind of size of of data, megabytes, petabytes, I mean, talk about data silos, and the scale behind it. >> Perfect, so if you look at actually how to set up something like a Kendra search cluster, even as simple as from your Management Console in the AWS, you'll be able to point Kendra to various data sources, such as Amazon S3, or SharePoint, and Salesforce, and various others. And say, these are kind of data I want to index. And Kendra automatically pulls in this data, index these using its deep learning and NLP models, and then, automatically builds a corpus. Then, I, as in user of the search index, can actually start querying it using natural language, and don't have to worry where it comes from, and Kendra takes care of things like access control, and it uses finely-tuned machine learning algorithms under the hood to understand the context of natural language query and return the most relevant. I'll give a real-world example of some of the field customers who are using Kendra. For instance, if you take a look at 3M, 3M is using Kendra to support search, support its material science R&D by enabling natural language search of their expansive repositories of past research documents that may be relevant to a new product. Imagine what this does to a company like 3M. Instead of researchers who are spread around the world, repeating the same experiments on material research over and over again, now, their engineers and researchers will allow everybody to quickly search through documents. And they can innovate faster instead of trying to literally reinvent the wheel all the time. So it is better acceleration to the market. Even we are in this situation, one of the interesting work that you might be interested in is the Semantic Scholar team at Allen Institute for AI, recently opened up what is a repository of scientific research called COVID-19 Open Research Dataset. These are expert research articles. (poor audio capture obscures speech) And now, the index is using Kendra, and it helps scientists, academics, and technologists to quickly find information in a sea of scientific literature. So you can even ask questions like, "Hey, how different is convalescent plasma "treatment compared to a vaccine?" And various in that question and Kendra automatically understand the context, and gets the summary answer to these questions for the customers, so. And this is one of the things where when we talk about breaking the data silos, it takes care of getting back the data, and putting it in a central location. Understanding the context behind each of these documents, and then, being able to also then, quickly answer the queries of customers using simple query natural language as well. >> So what's the scale? Talk about the scale behind this. What's the scale numbers? What are you guys seeing? I see you guys always do a good job, I've run a great announcement, and then following up with general availability, which means I know you've got some customers using it. What are we talking about in terms of scales? Petabytes, can you give some insight into the kind of data scale you're talking about here? >> So the nice thing about Kendra is it is easily linearly scalable. So I, as a developer, I can keep adding more and more data, and that is it linearly scales to whatever scale our customers want. So and that is one of the underpinnings of Kendra search engine. So this is where even if you see like customers like PricewaterhouseCoopers is using Kendra to power its regulatory application to help customers search through regulatory information quickly and easily. So instead of sifting through hundreds of pages of documents manually to answer certain questions, now, Kendra allows them to answer natural language question. I'll give another example, which is speaks to the scale. One is Baker Tilly, a leading advisory, tax, and assurance firm, is using Kendra to index documents. Compared to a traditional SharePoint-based full-text search, now, they are using Kendra to quickly search product manuals and so forth. And they're able to get answers up to 10x faster. Look at that kind of impact what Kendra has, being able to index vast amount of data, with in a linearly scalable fashion, keep adding in the order of terabytes, and keep going, and being able to search 10x faster than traditional, I mean traditional keyword search based algorithm is actually a big deal for these customers. They're very excited. >> So what is the main problem that you're solving with Kendra? What's the use case? If I'm the customer, what's my problem that you're solving? Is it just response to data, whether it's a call center, or support, or is it an app? I mean, what's the main focus that you guys came out? What was the vector of problem that you're solving here? >> So when we talked to customers before we started building Kendra, one of the things that constantly came back for us was that they wanted the same ease of use and the ability to search the world wide web, and customers like us to search within an enterprise. So it can be in the form of like an internal search to search within like the HR documents or internal wiki pages and so forth, or it can be to search like internal technical documentation or the public documentation to help the contact centers or is it the external search in terms of customer support and so forth, or to enable collaboration by sharing knowledge base and so forth. So each of these is really dissected. Why is this a problem? Why is it not being solved by traditional search techniques? One of the things that became obvious was that unlike the external world where the web pages are linked that easily with very well-defined structure, internal world is very messy within an enterprise. The documents are put in a SharePoint, or in a file system, or in a storage service like S3, or on naturally, tell-stores or Box, or various other things. And what really customers wanted was a system which knows how to actually pull the data from various these data silos, still understand the access control behind this, and enforce them in the search. And then, understand the real data behind it, and not just do simple keyword search, so that we can build remarkable search service that really answers queries in a natural language. And this has been the theme, premise of Kendra, and this is what had started to resonate with our customers. I talked with some of the other examples even in areas like contact centers. For instance, Magellan Health is using Kendra for its contact centers. So they are able to seamlessly tie like member, provider, or client specific information with other inside information about health care to its agents so that they can quickly resolve the call. Or it can be on internally to do things like external search as well. So very satisfied client. >> So you guys took the basic concept of discovery navigation, which is the consumer web, find what you're looking for as fast as possible, but also took advantage of building intelligence around understanding all the nuances and configuration, schemas, access, under the covers and allowing things to be discovered in a new way. So you basically makes data be discoverable, and then, provide an interface. >> Yeah. >> For discovery and navigation. So it's a broad use cat, then. >> Right, yeah that's sounds somewhat right except we did one thing more. We actually understood not just, we didn't just do discovery and also made it easy for people to find the information but they are sifting through like terabytes or hundreds of terabytes of internal documentation. Sometimes, one other things that happens is throwing a bunch of hundreds of links to these documents is not good enough. For instance, if I'm actually trying to find out for instance, what is the ALS marker in an health care setting, and for a particular research project, then, I don't want to actually sift through like thousands of links. Instead, I want to be able to correctly pinpoint which document contains answer to it. So that is the final element, which is to really understand the context behind each and every document using natural language processing techniques so that you not only find discover the information that is relevant but you also get like highly accurate possible precise answers to some of your questions. >> Well, that's great stuff, big fan. I was really liking the announcement of Kendra. Congratulations on the GA of that. We'll make some room on our CUBE Virtual site for your team to put more Kendra information up. I think it's fascinating. I think that's going to be the beginning of how the world changes, where this, this certainly with the voice activation and API-based applications integrating this in. I just see a ton of activity that this is going to have a lot of headroom. So appreciate that. The other thing I want to get to while I have you here is the news around the augmented artificial intelligence has been brought out as well. >> Yeah. >> So the GA of that is out. You guys are GA-ing everything, which is right on track with your cadence of AWS laws, I'd say. What is this about? Give us the headline story. What's the main thing to pay attention to of the GA? What have you learned? What's the learning curve, what's the results? >> So augmented artificial intelligence service, I called it A2I but Amazon A2I service, we made it generally available. And it is a very unique service that makes it easy for developers to augment human intelligence with machine learning predictions. And this is historically, has been a very challenging problem. We look at, so let me take a step back and explain the general idea behind it. You look at any developer building a machine learning application, there are use cases where even actually in 99% accuracy in machine learning is not going to be good enough to directly use that result as the response to back to the customer. Instead, you want to be able to augment that with human intelligence to make sure, hey, if my machine learning model is returning, saying hey, my confidence interval for this prediction is less than 70%, I would like it to be augmented with human intelligence. Then, A2I makes it super easy for customers to be, developers to use actually, a human reviewer workflow that comes in between. So then, I can actually send it either to the public pool using Mechanical Turk, where we have more than 500,000 Turkers, or I can use a private workflow as a vendor workflow. So now, A2I seamlessly integrates with our Textract, Rekognition, or SageMaker custom models. So now, for instance, NHS is integrated A2I with Textract, so that, and they are building these document processing workflows. The areas where the machine learning model confidence load is not as high, they will be able augment that with their human reviewer workflows so that they can actually build in highly accurate document processing workflow as well. So this, we think is a powerful capability. >> So this really kind of gets to what I've been feeling in some of the stuff we worked with you guys on our machine learning piece. It's hard for companies to hire machine learning people. This has been a real challenge. So I like this idea of human augmentation because humans and machines have to have that relationship, and if you build good abstraction layers, and you abstract away the complexity, which is what you guys do, and that's the vision of cloud, then, you're going to need to have that relationship solidified. So at what point do you think we're going to be ready for theCUBE team, or any customer that doesn't have the or can't find a machine learning person? Or may not want to pay the wages that's required? I mean it's hard to find a machine learning engineer, and when does the data science piece come in with visualization, the spectrum of pure computer science, math, machine learning guru to full end user productivity? Machine learning is where you guys are doing a lot of work. Can you just share your opinion on that evolution of where we are on that? Because people want to get to the point where they don't have to hire machine learning folks. >> Yeah. >> And have that kind support too. >> If you look at the history of technology, I actually always believe that many of these highly disruptive technology started as a way that it is available only to experts, and then, they quickly go through the cycles, where it becomes almost common place. I'll give an example with something totally outside the IT space. Let's take photography. I think more than probably 150 years ago, the first professional camera was invented, and built like three to four years still actually take a really good picture. And there were only very few expert photographers in the world. And then, fast forward to time where we are now, now, even my five-year-old daughter takes actually very good portraits, and actually gives it as a gift to her mom for Mother's Day. So now, if you look at Instagram, everyone is a professional photographer. I kind of think the same thing is about to, it will happen in machine learning too. Compared to 2012, where there were very few deep learning experts, who can really build these amazing applications, now, we are starting to see like tens of thousands of actually customers using machine learning in production in AWS, not just proof of concepts but in production. And this number is rapidly growing. I'll give one example. Internally, if you see Amazon, to aid our entire company to transform and make machine learning as a natural part of the business, six years ago, we started a Machine Learning University. And since then, we have been training all our engineers to take machine learning courses in this ML University, and a year ago, we actually made these coursework available through our Training and Certification platform in AWS, and within 48 hours, more than 100,000 people registered. Think about it, that's like a big all-time record. That's why I always like to believe that developers are always eager to learn, they're very hungry to pick up new technology, and I wouldn't be surprised if four or five years from now, machine learning is kind of becomes a normal feature of the app, the same with databases are, and that becomes less special. If that day happens, then, I would see it as my job is done, so. >> Well, you've got a lot more work to do because I know from the conversations I've been having around this COVID-19 pandemic is it's that there's general consensus and validation that the future got pulled forward, and what used to be an inside industry conversation that we used to have around machine learning and some of the visions that you're talking about has been accelerated on the pace of the new cloud scale, but now that people now recognize that virtual and experiencing it firsthand globally, everyone, there are now going to be an acceleration of applications. So we believe there's going to be a Cambrian explosion of new applications that got to reimagine and reinvent some of the plumbing or abstractions in cloud to deliver new experiences, because the expectations have changed. And I think one of the things we're seeing is that machine learning combined with cloud scale will create a whole new trajectory of a Cambrian explosion of applications. So this has kind of been validated. What's your reaction to that? I mean do you see something similar? What are some of the things that you're seeing as we come into this world, this virtualization of our lives, it's every vertical, it's not one vertical anymore that's maybe moving faster. I think everyone sees the impact. They see where the gaps are in this new reality here. What's your thoughts? >> Yeah, if you see the history from machine learning specifically around deep learning, while the technology is really not new, especially because the early deep learning paper was probably written like almost 30 years ago. And why didn't we see deep learning take us sooner? It is because historically, deep learning technologies have been hungry for computer resources, and hungry for like huge amount of data. And then, the abstractions were not easy enough. As you rightfully pointed out that cloud has come in made it super easy to get like access to huge amount of compute and huge amount of data, and you can literally pay by the hour or by the minute. And with new tools being made available to developers like SageMaker and all the AI services, we are talking about now, there is an explosion of options available that are easy to use for developers that we are starting to see, almost like a huge amount of like innovations starting to pop up. And unlike traditional disruptive technologies, which you usually see crashing in like one or two industry segments, and then, it crosses the chasm, and then goes mainstream, but machine learning, we are starting to see traction almost in like every industry segment, all the way from like in financial sector, where fintech companies like Intuit is using it to forecast its call center volume and then, personalization. In the health care sector, companies like Aidoc are using computer vision to assist radiologists. And then, we are seeing in areas like public sector. NASA has partnered with AWS to use machine learning to do anomaly detection, algorithms to detect solar flares in the space. And yeah, examples are plenty. It is because now, machine learning has become such common place that and almost every industry segment and every CIO is actually already looking at how can they reimagine, and reinvent, and make their customer experience better covered by machine learning. In the same way, Amazon actually asked itself, like eight or 10 years ago, so very exciting. >> Well, you guys continue to do the work, and I agree it's not just machine learning by itself, it's the integration and the perfect storm of elements that have come together at this time. Although pretty disastrous, but I think ultimately, it's going to come out, we're going to come out of this on a whole 'nother trajectory. It's going to be creativity will be emerged. You're going to start seeing really those builders thinking, "Okay hey, I got to get out there. "I can deliver, solve the gaps we are exposed. "Solve the problems, "pre-create new expectations, new experience." I think it's going to be great for software developers. I think it's going to change the computer science field, and it's really bringing the lifestyle aspect of things. Applications have to have a recognition of this convergence, this virtualization of life. >> Yeah. >> The applications are going to have to have that. So and remember virtualization helped Amazon formed the cloud. Maybe, we'll get some new kinds of virtualization, Swami. (laughs) Thanks for coming on, really appreciate it. Always great to see you. Thanks for taking the time. >> Okay, great to see you, John, also. Thank you, thanks again. >> We're with Swami, the Vice President of Machine Learning at AWS. Been on before theCUBE Alumni. Really sharing his insights around what we see around this virtualization, this online event at the Amazon Summit, we're covering with the Virtual CUBE. But as we go forward, more important than ever, the data is going to be important, searching it, finding it, and more importantly, having the humans use it building an application. So theCUBE coverage continues, for AWS Summit Virtual Online, I'm John Furrier, thanks for watching. (enlightening music)

from Houston Texas extracting the signal from the noise it's the cute coverage Grace Hopper celebration of women in computing now your host John furrier and Jeff fridge okay welcome back everyone we are here live in Houston Texas for the Grace Hopper celebration of women in computing this is SiliconANGLE media's the cube our flagship program we go out to the events and extract the simla noise i'm john ferry the founder of SiliconANGLE join with Jeff Frick general manager of the cube our next guest is Lisa Dougal who's the chief diversity officer pwc consulting welcome to the cube thank you very much great to see you great to chat with you before we came on we talked about you were at Carnegie Mellon back in the 80s and we just had Eileen big enough for it to it another 80s throwback like me in sheb back to the 80s hot tub time machine whatever you want to call it it's a lot of fun so thanks for spending some time with us oh my pleasure so first what are you working on so that's the first point we've learned that's a good question to ask what are you working on what am i working on so for me personally I do a number of different things right as my role is chief diversity officer I am creating and evolving and implementing programs that help all kinds of diversity in the workplace which ranges from women to minorities to men as well which is one of our big focus areas right as a partner in the practice i'm also a retail consumer partner so I work with retail and consumer clients on transforming their businesses from strategy to execution digital transformations hot right now Adam everything is being automated I mean everything's addressable now Internet of Things creates absolutely % data acquisition it does but I think at the same time it's created such a wealth of I will call it information old school or data its recent project right I think companies are struggling with how do you parse through how do you tell the story how do you figure out a what the data is telling you if you take the consumer industry for one right they've got huge amounts of consumer data now the question is how do you use it how you turn it into innovation one of the things you were mentioning before you came on was that you did a thesis at Carnegie Mellon back in the eighties where you ready to say a computer science major but everyone had the code which great paid back in the 80s and maybe we should reinstitute that across the university I agree I think everything went should coach likes math and sciences to me I think a requisite skill for everybody but you say that these are supposed decision-making using computers now fast forward to today where we were just chatting about for the first time in modern in business history you can actually measure everything so no more excuses if you could actually measure everything right so the question becomes what do you want to measure right yeah so what does that do with a business how does that change and I think it's a combination of measurement which just looks historical and that's important right with predictive and right where the world is going it's predictive analytics behavioral analytics right because that enables us to figure out how we want to change we're only ever looking backwards we had a static point in time yeah and that's informative and you need that and as we talked before you need to be able to parse through the data and decide which is relevant and which is really the lever you want to pull but I think more and more we're seeing companies doing data modeling and data predictive analytics on just about everything right right and Merv Adrian loves to talk about data in motion from gartner and you know it's no longer good enough to have it look at it then decide what you're going to do now really was spark and some of the new technologies you actually have an opportunity to look at the data in motion in a transaction in a retail environment and change change the transaction midstream to hopefully get to a better out absolutely so what you seeing kind of out in the in the world of some of these more advanced retailers and some of the things I think that's happening i think the ability to drop coupons as people walk by the aisle is more and more prevalent right not just any coupon but we know you buy a lot of milk right i think you're going to see more and more price changing based on the consumer i know you you've been into my store you're a loyal customer I'll pop you the milk at this price where somebody else might pay a higher price I think the world is open in terms of how these companies are using not just the data they collect on the product and the technologies but also on you as the individual least I want to get your thoughts on a concept that we've been kind of gleaming out of the data here at Grace Hopper and other events we've been to around women in computing but more importantly also computer science and that there's a lot of different semantics people argue about women versus ladies this versus that there's so many different you know biases mean I'm biased whatever all that stuff's happening but one constant in all this is that these two debt variables transparency and always learning and that seems to be a driver of a lot of change here and you mentioned digital transformation what are you seeing out there that's really driving the opportunities around transparency you can save data access you have data then things are transparent always be learning this new opportunities so those seems to be a big pivot points here at this event here where there's a lot of opportunities there's a subtle conversation of not just the pay thing and the gender equality on pay but opportunities is the big theme we're seeing here absolutely I am really energized by being here right first of all to see so many young women all passionate about technology and computing and really being inserted in the right ways you know I've had women come up to me even on the escalator shake my hand as a hello you're from pricewaterhousecoopers let me ask you what you do during your day right I think in my day a there was no place to go and even if you did you were trying to navigate a very different world and you were trying to perhaps not be you but be somebody else right how do you fit into the man's world I used to watch all sports all weekend so I can make sure I could participate in office conversation when I got in on Monday mornings right I think to hear the conversations that the women are having that are very technology driven but also very much authentic to who they are is where we're going see if you were a young lady in tech now you actually program the fantasy games so that you'd win the game everywhere that's right you could write the code this is but there's a lot of coding a lot of developers here phenomenal growth in develops we just had a young girl just graduated she's phenomenal Natalia and she got into it she started in journalism major and second year in she switched into computer science because she was tinkering with wearables which is terrific right one of the conversations I like to have with our young women about PwC in particular but a lot of parts of the industry the ability to combine industry or sector knowledge with the technology right so I was talking to one women who said well you know I just switched out of pre-med I really like medicine but I got into coding and I simply have you thought about you know the whole arena of the health care industry is dramatically changing right we're moving to the point where we have you know patient information hospital information drug trial information we can integrate all that you could stay with healthcare and still do technology and coding and she's looking at me like she'd never thought about the revelation you said early undulation the old days you try to be someone else try to fit into a man's world but now you're saying you know just the app just follow your passion and this technology behind it interesting enough is also an effect on the men like I had a Facebook post on my flight down here at the Wi-Fi on the plane and i typed in my facebook friends hey real question is a politically incorrect to say I love women in tech I kind of put that out there is kind of a link bait but all sudden the arguments were weighed politically correct love is for versions of love's like argument and wedding Gary deep hey very deep but the one comment was just be yourself and I think I tell our women that all the time and all our people right but i think this the shift to the workplace openness where you can be authentic and i find often are young women in particular get guidance from mentors who are men and they try to emulate that and some of that is good but you have to emulate that while being authentic to who you are otherwise you run that risk of perhaps being perceived in authentic or you know it comes off a little bit too can write what's your best advice to men because one of the things that we seeing is a trend now and certainly is that men inclusion is also into the conversation absolutely big thing we are doing that as a firm both in the US and globally we're a ten-by-ten impact sponsor for he for she which is the UN's initiative with companies governments and not-for-profits to engage men in a conversation about raising awareness around women and for us it's women in the workplace right so there really a couple of things I think men can do one is listen and actively engage with the women and not just women at your level women who are Millennials as well if you can't of not comfortable having that conversation which I know many with women and men both aren't it's hard to put yourself in their shoes right the second is to really be an advocate right think about when you walk into meetings who's not in the room are the people looking all like you what do you do about that right and i think that the third is make it personal you know be involved and know what's going on and know how you could help it seems so simple right when you just lay it out there right those are not complicated concepts but but to put them in practices is you know it takes an active you know kind of thinking about it right to really make it happen to impact change it does and i think more it is natural for people to gravitate to people who are like them particularly in the workspace we get very comfortable in our own let's call them echo chambers and then you move with your echo chamber and your echo chamber might have a little diversity but likely it doesn't have a lot of generational diversity it may or may not have all kinds of racial ethnic gender diversity and so you might meet somebody on the outside who's a little different but you go back to your go tues who are still in your echo chamber so I think the goal is to get into multiple a few echo chambers right also I also comfort zone right i mean people like what's familiar to them and pushing the comfort zone barrier is one issue right now happy young come to be uncomfortable be comfortable and the uncomfortable how is that right what people should look for I mean and everyone has their own struggles and journeys what how did people cope it so I often to have this conversation with methanol how do I talk to women about being women I said well that's probably not the first conversation you should be having right talk to them about who they are and what's important to you and then the relationship you have to build what we call familiarity comfort and trust and once you've built that you can have a conversation perhaps about what a woman's plans are if she's pregnant but you can't just walk in and taught me the for that yeah you can't blurt it out right thank you thanks off at not a walk not a good icebreaker yeah yeah so Lisa you know there's a lot of talk about what's the right thing to do what is right meaning it's the right thing to do in terms of morally and as a human being to include people but really there's there's a bottom line positive impact to there's a better outcome impact and pwc you guys do a lot of analysis you work a lot of companies so there's some studies you can share some some facts or figures that you guys have discovered about how there's really great bottom line better decisions better products better profitability when you have a diverse point of view that you bring to a problem set absolutely there are number of different ways to look at that I think you're right it is the right thing to do the moral thing to do people want to feel good about it but at the end of the day we know that diversity is good for business performance right and there are a number of studies out there that talk about board composition and how you know now bored women on boards has been legislated in enough countries around the world for long enough now you can correlate long-term 10-15 year performance with the performance of those companies and we see that those companies perform better right you can look at just the diversity I mean another angle of looking at it is we do a lot of work with Millennials in the millennial studies right and people coming off a campus are more Geographic gender ethnic minority diverse than any generations we've seen at a very long time right there more women coming off of campus in general than men right now and they're doing very well right so there's also the zero-sum game that says if we don't figure out how to accommodate a track promote retain women then we're not going to be able to get the best of the best of the workforce and you become at a competitive disadvantage well it's quality that's the competitive advantage is the quality that you get with the diversity absolutely how do you manage that process because some would say diversity slows things down because you have different perspectives but the outputs higher quality high equality and more innovation right and one of the things we like to do is talk about diversity and a number of different angles so there's race gender sexual orientation there's also in our business diversity of degrees so we have coders working with mba is working with lawyers doctors strategist and part of that is the way you get the thinking and the most innovative solutions to your problems and I think when you begin to develop and to find it that way there are places for more people to get on the wheel so to speak right everybody is thinking about diversity not just you look different or you experience but you bring a different perspective to the problem because you have a different background where you grow up and what you studied it's just it's just funny that you know in being diverse you're actually leveraging people's biases to get to a better solution absolutely perfect all the way around that's right and i think that there's a movement now and we're really moving from thinking about being equal to thinking about being equitable right equal would say if you have three kids peering over a fence ones four foot ones five-foot 16 foot give them all in one foot box well that's not going to get the forefoot guy over the fence right what you really have to do is give them each a size box that they need right so the six-foot kid probably doesn't need a box at all if it's a five-foot fence right the 5-foot kid might need a little stepstool and the forfeit kid probably needs a large cube right right that's being equitable it's not necessary to me out well based on the outcome based on the album about the objective right versus some statistical equitable correct so I think in business we're moving more to looking at that outcome based heck with biddle equity being equitable across outcomes equitable thank you not just being equal because I think for a long term it was treat everybody the same and that's diversity it's really appreciate everybody for their just as differences and let them play to their strengths right and use the data science tools available Go Daddy put out the survey results of their salaries to you seeing the University of Virginia Professor Brian gave a keynote today about the software that they're building an open source for tooling but the date is going to be key but at the end of the day management drives the outcome objective so I'm Celeste someone at a senior level who's had a good journey from the 80 Eileen big and talk about the same thing you're now at the top of the pyramid the flywheels developing there's some good on in migration with women coming into the field house the balance how's that flywheel working for the mentoring the pipeline in the operational I'd say I give you one example right so we have a women in technology what started as a program it's now a part of our business right we started about two and a half years ago with 30 women who are trying to figure out in technology you give you a long term implementation projects for you know six months a year two years and only operate in the same echo chamber right so how do you network with other women how do you meet them it's now 1400 people strong and one of the pillars of it is a mentorship program we had and it doesn't sound like a lot but see from where you start right increase if we started with needing having about 50 50 women mentored right we're up to hundreds of women being mentored and last time we opened the program we had 150 leaders not just we had other people but leaders sign up within the first few days to mentor the women so in my mind that's success that success reason I didn't need to promise my job good job on your older thank you taking you for that network effect there's an app for that now the network effectors are dynamic now so coming back to the theory of socialization and social theory as you get a network effect going on there's a good social vibe going on talk about that dynamic it's kind of qualitative and then be might be some numbers so save it but talk about that the the network effect of that viral growth if you will I think you sort of have it's now a important and good and rewarded thing to do right but I also think there's a millennial factor there yeah right so what we've been able to see is as our tech women come in off a campus they're beginning to get opportunities that change the game around women in the community right so we brought a number of two-year three-year out women with us and have them help us in the planning of being here all the way from designing our website to putting together the booth to submitting and speaking at so they got speaker slots which gives them amazing exposure with then sentenced that social dynamic in a number of ways right you have them wanting to other people wanting to emulate it you have leaders reaching out to me and say wow we didn't know Emily you know Emily did that that is great right she spoke to 900 people yesterday and so that changes the social landscape acceptable it certainly does it's great amplification so as we wrap here at Lisa I think that's a great segue talk about the Grace Hopper celebration of women in computing it's a very different kind of conference it's a very different kind of feel why is it important to pwc why do you guys invest in this show and you know the example you caves just a great lead into it I think it's for a number of reasons it's a great source of recruits right so so we want to be here we want to meet the young people coming off of campus so maybe we might not meet in our structured campus environment right I think the second is it's a great opportunity for our young women to promote and develop themselves and gain skills that we would never gain I think the third is just to empower our women just like being here and even the emails i'm getting from our women who are not here and our men who are not here the fact that we are here has sort of had a little bit of a viral offensive foam oh you're missing out you're missing out it's an amazing experience it's really helped put in some ways women in technology in a little different league right a lot of the alliances and a lot of the conference's we do are we do 15 major conferences now and we support leadership for women events at all of them but this is one of the few that's not alliance space it's not being at SI p with us AP or being an owl with Oracle which are great things for us to do but this is for the women about the women and the development of the women it's an exciting time and we're excited to document and thanks for spending the time sharing your insights and data and perspective here on the cube well thank you so much John and jeff bennett me having me whereas our pleasure was so inspired so really awesome and if you want to be part of the cube we are hiring looking for women digital scientists data analyst on-air host and we've been shamed a little bit for having an all-male team here I was just gonna ask ya we are looking for powerful strong smart women who want to join the cube we're hiring so contact us offline thanks for watching me right back with more live coverage here in Houston Texas at the Grace Hopper celebration be right back