Hello everyone, my name is Pam and I'm the Executive Editor for DataVersity. We would like to thank you for joining today's webinar, helping HR to cross the big data chasm. As just a quick point sticking up started, due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via the Q&A section in the bottom right-hand corner of your screen. Or if you like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVersity. As always, we will send a follow-up email within two business days containing links to the slides, the recording session, and additional information requested throughout the webinar. Our speaker today, Mario Faria. Mario is a big data advisor at the Bill and Melinda Gates Foundation and member of the MIT Data Science Initiative. He was the first Chief Data Officer for Latin America. He worked as a CDO for Boa Vista, a credit bureau services provided in Brazil, which is partly owned by Equifax. Or he is a Professor of Marketing and Strategy at the MBA program of the University and a contributor of several conferences, magazines, and publications in the area of big analytics, data management, digital marketing, social media, and technology. Prior to Boa Vista, Mario has worked for IBM, Accenture, and Microsoft, leading projects related to BI, DDRM, supply chain, and development and management consulting. Mario has an MBA from the University of California at Santa Cruz, a BSC from the University of New York at Albany, and a BSC from Unicamp, Brazil. Mario has moved to the US in January 2013 and is currently working as a big data business provider helping companies to cross the data calzone. We're very lucky to have him here today. And with that, I will give the floor to Mario to begin the presentation. Mario. Hello, everyone. Thanks for joining in. The purpose of this webinar is to help HR professionals understand what is happening as of today on data and technology and give them some insights and also to the HR managers who have built a successful data analytics organization. I know that your work is not easy, and my goal here is to give them some materials and some points from my experience that I have been passing through at this point, and also with discussions that I am having with other chief data authors on this matter. So I have a personal mission. It is to help the data community worldwide to evolve with sustainability. In addition to that, I do share knowledge. I try to present as much as I can in conferences, write papers. I just came back from Europe in a series of presentations there on the issue of big data, and it's very important for people like me who is like a step in front of everyone to share technology. Because of that, we will grow this area with quality, with a very professional approach, and we will make at the end of the day the technology world, the data world is a better place to work and to live. Okay, why I have chosen this from my presentation, like helping a chart to cross the big data cache. There was an author that on the middle nine, he wrote a book called Crossing the Cache, and his name was Jeffrey Moore. And for me, at the time when I started working on the technology world, that was one of the best books that I had, and it guided me throughout every decision that I have taken so far. And for Jeffrey Moore, in terms of the adoption curve of technology, when it's adopted by the community and the society in general, it goes through several steps. Like people need to pay a high premium price to get access, which are the innovators. And then those innovators influence some early adopters and visionaries, and the chasm. And for that point, you've seen technologies that will be able to cross this chasm, and some technologies that are not able to do that. And at the end of the day, I have to cross that. Big data has reached a mainstream where we see real, real cases and applications. My goal is to help HR and hiring managers help them to cross the chasm here on how they should affect, retain, hire, and sustain their data organizations. So that, of course, wasn't why I have chosen this title. And let me tell you the truth. The data analytics, people and professionals, they are not making more life easier. They are talking very technical terms. They try to make the things harder as they are. And my goal here is to make the things simpler in a sense that we will have a better understanding for you to do your work and at the same time present some questions that you are able to challenge the data analytics professionals. Okay? So if you go into the grid and data details, yeah, of course it is complicated, but it's the important for everyone to bring the conversation at a certain level that everyone can be involved and understand what is needed, what is required to hire those professionals. Okay? So if you are a HR person and you want to learn more about how can hire big data professionals, what do I do for a thing? When you don't know anything, you go to Google. So I typed in hiring big data professionals yesterday. And guess what? I found more than 51 million results. There is no way that a normal individual can learn anything by looking at this huge amount of information. Here we have a big data problem. Okay? So the information that is presented to us is presented in a way that it's very hard for anyone to digest a number of objects. So I decided to go to the other web search engine, which is Bing. So I typed in there, it says, Hiring Big Data Professionals. Guess what? All million results. There is no way that you can do anything with two million results. So when you try to find something that you don't know too much, and then you go to Google, go to big, and you cannot find the right answer, what do you do? Hey, you have a consultant to help you make sense on that. And I'm a consultant. I've been a consultant for my whole time. And even though when I wasn't a consultant working for a company as a Chief Data Officer, I always see myself as a consultant. And when you're a consultant, you're bringing to a lot of problems to make a business have a better sense. And maybe a consultant, I would like to say three things to you. First, see where the market is at this point. Second, what's the complication? Why are you dealing with big data professionals? And third, what I recommend as a consultant, what you should do, how you do it, and the steps that you have to take to be successful. So let me go to the current situation, how we got here in terms of big data. And we got here because, say, a web has been created in the middle of the 90s, it allowed us to use a lot of information that is from the bricks and clicks on the first websites and then we have to go to the craziness of social media. The web has now become very data-driven. When you do a search on Google, some things, and your colleague, if he or she does the same search, it will appear completely different results for the other person. Why? Because more and more the web is being driven by favor, by preferences, by your profile, by your previous access to information. And more and more, we're going to be seeing things like my learning, predictive analytics, automated modeling, where it will make the web, and what we call web 3.0, a max phase of the Internet, which is very, very data-driven. That's why the explosion of information happens. And to tell the truth, there are four things and four driving factors that I've seen in the world today that are not separate. For me, from social media, everything related to mobile devices, tablets, the explosion of cloud computing where it's allowing small companies to access very top technology at the same price and at the same scale as large corporations. And the explosion of information, that's what we call big data, those four driving factors are really changing on how we do business at this point. And we believe we're in where the consumers who are the marketeers who have a lot of loss of what we call data points to get our decisions. And we have to understand all the details about relationships between customers, among customers and your clients, where in this world, you can create your customers. You can create loyalty. You can really understand patterns of consumption. And to tell you the truth, what I'm seeing here is a lot of confusion. The CEOs, the presence of the companies, the leadership is completely lost with everything. This confusion has penetrated through organizations and from several layers of management, we've seen a lot of confusion for what's happening with those four issues that I've talked about, the social model, the data and cloud computing. And from what I've seen and what I've been discussing with the leaders of this sector is it has become the new model that has been in the economy growth. And I have just met Gwen Thomas, a wonderful lady. I've met her at the San Diego EDW conference a month ago. And I was talking to her and she told me this since I decided to write it because it was so powerful to me. And Gwen told me at that conversation that the presence of power in the 25th century, power, and I wanted to talk about corporations, government, and institutions, is very influenced by how we were able to leverage the information assets that it has. So data, you use it quite well. You extract a lot of value from that. Now look at companies like Facebook, Google, those are the companies who really understood that and have been assessed. And what this point is, the behaviors of what those companies do quite well with their data. If you look at two corporations, two regular and homocopal corporations, and that will help those companies to attract more business, two growth margins, to attract new set of customers. So for me, when I talk about data, I'm talking a lot about people. I'm talking about technology, products of dealing with everything. I'm talking about modeling and analytics. And I'm talking about communication, which is very, very important for people who are dealing with data. And on top of that, I'm talking decisions that we will make and I'm talking about actions that will happen when you look at this data. And what we have seen today is we really understand that they're creating what we call a data-driven culture. And this data-driven culture is a factor in several industries. Because at the same time that all the possibilities that are in the market, new competitors, artists, day status quo, are creating new companies that might take and load business out of the market. Data is very important and I'm not saying that there's a disrupted factor for that. For me, what is big data? When you deal with a lot of information, what we call a high volume of information, and you have to deal in a very fast way that you have to deal with. And the data that you have, it comes from lots of data sources and from a variety of data sources. And the data is very... in a sense that it has a lot of details on that. So that's what we call big data. And the big data per se does not add value. What it adds value is when you start applying algorithms and analytics, when you start using that data to help me with something, with our business goals. So big data per se is not a buzzword. I definitely agree with EAA1. Probably this buzzword will fade away in the years to come. However, the impact that the big data process and culture has brought to us as a society has changed us forever. And why is that? Why is the term data exploded in the last years? Simple. Because we were able to start information to zero cost than what happened in the past. So we were able to create digital information that is being created at this point. But we were able to throw that away. We saw it very, very easily. However, when I started using e-mail in some corporations in the early 90s, you couldn't store more than like, I don't know, 200 messages, or I don't remember how many cases on your e-mail storage. And now, actually, if you go to accounts like Hotmail or Gmail, you can store as much information as you can. There are almost no limits for that. So it becomes cheaper for that. And because of that, to store information is very, very cheap, we have seen this exponential growth of data and data being stored. Okay. The main difference would be a traditional AI project from a big data project. Let's call it analytics. The term analytics, it existed for a long, long time. However, a few years ago, from Thomas Devenport, he wrote a very interesting book named Computing Analytics. And with that book, it was suddenly a phase in awareness of this term analytics. Because of that time, the computing power was starting to get more, you could get more, you could use more data. And then this book, in writing hand, and Professor Thomas Devenport on his book wrote that, even out of using data, quantitative analysis, and predictive models, they will have you drive big and bad decisions. Okay. And when you use, when you do big data projects, there will be reasons for the quality that you can achieve as time goes by. In the early beginning, when you start doing your analytics projects, you're going to do static reports. And all of a sudden, you give power to your user to do real-down on the reports. While the user should do some ad hoc queries that, and then doing that in some kind of probability, you start to do some forecast with high precision.  And the most mature for this curve, it happens when you have visualization for you. And the more mature you have in your use of analytics, the more mature you are able to compete. For example, Accenture, Boolean, and McKinsey, they follow up companies with the potential performance comparing how they use analytics for their business decisions. And in fact, the really quantitative research that they have done, they found out that the companies who are more mature analytics, they have better profits, they have better stock performance, they tend to lower their income among the employees, and they tend to be as companies, which are always on the best companies to work. So that's a matter of computing on a competitive So analytics, for me, it's not just about analyzing large volumes, great scope of information when you have real time and you're using those data sources and things like that. It's everything. And it's a way that is transforming the process. It's it's not just a chart, it's bringing a new edge of competitivity to industries around the world. If you can you define a big data, I'll define it to work. Because as we're talking here, all this information, all this data that's been created is being created because we're able, at the first moment, the young kind to analyze everything that happened with the people. The patterns of product consumption, patterns of what they post on Facebook, on how they consume information, patterns on how they interact among each other. And the value of the data is that in several, several industries from government to financial institutions to construction and everything. In every industry that you can go to, you will always find ways that you can make money on big data. Here in the States, there has been a strong push from the Obama administration regarding the open data for government to open the data that they have. Data companies and data providers and data exchanges are being questioned by the regulatory agencies the data that they have on how to provide transparency to students. So then, we take, for example, the information that a supermarket chain is able to get from their PDs, from their point of sales. If they compare that information with the patterns that are happening on their website, they can create a new way to compare and to generate money on top of that. Great. So we went to the first part about the current situation that we are looking at this point. Beautiful. So there's a lot of complication. Why are we struggling to hire defined and to hire big data professionals? That's the land of confusion. In some companies, there has been a lot of confusion of data responsibilities, of analytics responsibilities, of who should be doing what and how. For example, that I'll raise to everyone who is inside the organization. That's a sentence that was given to me one of the in my mission quality conference that happened last year. I was asked that question was raised. This person here, Tomas Kelly, he works for the U.S. Army and in his opinion, inside the Army, the person who is responsible who owns the data is the secretary of the Army. He is not a lieutenant there who is the person responsible for it. He is not a general, four-star general. He is not a colonel on the battlefield. He is the highest rank in the organization. In the case of the Army, he is the secretary of the Army. He is not the general, four-star general. They are caretakers of the data and their user, or maintainer of the data. So if you put the inside the business corporation who owns the data? For me, I'm the CEO. I find at the end of the day the sole responsible for the data inside the organization. It's not the IT organization, they are not the owners of the data. So the marketing manager or director who contains all the resources there, he is not the owner of the data. It's not the CFO who has all the information about the national performance of the corporation. They are not the owners of the data that was created and generated. And the data inside the organization is fragmented in several ways, from 10 drives to these drives, to the high end servers that you have, to the cloud providers that you are hiring. When you have that information fragmented and scattered throughout the corporation, you will find silos of information, silos where you have one person that thinks, okay, this information is in my notebook, so this information is mine. So I have put the information on that server so that I'm responsible and I'm the owner of that information. You are not. You are the data that should be seen as a corporate and enterprise asset. And when you start having fragmented information throughout the companies, what you see are versions of the data and one version who has it. And to streamline that, to help you out, a concept called Data Life Cycle was created. But Data Life Cycle is a very complex project to do. And above all, I have seen a lot of corporations that the data projects are being managed by IT professionals. And I think that's a terrible mistake because data projects are business projects. Like in the ERP projects, they were the successful ones, were the ones that you had a business user responsible for the project. For me, they are not responsible of IT. IT should be part of that, but not the so responsible. And data is a very, very abstract concept. In the webinar, if you are from T, have studied computer science in college, and if I talk about data, you understand quite a lot of what I'm talking about. Probably from HR person or from a market perspective, when I talk about data, it's a very abstract concept. So it's very hard for human beings to understand those abstract concepts. As I told you, the complex of dealing with Data Life Cycle, it's because the main process is like you capture the data, you analyze the data, you make a deployment, and then you have to maintain that. Okay, just for phases, but on top of that, a lot of requirements, a lot of rules, a lot of issues related to privacy, they come in. So it's not a very simple issue to manage inside the organization. And a lot of those technology players, the technology players of Sunday, every technology company in the world, they are big data provider. So every traditional guy, so they decide, okay, I'm now a big data provider. So I will end on this slide here after this presentation. Print a couple of that, and in your briefcase, anytime you go to a meeting where you're discussing a big data project or discussing who you should hire, take a look at this slide here. It shows the evolution that since computers appear in the world, it's still today. So if you go to the bottom center part where I see punch cards, in the beginning information was stored on punch cards. And the time went by from the six, we start to see like four files, files that were stored inside disks, sometimes external disks. And then came up the concepts of tables, relations, SQL server, which was a concept that appeared on the 80s. And then we will see here a lot of branches on these three here of all technology that we deal with as of today. Folks, you're going to see a lot of something called Hadoop MacReduce. Those are technologies that just appeared like for the last five years. And there's a lot of confusion here when we talk about big data. People say Hadoop MacReduce project using these technologies is very wrong. So that's what I'm suggesting you to make a couple of slides ahead with you. You can do big data projects with several technologies. However, when you do it with Hadoop MacReduce, you are doing it in a new way that was generated that allows you to do with a cluster of servers on the cloud. But that's not the only way. You can do big data projects using several technologies. And one of the most important big data technologies that exist in the market today. Probably I have forgot someone, but there are some companies that are not here that should be inserted here. And when I look at this slide here, that in a few years we have been having a lot of market consolidation. A lot of companies that are here, they will not be able to survive. They will not be able to generate money to sustain their business. A lot of companies here, they will merge with other business. A lot of companies here, they will be bought out by the large players. Just a handful of those companies here that they were start-ups and they will be able to cross on the future to come. But lots of lost technologies related to the big data initiatives. So if someone comes to you and says, okay, I'm going to do a big data project, hire me a Hadoop expert, okay, say, why Hadoop? Why not analyzing other options? And unfortunately, the technology vendors do not make things easier for us as customers. They care and they try to throw technology to us.  But not to tell you that if you want to achieve different results, it's just not a matter of acquiring new technology. You get the mentality of using this new technology. You can't change the process on how you do that. And if you want to have the same people on your business to use a new technology, you don't have the ability to accept. So in a data analysis organization, for you to succeed, you have to look differently on the current process of the organization. And I say that the big data professionals that are being hired will be seen for each one of them as a change inside your business. The problem is consulting companies, technology companies really understand at this point the details about doing data analytics projects because it requires you from moving from a technical view to a strategic information management view. You have everything that will combine your business strategy, your processes strategy, your people strategy, your marketing strategy, everything and the data and the technology and the process will need to support that. It's really, really, really hard. And that's why it's very hard for HR professionals to find those skills and trust them and they're going to see how much it is. So there's no solution to do that. Okay, first thing. Data, do you remember what I told you? It's a very abstract concept. My solution is to find a real object that people can relate to. If I talk about data, data, data and data management, data process, it's very hard to understand. By looking at this picture here, oh, okay, let's imagine that we are a factory. Probably all of you of us in the webinar has put his or her feet into a factory. But as you understand the factories, there are some drug trucks come, they unload some components. There are people, robots, a lot of things that have a factory that have materials that have been dropped off. And they have worked in a sense that at the end of the day, you have products that came out of those materials that were first entered in the factory, correct? So on that sense, see data as being managed as a real object that people can understand and relate to. But it's not going to be much easier to identify that as a systemic process for your business. And when I talk about data, you've got to understand what's the data that will change inside your organization. Where are the data sources that I'm getting my information and the data sources can be internal or can be from your partners, from your clients or consumers, from your partners. So the data that I acquire, in a sense, this data is collected, it's a lot of quality process. It's being sampling, you do a lot of studies on that. You aggregate this data in a sense that you put this data with combined with other data. And then at the end of the day, you have any products that are the final result of the data that you're working on. That's the only way that you can make sense on top of that. And on my personal experience, my expertise with Slidechain has me up to do my work on big data.  And when we talk about system and quality and things like that, the first person that comes to my mind is Professor Danny. Professor Danny was very famous on the late 40s, early 50s in Japan. He was an American professor, not very well now. He was doing his work. But when his ideas that he wrote from 1935 to 1940 were that you have to see in every factory, have production as an overall system when you see suppliers, you've seen production, you've seen tests. You apply quality in every part of this phase here and you distribute that to your market and you apply concepts of design and redesign on every stage of those processes here. Those are the things that became very popular when the Japanese government hired him to help him and another to help him to rebuild the Japanese industry that was completely destroyed after the Second World War. So applications like that to data management definitely help a lot for you to have better quality with your data. And data management can destroy credibility for any business. On the other hand, the United States Rotary Commission, they found that one in every four American consumers had information error in their credit report. If you're in the United States, you'll know that you cannot do anything. You can't get a car, get a mobile and get a subscription to your home if you do not have a good credit score, right? So can you get data errors on companies like Equifax, Experience, TransUnion, and that's terrible. So in the United States, we're coming to a point where if you don't get the information properly, that can hurt your business at the end of the day. So a lot of people who work with data, you will hire those people to try to understand about quality, what they understand about quality, how do they see quality as part of their business. In the United States, there are several quality programs throughout the world. And in the United States, some of them were created for data, like the data DMBoc framework that I show here in the center. Some of them, like what you see here on the other side is called PDCA. That was created by Professor Dan in the 30s. You will be using it for performance from factories and manufacturing. And they will be using it for data management and data quality purpose at this point. So you try to create a big data team. You try to hire big data professionals for your business. Then you can do big data if you do not have a data storage in place, if you do not have data policies, data governance, a security program in place as well, if you do not have a data architecture and if you do not have a data team that's focused on analytics and a data team that's focused on quality. If you do not have all those foundations as part of your team responsibility, the results will be not as good as you think they are. They are very, very hurt you at the end of the day.  And guess what happened? Come to realize that you need to hire data leaders that will think strategically about the data that will follow all the data regarding on this foundation here to manage the data organization. And the data at the end of the day is just a raw material. The data is where you can get raw material and make it very useful for business use. And that's why my point is, this is not a technology issue. This is much more initiated into business. And to read everything from these organizations, new titles have arrived in market. Chief head of analytics, data scientists. And let me explain my point of view on those titles here. First, if you look about those foundations there, those 9 or 10 foundations that I have put, you will definitely see someone who has a broader view who can look at all the people and processes and technology doing that. That will be responsible for managing that. So for me, you will need someone who can lead that organization in a sense that will drive business results. But to remember in the beginning that I told you that who is the owner of information and a lot of people from IT think they own the data inside the organization, seems that at this point they are the leader of technology in a company that is very concerned with this new role that's coming up in the market. Why? Because the new roles for the chief data officer, chief analytics officer, they are questioning how IT is doing their current job in the organizations. So it's like the new kid on the block suddenly arriving on the corporation and saying, hey, you guys are doing something wrong. Let's do it the right way. Let's look at data as an asset. Let's treat it as an atomic asset. Let's look here on how to make money. And that has brought some concerns. So a scientist is a person or a team who will look at the data to look for insights. And after those insights happen, you have to make the information available through other organizations.  Again, it involves a lot of steps that I'm showing here. There's a way that you can make it happen if you do not have a business leader responsible for that. So the main difference between a chief data officer and a data scientist is that a chief data officer will go over all the steps throughout the process. And the data scientist will have a more technical role looking at the data by itself, looking at the context of the business. The chief data officer, the lead data scientist toward the chief analytics officer is the executive responsible to manage the data professionals inside the organization. And why do you want to make this transition? Because, as I told you, it's not an ethnological project. The teams are the bridge between IT and the business units. In other words, that the role of the chief data officer and the role of the data organization is that this team here should be responsible to make the bridge between business and IT. Thinking about the data governance should be thinking about the systems and the processes involved in order to apply supply chain management concepts to achieve the goals of the organization. And I'm not saying that the IT, that's not that. They have their value. But when you think of a business in a true architecture model, when you think of any company to have a business architecture, that's the key tool. With the market, I will go after what kind of products and services I will have, what kind of customers I'm looking for. That's the business architecture. And then with the technology architecture, we're going to have that many sectors. We're going to deploy cloud computing. We're going to give those options here for users to access our information. We're going to deploy iPads for people in the field. That's the technology architecture. Data architecture is the bridge that will help the business architecture and the talk lines architecture to do well. And that's what you need. Three different mindset to make it work in the organization. For Peter Eichen, he just wrote a book, you will need a Chief Data Officer sooner or later because people are facing with synonymous of massive data that are happening nowadays. And if you do not have an executive that's responsible for all the new aspects architecture and the later organization data success, we will not be able to use your data to make money. And data science at the end of the day is the sport of taking the raw data, producing information, making this information useful, making this information a way that will drive actions and at the end of the day will bring money to the organization. And when you look at the value chain of data inside the organization, you will find very technical components like the big data, big data appliances, big data warehouses and you will look like information management concepts like data governance, data integration, data quality visualization and you will look very complex analytics stuff, analysis process like distributed analytics, machine learning, reporting. See, these are very complex issues that will serve vertical people from finance. So the data visualization there is responsible to manage overall. And the Chief Data Officer is that it's going to be responsible for managing the aspects of the data, the data governance programs and also the analytics involved on that. That's where the Chief Data Officer comes in handy. And one thing that's very important is like makes a great Chief Data Officer is that the person will be responsible for this organization to bring money at the end of the day. That's not the responsibility for the Chief Data Officer to keep writing code or writing SQL Server scripts. And to do that, like you were raising tremendous effort from a senior leader to put in a role that he or she should not be responsible. Gartner wrote a great article on this year, Advocative Executive Officers, CDOs are not a fact because as I told you, the big data tsunami that we're just passing through, that is the consequence for our lives forever. So you want to be a big data organization, great. You need a process to implement that. You need metallurgies to deliver your services because at the end of the day, a data organization is a service organization that will support the business. You need good knowledge to support your needs and above all, you need people. If you have any of those four components, forget it. You are doing a poorly job in implementing data organization. And a few examples of some screw ups that I have personally passed through recently and some of those screw ups that I have seen that some of my colleagues had told me in private. Before that, those cases are real. Also, I think a lot of people here find who they were. So I'm trying to protect the clients and also the gift ones. Some examples of why we're not doing that good. Let's talk about an advertising agency, a very large media group. They were searching for a head of analytics. Guess what? They interviewed those people, but they could not get the process forward because they needed five candidates to move the process to the next phase. And the third time they had five candidates, guess what? One of the candidates gave up, found another job, and decided not to compete anymore. This platform has been open for more than eight months. So they did not hire anyone. So that is really what you have someone hired at this point. It's been eight months and this business has been operated. So my recommendation to this company here that you forget about, you have survived for eight months without a person and you cannot apply this rule of having five candidates. So that's not good. Let me tell you about this service company. The service company, they have headquarters in the Bible Belt. They were looking for a vice president responsible for the I-Date warehouse. It was a leadership role, and I had a friend of mine who applied for the position. Every time he saw Elwin, he was talking to the recruiters, the manager management of the company, and then she started talking to the upper management there. And the company went kind of like in a way, not much. And my friend, she was like a true minority person between Hispanic and American, and she wanted to work together with me and my friend. We went to LinkedIn, to Facebook, and we completely backed the executives of the company who reported to home where they went to school, their friends, and what we found. We found that the CEO of the vice president and on the level below on the senior vice president, you could not find anyone that did that on the Bible Belt and didn't go to a certain range of schools. It's not a matter of knowing your skills, what they need. It's a matter of culture. Because probably you don't fit the culture of what they are looking for. The company, this was really interesting. The data director and chief analytics, the same position. And part of the job description was, at our company, we worship God's love for people. If you look at this position, we will be able to work and support your mission statement, respect diversity of employees, and work in a way that aligns with car values. The point is, okay, how about if the metadata is an essay? We'll look at it later. That's one of the issues that I think that we are not looking for the overall picture. And that one here, it happened to me. A major technology vendor, very recognized, but they didn't have a crew on big data. They were searching for a senior sales and business leader for their consulting practice. I have a very detailed interview process. And one of the interviews, one of the people who interviewed me, he asked me, hey, can you tell me what your Hadoop Map Reduce Programming Skills is? I said, you want a business leader writing code. So I came to the recruiter, to the hiring man, and said, maybe we're missing something here. You're asked for a sales business strategy leader, and why does that person need to program? The point is change the title. Avoid questions like that because you're doing a completely waste of time of each one of the people you're interviewing. That's never going to work. We're frustrating everybody. And this is the last experience that I want to share. As a consumer service company, they were searching for a vice president of Data Analytics to report to the marketing executive vice president. And on the job description of the senior Data Analytics person, they put there, creativity is a key for our success. They're doing their own slides. The more you partner, the more successful your team will be. Creativity will not just run for another newsletter example. You have to open your minds throughout analytics. And you don't have the Data Analytics college for my education, but you have a kick butt field experience. Well, I know I can kick some butts, but what I've seen here from the description here, they took a marketing role and combined the data and put the advertising. Not very good. So what the future brings to us, I see that the companies that will thrive in 2015 were the ones who will adapt faster to this scenario, are the companies who will be able to use analytics quite well and are the companies who will be able to use great human capital for their purpose. And if you look at a big data project, big data is there to make money for your business, to reduce your current costs, or also to improve your efficiency. And what does it take if a company wants to succeed in this data journey? My question is, you want to hire the best and most eager source to define the market. If you hire the best, but they're willing to take one for the team, if they're willing to take one for the team, but it's not good on what they do, you have to do well. So it's a matter of finding the best quality, technical quality, qualitative quality, and also combine with what you have to, you must have blood in your ears to do this job at this point. A friend of mine, Shamir Akmena, he wrote a paper with a checklist to hire big data professionals. And Shamir Akmena has very great points. He said, where they are? You have to think global. But they're not just around the corner. Probably they live on the West Coast. Probably they live on the West Coast. Probably they live somewhere in a city in the middle of the country. You have to understand that there is not just a matter of money. Those professors, they're trying to look to create something. Also, they have to understand what they have to do. They have to have a clarity of your business strategy overall. You will not be able to bring them unless you have the ability for tools, for data acquisition, and for all the resources that you're going to need. It's not enough of trying to bring one person that you're going to see the results. You have to think bigger than that. If you want to download his paper, I really recommend Shamir. He's done a great and superb job on putting it very clear for recruiters. So a data team must have passion for analytics. We want to be one to learn. If you're looking for a team that's not receiving hard questions, and I think that's all. That may be the between IT and the business to succeed. Let's talk about salaries. In some positions like that, as of today, they are looking for $3K a year for chief officer, chief knowledge officer, or VP roles, up to $2 million a year if you're consulting financial companies in New York City. Very spread out, and I would say, if you think this is expensive when you try to bring something, I'm not sure about your business strategy. I'm sure that you're going to be paying a much, much higher price than that. And then, if you want to hire well, this is changing how companies compete in the market. These are, at the end of the day, business projects. Talent is scarce, and I can say it predicts that we're going to, in the long run, we're going to have a lack of almost 200,000 analytics people in the next three years. And a new breed of professionals is in need. Professionals who understand data, who understand technology, who are good at communicating with people, who are at making the right connections to deliver results. And entrepreneurs and HR professionals, they have to work to get on that. And many of these people are thinking and thinking out of the wall, because the worst thing that can happen is that you bring a data organization in place, and they become frustrated because you have not planned for their career development, for the budget that's going to be needed to buy those things that's necessary for them to do their work. Okay? So that's what I had to say. Those are our Marri formation, and Shannon, that's the boy, too. Obviously, we're right at the top of the hour, but I do encourage the audience to submit their questions in the Q&A section there in the bottom right-hand side, as Marri has offered to write up the answers to your questions, and we'll get that in the follow-up email that will go out with the links to the slides and links to the recording within the next two business days, so by end of day Monday. Marri, we do have one quick question that came in, if you don't mind just hanging out just a little bit here. Research requires quality, so who's responsible for data quality? Is it the CDO? Is it the CDO who's responsible for that? Okay, Robin, yes. The chief data officer's main goal is to bring money, and if you do not have a data architecture in place and if your data does not have quality. Remember when I presented a slide about the foundations of the data team, one of the foundations there is data quality. So the chief data officer is the sole responsible for the data quality inside the organization. Great question. That's really all we have time for. Mario, thank you so much for another great presentation. This was just really another very fantastic, very educational. Thanks to everyone for attending today and for your time. We look forward to it. And again, I will send out the links to the slides and links to the recording within two business days. And Mario, I'll be sure to send out your contact information there so in case you have any additional questions. I look forward to it. I appreciate it. Thank you. Bye.


Hi, I'm Fabio, Product Manager at GitHub. In this video, we'll go through the vision for the secure stage and the roadmap for the next releases. Let's start with a brief overview of the stage and which is the vision for the next year. GitHub covers all the stages in the DevOps life cycle, all the stages from plant to monitor, all the stages in one single application. GitHub also includes a secure stage. The secure stage makes applications built and applied with GitHub more secure by checking for vulnerabilities during the entire development life cycle. The main goal of the secure stage is to make security easy and accessible to anyone. Security tools are often hard to set up and manage, or sometimes people just don't focus on security because it's not their top priority until it's too late. Security automatically integrated in any pipeline with no configuration helps to onboard people and to show which is the potential of this area. The features provide support for decision makers. They don't replace them. Users have their final call on triaging vulnerabilities and taking action to remediate them. Security is not a black and white thing and it may highly depend case by case. Provided feedback is actionable. Developers can look at the high-level security status of their projects and then drill down into specific vulnerabilities. They can start remediation processes and follow the complete flow until a fix is deployed. All in one single interface, all in one single application, collaboration between different departments is easier with GitHub. Features are designed for both developers and security professionals. Even if they use different views and flows, they work on the same data and there is a single source of truth. No matter which is your role, GitHub will give you what you need. In 2019, the secure stage is focusing on the following product categories. Categories are organized into groups. We also have other top-level features that are cross-category but deserve to be mentioned as critical components of our vision. Cross-category is a static application of security testing. It checks the source code to verify that it has no well-known vulnerabilities. For example, coding errors like buffer overflows and insecure function calls are detected by this type of test. Our goal is to increase the list of supported programming languages to make the experience even better. Developers might unintentionally commit credentials and passwords to their repositories. In some cases, this means that their values will be publicly accessible to anyone. Secret detection aims to spot this data. For example, authorization keys to access their production environment. After the detection, users can be aware of the problem and revoke liquor credentials. Our goal is to introduce support for this new category and integrate results in existing security reports. Modern applications include public libraries very often. Dependency scanning, part of software composition analysis, checks security advisories for third-party dependencies and packages, reporting if they contain known vulnerabilities. This helps developers to upgrade them to newer versions that fix the flows. Our goal is to add more package managers to the supported list and to shift left these checks even more, even before code is committed to the repository. The second category of software composition analysis is license management. Even if not strictly related to security, it deals with third-party components that may introduce compliance problems. Defining policies for approved and blacklisted licenses is critical to ensure that the final application can be released to the public. Our goal is to make very easy for project and group owners to define policies so developers know in advance if their changes can be accepted or not. Once the application has been applied, Gillab can perform an application test that emulates a real attack. Dynamic application security testing can target applications written in any language. To get earlier results, Gillab can target review apps and provide feedback in the merge request widget even before code is approved and merged into the master branch. Our goal is to support multiple approaches for dynamic application security testing, like quick scans to fit in pipelines where time is a critical aspect. Interactive application security testing is an advanced technique that mixes dynamic attacks and runtime inspection to get more information about possible vulnerabilities triggered by remote requests. While the dynamic scan is running, a local agent monitors the application behavior spotting unexpected errors and flows that could be leveraged by attackers to get access to sensitive data. Our goal is to develop the technology needed to provide this feedback and to cover the main programming languages. Fuzzing is another new product category that we want to start in 2019. It consists in creating a huge amount of arbitrary random payloads and to send them to the running application. As opposed to standard dynamic analysis, fuzzing can test the app behavior with unexpected inputs. Our goal is to enable fuzzing as an additional advanced type of dynamic scan that users can leverage for specific sessions that don't have time-sensitive constraints. Container scanning scans Docker images for well-known secure components that may be part of the base image used to wrap the application. Even if the app code doesn't contain any security flow, vulnerabilities in the deployed container make the entire environment insecure and exposed to attacks. Our goal is to improve the information available to developers and to deeply integrate container scanning in the built-in GitHub container registry. Auto remediation is a cross-category feature that aims to provide an automated solution to remediate security vulnerabilities. GitHub will automatically check, detect, fix, deploy, and monitor the new version of the app, notifying users about the process and in case monolactions are needed. Our goal is to ensure that the flow can be fully automated and that the highest amount of vulnerabilities can be addressed by this flow. The security dashboard is the primary tool for security directors and engineers to monitor and start remediating security vulnerabilities. It gives a high-level view of the security status of a group, allowing drill-down capabilities and statistical data to track how the security process is performing. Our goal is to provide more insight to better understand performances of the security process at the instance level and support to first-class management of vulnerabilities from detection to solution. You can find more details and additional information in the product category page of the GitHub Handbook. Everyone can contribute with comments and proposals in topics and issues where these categories are discussed. Anything else? Please welcome the Defend stage. Defend is a brand-new DevOps stage that along with Secure completes the security coverage of the development lifecycle. It focuses on the Ops side, protecting the applied applications and crowd environments. Even after the app has been deployed, it should be constantly monitored to prevent and detect real attacks. Security teams should be warned of potential threats so they can mitigate effects and minimize the impact. Now it's time to see what will be released in the next versions of Gillette. The planning is always subject to changes and you can check the direction page to be up-to-date with the latest decisions. Gillette 11.9, that will be released on March 22nd, will include many interesting features. It will be possible to detect secrets unintentionally committed to the repository and to see results as part of the SEST report. This is the very first MVC for secret detection. Auto remediation will reach a major milestone with the ability to create a merge request that fixes a given vulnerability, making the entire remediation flow available in the Gillette UI. SEST will be able to analyze multimodal Maven projects that are currently unsupported. This release will also include container scanning improvements. First of all, results will be available in the group security dashboard along with SEST and the panacea scanning. We will increase details of container scanning vulnerabilities, making it easier to identify the problem and to fix it. Starting with this release, pipeline job definitions will be available officially as include templates for all the security features. They should be used when adding security jobs to pipelines so they can be upgraded automatically when a new version is released. On April 22nd, Gillette 11.10 will be released. It will contain the first iteration of runtime application security, one of the categories of the brand new defense stage. The web application firewall included in the Ingex controller for Kubernetes will provide the first layer of protection against external attacks to deployed applications. This release will also include the first iteration of the integration between container scanning and the Gillette container registry. Users will be able to check security status from the registry page when available. We will add desk vulnerabilities to the group security dashboard. This is the last missing piece to make the security findings available to security engineers. SEST will be also improved with the addition of a new analyzer for projects using the TypeScript language. It will be automatically available to everyone that already has SEST capabilities enabled for their projects without any specific change. Last release before the new major version is Gillette 11.11 that will be available on May 22nd. The first improvement is for dynamic application security testing. We want to make active scans available so users may have more accurate results on environments where this kind of attack is not impacting stability of a production environment. On the same topic, we also want to introduce dynamic scans on the master branch for auto devops. This could be risky since it may impact the production environment. So we'll ensure that the behavior is not bringing the main site down. The security dashboard targeting security directors and engineers will be available at the instance level. Users will be able to monitor any project and group in their Gillette instance. And it will be easier to keep everything under control. It will also be possible to forbid merging a change that is introducing a new license that has been already blacklisted in the project. This will prevent accidental mergers that would break compliance, creating possible legal and management problems for the app. In this release, we'll add a second iteration for secret detection. Users will be able to test the entire history of their repositories instead of just the last version available. This could be useful in some cases, even if it may take a lot of time to complete. This option will be available as an alternative to the default flow. Excellent. Here is what you will see in the next releases of Gillette. Thank you for watching. And if you have any question, please leave a comment to this video. Open an issue in the Gillette issue tracker or just write an email. We love talking with people about our plans and we really value any feedback. It helps to better understand how to solve real problems. Goodbye and stay tuned for the next video.