MS in Business Analytics Webinar

Scott Dawson: Hi everybody, I’m Scott Dawson. I’m Dean of the Orfalea College of Business and thank you so much for joining us today for our webinar on the tremendous opportunity that the field of business analytics presents to us. Our agenda today: we are going to first meet our speakers briefly, we’re going to talk about the field of business analytics, and an overview of our new MS program in business analytics, then we’re going to have a conversation with a couple of our industry partners, who are experts in the field, and then finally, wrap up with next steps. Scott Dawson: So in addition to myself, we have with us today Sanjiv Jaggia, who is our Associate Dean for Graduate Programs, in the Orfalea College of Business, and the individual who led the development of this program We have Josh Knox from Google, who is the Engineering Program Manager, and we have Rich Clayton, from Oracle, who is the VP of Business Analytics. Both of these individuals have been very helpful to us in helping us develop our program. So, let’s talk about the overview of the industry, we spent a fair amount of time, talking to a lot of folks in the industry and looking at a lot of different programs that exist out in the field, and what the industry really needs are individuals who can develop insight, from data, within the context of business issues, and to be able to deploy that data for effective business solutions and input into good strategic decisions. We heard over and over again that the industry really wants well rounded people, who really understand business, that are good team players, and good communicators. There’s huge demand in the industry for folks in this field. I’ve seen figures of everywhere from 300,000 open positions to 500,000 open positions. It is predicted that by 2018 there will be a shortage of 1.5 million people. These are huge numbers, and who knows how accurate they are, but I think we can be assured that there are tremendous opportunities out there for experts in the field. And why not? Because the ROI on dollars spent on analytics are significant- estimated to be about 11 x return. Interestingly only about 12% of executives actually have a good sense for how they could employ data for good strategic purposes and for good return on investment, which means there’s another 88% who maybe just have this nagging feeling that they’re missing an opportunity, and indeed there’s a ton of data out there and the amount of that data that’s actually being used for strategic business purposes is very low. Applications exist across all kinds of industries, from communications to oil & gas. In our field of education, particular examples are trying to predict when a student has made a bad choice about a major, or they’re about to drop out. Or in healthcare, where analytics is predicting when patients are likely to go off their meds, and end up having to be readmitted into hospital- so lots of applications exist. So we developed this MS in Business Analytics to achieve three goals. First, to produce industry leaders in a rapidly evolving field, to address the huge, growing number of unmet needs that exist in the industry, and to collaborate with our business partners on developing a pool of analytic professionals. As I’ve mentioned before, there are just tremendous opportunities for folks that are coming out with expertise in this field. It’s one of the most demanded skills that exist now, according to Burning Glass. The Bureau of Labor Statistics shows the job category is expected to grow by 45% between 2008 and 2018, making it one of the fastest growing fields. And the salaries are extremely good, with average annual salaries of $171,000 almost $172,000. So at this point, we are going to turn it over to my colleague Sanjiv to talk more about the program. Sanjiv Jaggia: Hello, so when we started about a year and a half ago, it was very clear to us, there was tremendous need for a program in business analytics, but we wanted to make sure that we have a good team for the program. So we first started with faculty. We looked at our pool of faculty in the college, and what their academic interests were, and what kind of industry experience they had. So what was really interesting is that almost all areas were very well represented in business analytics. So we have a professor in finance, whose primary interest is business analytics, we have a professor in economics, in accounting, in marketing and information systems. We wanted to develop a program which was truly interdisciplinary and we were able to do that because we have the faculty who are interested in contributing to this program, and also have tremendous industry experience in the field. So that was step number one. We had the ingredients. And then we wanted a niche; we wanted a team, we wanted something that our program is famous for. By talking to both directors of other programs around the country, and also talking to business leaders in the field, the first thing that was made very clear to us, is that the market is truly hungry for those who can ask questions, not only the ones who can answer every question, but also for people who can ask these questions. and again it was clear to us, that having a program through the business school, where people understand the business acumen. They are the ones who could actually ask questions and then go through the techniques to come up with something which actually has value for the business. So that was our first step that we wanted people, we wanted to devise our courses also in a way where quite a bit of focus is on asking questions. Then we wanted something regarding the curriculum. What kind of an approach do we really want to follow? Here there were two types of approaches, which are pretty well known. One is the data first approach, where you write really complicated computer codes and let the data do all the talking. The other is where you have a model first approach, where you actually build a model to do predictive analytics. So there is the age old problem to be able to distinguish between correlation and causation. If you look at just data and you find that areas which have a lot of crime are also the areas which have a lot of cops Now, if you’re doing your predictive analytics just on the basis of that, then the policy recommendation would be that in order to reduce crime, you reduce cops, which we all know that that is ridiculous. So there is that issue of what causes and what correlates, and by doing a model first approach, we can actually develop models which can actually differentiate between the two types and some models with instrumental variables and other techniques have been known in statistics and econometrics… That’s something we could do. We also have incredibly good faculty, and talented faculty, in the field of econometrics. So, that’s the approach we took- we thought we would have a program that has a model first approach. Another thing we were learning from industry professionals was that people should be able to communicate. A common problem described to us was the CEO of a company asked a technical person to use data to answer a particular question, and this technical person, goes to his/her office comes up with the answer and now the CEO says that’s not what I really want to do. So what’s really lacking in this scenario is there was no back and forth there was no communication. So again, we want to put a lot of emphasis on communication, as I’m going through the curriculum, you will see that there is a lot of emphasis on communication. And the end result for our program is to train managers. Yes, there is a lot of data crunching that goes along the way, there’ll be a lot of models and technology that we will be learning along the way, but the end product is that we are trying to make managers not just number crunchers. So as Scott mentioned, we do have an incredibly fine advisory board, who are basically experts in this field, and we have people from Google, from Oracle, in fact you’ll be talking to somebody from Google and Oracle in a few minutes, but we also have people from Walmart, from Symantec, Nest and many other companies. So one thing that they suggested, which now we believe is immensely valuable is that we will have two courses, or eight units of courses, which are basically collaborative industry projects, where we actually go to our industry partners, and we get the data and we get the project from them and then we give it to students. Again, we do not provide the questions, we just give them the context, we give them the data, they come up with the question, and they provide the answer, they work in a team, they get valuable experience working in a team, for a client, and again communicate the results to our industry partners as well as to their professors to get credit. I’ll quickly go over the curriculum now. So we have a course in data visualization & communication. You know as I said, communication is going to be very important here. And then we have a course in statistics, we have a course in econometrics, and then computational methods, which will be based on the programming language R. Then we have a course in data management, data analytics, and the collaborative industry projects that i just talked about, which are for eight units. Then you have some electives here: more econometrics, you have a couple of courses in marketing, one would be marketing analytics and the other is marketing research. And then you have a course in basic econometrics as well. In terms of software, we’ll be learning MS SQL Server, Oracle, MS Access. For programming language there will be SQL and R, For statistical analysis, we will be using STATA, SAS, and R. And for data visualization, we’ll be using Tableau, R and Excel, and for Data & Text Mining, We’ll be using SAS Enterprise Miner, SAS Text Miner and XL Miner. I’d quickly like to now introduce our faculty. Aric Shafran will teach a course in Computation, with R. And Samual Frame will teach a course in Visualization, where he will be using R and Tableau. So, Aric Shafran, before he got his PhD in Economics, he actually got a master’s degree in Computer Science from Cornell, He is really the right person for that Computation course. And Samuel Frame, he has actually done culinary analytics for restaurants, and he has worked doing risk modeling for Wells Fargo. So both of them have very fine industry experience and Aric Shafran on the previous slide also worked for Oracle, Intel and IBM. So Barry Floyd and Leida Chen will be teaching courses in Data Management and Mining. And Barry is a widely traveled educator, as well as consultant all across the globe, with training in information systems. And Leida Chen has actually worked for Microsoft, Asia Pacific as data analytics manager, before he came to Cal Poly. And he has also managed analytics projects for Microsoft Smart City Solutions. So here are your three people doing Statistical Modeling in regular econometics, financial econometrics and Basean econometrics. Each one of these are highly reputed scholars in their field. Carlos is currently working on evaluating the largest federally funded job trading program in the US for disadvantaged youth. Jon James worked in the federal bank of Cleveland before he came to Cal Poly. And Garland has also worked for Amazon, where he was developing the Israeli state of the art software for doing forecasting. We’ll now meet our marketing professors. Jeff Hess, before he came to Cal Poly, was vice president at Harris Interactive and TNS research. Brennan Davis worked as a data analytics person for Nissan Motors before he came to Cal Poly. So again, both of them bring incredibly good industry experience. Here is Eduardo Zambrano and Garland again. They also do decision analysis. Eduardo is currently working as a consultant to the UN program… he is redesigning the human development and gender inequality index. Garland as I mentioned before, you know, has worked for Amazon. And finally, that’s me and Jean Francois Coget, and I also have 20+ years experience in applied statistics, and econometrics. I have authored a couple of highly successful books in business statistics, and I am currently working on another book on data analytics. And Jean Francois, he brings in the soft skills that our people still need. He is an effective team collaborator, leadership leader, and also teaches interpersonal communications. He will be providing a lot of valuable assistance when people are working on collaborative industry projects. Now we send the mic back to Scott to introduce our industry partners. Scott Dawson: I’m pretty excited about this Sanjiv, I think I’m going to apply to that program myself. It looks awesome. So now we’re going to shift gears and talk to our industry partners. I’m going to ask them to introduce themselves and share with you a little bit more about their backgrounds and their work. You want to start Josh? Joshua Knox: Sure, I’m Joshua Knox. I started at Google just under 10 years ago, right out of grad school at Cal Poly. Had this program existed back then, it probably would have been the go to for my masters, cause it was really my main interest even back then. I have spent the last 10 years working across our ad systems and analytics at Google. I’m excited to join you guys today and talk a little bit more about the program at Cal Poly. Scott Dawson: Great thanks. Rich? Rich Clayton: Good morning, and congratulations Scott and Sanjiv, this is a very, very exciting time for Cal Poly, and I’m happy to be a part of this. Good morning everyone, I am Rich Clayton. I’m VP of Business Analytics for Oracle. I joined about 8.5 years ago, when Oracle acquired an analytics company called Hyperiam Solutions. It predominately served CFOs and financial management leaders. I spent the last 25 years of my career in the analytics field and various different roles. Today it’s predominately a marketing role, where I lead our marketing strategy for all the analytic products at Oracle. But before that I started out in public accounting and corporate finance and consulting and different roles. I sort of learned the hard way about the opportunity of analytics, but it’s been a field that’s really changed in the last few years and I’m excited to talk to you a little bit more about it today. Scott Dawson: Well Josh and Rich, thanks so much for spending time with us. We have a couple individuals here who are deep into the field here. We’re going to start with the first question and ask Rich to take the lead on that: What unique business challenges can an MS in Business Analytics help solve? Rich Clayton: Yeah, it’s a great question Scott, and I think really there are many, many different challenges across industries and functions, you know in agriculture for example, we’re trying to increase yield to feed the world. And data and analysis provide a pretty important foundation for what’s called precision farming, where now farmers are using data from tractors, using geothermal data from satellites, to try to predict outcomes in the field, and try to minimze waste. In healthcare, you mentioned that analytics is not just working on curing cancer, but also about using genome data for personalized medicine. Manufacturing, you know may be an old school industry, but they’re trying to do preventative maintenance on equipment, because every hour that equipment isn’t operational, it isn’t generating revenues, and predicting that mean time to failure is important for their success. So, just to give you a simple example, I work with a large door and window manufacturer in the midwest. They’re trying to create smart products, and smart products mean that data from the product that’s being manufactured, can be actually used as a revnue generating source. Sometimes it’s called the internet of things or IO, but what are they going to do with the data? So they’re going to sell that information to real estate property managers, to security companies, and police departments, so when bad people walk through your door, or window, that can be detected sooner than later. So there is a tremendous number of opportunities, more traditional finance functions are even changing pretty rapidly. You know, it’s not just about building budgets anymore, it’s about predicting cash flow. And so, if you go into finance, and you don’t have an understanding about data science and predictive analytics, as Sanjiv talked about a little bit, I think you’ll be left behind. I think there are many, many, many different opportunities and challenges, but those are just a couple of examples. Scott Dawson: Those are great examples, I hear from a friend that is in accounting too, that the whole field of auditing is likely being impacted as well. And, Josh, do you want to add anything? Josh Knox: Yeah, I think Rich gave some really good broad examples, that illustrate clearly that it’s not limited to a few specific functions, or a few specific industries. It’s really across the board, so in the same way that someone in the past maybe didn’t need to have typing skills, but now in the 21st century, and everything being computing based, you know you have to know how to use a keyboard, and I think in the future, data is going to be one of those critical tools that everyone needs to understand. So the unique challenge is really to be able to build an organization that is data driven. I’ve worked with a lot of different companies on the Google analytics side of things, that you know, you talk to them and they’re known for being very data driven and data forward and really still, I would say that, truly leveraging for the value of the data is across the board. So, with all this data is a real democratization of decision making. Many people from the top to the bottom of an organization have access to this data, and if you’re able to come in and make an impression analysis, suggest really valuable actions I think you can show your value to an organization, and there is a tremendous opportunity there. So this great equalizer helps us make better decisions, you know, more civil discourse, so that we can be more based on facts and not just opinions. That skill is a really important part of business when you build a judgment, but I think it also needs to be balanced with some facts, not just the highest paid person’s opinion. Scott Dawson: Great insights. So we’ll move on to our second question. And Josh, I’m going to ask you to take the lead on this. What do comanies like Google and Oracle look for in a strong business analytics applicant? Josh Knox: I think it’s something that you and Sanjiv touched on earlier. I was looking earlier, just to get a sense for the number of jobs that Google currently has listed with analyst or analysis in the title, and there are more than 350, and in having worked with people in different functions at work, and external partners, I think a key piece is the ability to weave a story with these meaningful insights from data. So that focus that is being described not just on the technical side but also on the business, is really a critical piece. So I think a lot of times what we look for when we’re hiring people, into these types of roles, is that ability to build a story with the data, but also to be action oriented. It’s not just about reporting, its great to pat yourself on the back when you launch something and you see all these great performance metrics coming through, but that’s not the point. Getting reporting built is a very small part of the overall equation. So materially impacting the bottom line with actions based on this data is really the key and people with experience doing that, seems to still be pretty rare skillset. Scott Dawson: And Rich? Rich Clayton: Yeah, so, in my area we look for folks similar to what Josh said, who are curious you know? Candidates that want to challenge the biases, and look beyond just testing their hypothesis, but really curious about discovering patterns that perhaps people didn’t know, but not just for intellectual curiousity, but to Josh’s point, putting it into action. So, I think it’s critical and you all have I think put a curriculum together that embraces this idea, but we look for people that don’t just have strong technical skills and analyzing information, but can also put that in context to what’s trying to be decided that were acted on right now. So that story telling piece that Josh mentioned, I think, especially in large organizations, is pretty essential. And the last point I just wanna add is that data is never clean, right? It always has complexities, and it’s never a simple as it looks, and so we look for folks that have sort of, a design orientation and uhm, for improving analytic processes, because you can’t wait for perfect data. You’ve got to be creative, you’ve gotta be sort of scrappy, if you will, in solving the analytical challenges, and cleaning the data at the same time. It’s not just a sequential, but its a network approach. Scott Dawson: So what I’m hearing is that the demands are pretty sophisticated and certainly you can see why a masters degree, which allows a depth of study, is needed to really fullfil what you’re looking for? Scott Dawson: Absolutely. So we’ll move on to our third question now. What types of career growth can an analytics leader expect? And Josh, why don’t we start with you? Josh Knox: Sure, I thought this was a good one, because, even myself, I’ve noticed some very interesting trends in the business world, and the industry. A friend who was a product manager who I had worked with on Google analytics is now the Chief Data Officer, for Dao Jones. And I hadn’t really heard the title previously. Poking around a little bit, it still seems to be a somewhat uncommon role, but something that I really would bet on becoming more prevalent in industry, in the future, is the Chief Information Data Officer. So in terms of career growth, I mean that’s basically all the way at the top, to the executive suite. And over time, you see the executive officer’s in the major corporations of the world, you see shifting patterns, in terms of their backgrounds, maybe engineering was a common background for people who then make it to the executive suite for certain industries and then maybe law for some point of time was one of those, or accounting and skills like that. I think data is and analysis is really going to be one of those skill sets and backgrounds where you start to see people moving into the executive suite who are conversant and capable and comfortable in that role. So, you know, people who actually understand the data, I think have a real advantage, especially whether you’re starting out on the top, the middle, the bottom. Being able to dive into the data really allows you to understand the business of it in a very critical way, and often times, you’re one of the few who really has that understanding. Which really opens up opportunities, so countless sources coming to the tremendous growth of jobs requiring these skills but this really gets to, not just getting in the door and getting a job, but what is the future, and I think that’s a really big important part of feeling fullfilled in your career, and you know, your career being such an important part of your life, and how much time you spend everyday at work, I think it’s a pretty critical piece and I really think that not only is it an interesting job, but it’s an interesting job with a great future potential. Scott Dawson: That’s a very motivational response. Rich? Rich Clayton: You know, I think a couple things have come to mind when we ponder this question. First of all, I think that many times the perception is that “well I have to aspire to be a data scientist in this field” and clearly I think as you’ve mentioned, that’s not really the case. While there are plenty of roles and jobs for actuarial and actuaries and data scientists, I think the broader business market is the one that is in such great need. So much so that companies are now creating new executive roles called Chief Data Officers, which in my assessment is not a code word for IT or a technology person, but its a new role that they are creating to drive innovation and create data capital inside their companies, by using it as I described in the window and door manufacturer. So I think folks that pursue an advanced degree in analytics have lots of different opportunities, my view is that specialization is really essential, and that analytics is one that will open many, many doors for folks that have an appreciation, so you know, if you start in marketing, you might go into a marketing operations role, lead a marketing operations team. In my organization, in most organizations, that’s essentially the challenge for the Chief Marketing Officer, in finance, it could be a career path that includes running the head of planning and analysis, so a VP of Planning and Analysis. I really do think that analytics is the new business language and it does provide lots of different career paths if you have an appreciation for it. Scott Dawson: So tremendous opportunities. Well we have one last question for Josh and Rich. How does the Cal Poly MS in Business Analytics program stand out from other programs? And Rich, you want to take the lead on this one? Rich Clayton: Absolutely, Well Scott, as you know, I’m very excited to work with you, the faculty and administration, and I think what you’re doing is very, very, unique, analytics is a field I have dedicated my life to professional life to, and I’m really honored to work with Cal Poly on this. There’s a few things that stand out for me. First is, your interdisciplinary approach. I think connecting the dots across these academic fields is very important, and very unique in terms of your strategy. I think a lot of programs out there today are really one dimensional, I think Cal Poly is very unique. The second thing is that your approach in learn by doing, I think is distinctive because data is a doing thing. It changes quickly. To Sanjiv’s point, the questions are new, and your approach to connecting students with real data sets and real businesses and real challenges is something that is going to set your students apart, when they come out of the program. I think the fact that you have this blended curriculum is important, I also think that the connection to your econometrics background is special, and an important part of the strategy, so I think the students that come from your program will have many, many opportunities and look forward to working with you in the future on this. Scott Dawson: Great. Thank you Rich. And Josh, anything to add? Joshua Knox: Yeah, I think Cal Poly has a lot of advantages especially with regards to this MS in Business Analytics. I think that Sanjiv and Rich alluded to a lot of the programs in this area, first of all, it’s a fairly rare thing to have such a degree. But a lot of the programs I’ve seen tend to chase people away because they are very heavily focused on the computational side of things. And as was alluded to, it’s really critical to understand the practical implementations of the data and the actions you’re suggesting based on that data. So there really needs to be an intuition for the communications and the business side and the interpersonal, and some of those soft skills which is really reflected in the curriculum. And it being centered in the business school, so you know, how do you get the right people on board? What are the motivators for those groups of people who are at play with these actions that you’re taking. Based on data, if you come in and say “you’ve been doing everything wrong historically” and what you should do is completely countered to anything you’ve done before, and we’re going to have to slash and burn. That may not be a suggestion that is actionable, not because the data doesn’t show it to be true, but because it’s approached and it’s a motive in an impractical way. So, you know, I think that as well as the industry connections that we’ve seen you know, it’s been very illuminating to participate with the advisory board and give suggestions about common pitfalls, when people are educating those who enter this field. Some of the oversights, some of the weaknesses in the candidate pool when we go out there and try to hire for the 350+ jobs that are open at Google. I think its that mix of business and technical with the focus on group work, and really the bread and butter of Cal Poly learn by doing approach that sets it apart. Scott Dawson: Well great, thank you so much again for spending time with us today, and all the time you’ve helped us in designing this program, and I think in addition to the Polytechnic learn by doing aspect so deeply in-bedded in the Cal Poly ethics, I think partnering with industry to make sure that we’re designing programs, delivering programs that the industry needs. You two have been instrumental in helping us. So let’s move on to next steps. I’ll turn it back over to Sanjiv. Sanjiv Jaggia: I’ll talk a little bit about the prerequisites for the program… You need a bachelor’s degree, it really doesn’t matter what your degree is in. It could be math, it could be statistics, it could be business, it could be engineering, anything… What we really require is your interest in quantitative thinking, and that’s essential. And also that you have taken the basic quantitative courses. We are not asking for a whole lot in terms of prerequisites, but these are the bare minimum. We need two college level courses in statistics, one college level course in calculus and one college level course in linear algebra. And as I said before, these are minimum. The more you come with, the better qualified you would be for a program like that. Sometimes we hear from people who have taken courses in statistics and calculus, and perhaps not in linear algebra, and they are still very interested in applying, so we will have a conditional admit, where the cover letter, or the letter we send out, the decision letter will state very clearly what kind of remedial courses you’ll need to take, before you actually start taking courses of the program. In terms of getting ready, we have take your GRE & GMAT, or one of the two, if you have not done that already. We don’t have, again, a magic number of GMAT or GRE score, what will bring you in, we look at your GMAT, GRE scores, we look at GPA, we look at your statement of purpose, we also look at your letters of recommendation, so we look at all of those things to decide who gets in and who has to wait. Again, it’s difficult to come up with a number but if I were to say a number, I would say we need at least 600 on GMAT, and on the quantitative part of the GRE, we need about 160. But again, the more the better, and if you don’t have those thresholds, met yet, you may still be admitted if everything else about your application is compelling. Our program has still not been formally approved by the Chancellor. We are expecting it to happen anytime soon, hopefully over the next two or three weeks, but until the program is approved, we will not be taking in your applications, but you can be ready for it, you can do your scores, GMAT and GRE, if you haven’t done that, and get your package in place, so that when it does open up, you can apply right away. We will be sending you an email when admissions opens, and we also encourage you to look at our website for updates. Scott, back to you. Scott Dawson: Okay, so just to wrap up, I want to thank Josh and Rich again, and Sanjiv for joining us today. I want to thank everybody who has been listening in and I hope that you learned something about the field of business analytics and something about our program. We’d love to see you in that program… you know the demand has grown so quickly here, we worked very hard last year to develop this program from scratch, and get it through the approval system at Cal Poly and into the Chancellor’s office, in record time. The feedback that we’ve received was really not sensitive to the curriculum itself, from the Chancellor’s office, and we feel confident that we can address the couple issues that they have pointed out, very rapidly. So look for the approval to be on the webpage soon, and please let us know if we can answer any of your questions, and have a good day. Thank you!

MSP Databalance Case Study: Business decisions in real time based on fast data-driven Analytics

In our modern datacenters, we prefer IBM infrastructure. For our environment we have a mixed platform from Intel, Linux, Power i and AIX.
For this we use the various IBM Power P, Power i and Intel servers. All these systems are linked using the capabilities of the IBM San Volume Controller, FlashSystem V840 and V7000. As central storage solutions we use the V3700, V7000 and V840 storage systems, because of their excellent speed, reliability and low operating costs. The San Volume Controller is used to easy tier, real time compression and mirroring. With these standard techniques present at our systems we are redundant and thus high available. To insure continuity, we are using Tivoli Storage Manager software on most platforms, fully integrated with our SVC solutions. Together with IBM we can offer all possible cloud solutions for our customers, IAAS, PAAS, SAAS. The SAP platform used by Beeztees is hosted by Databalance Services. Databalance advised Beeztees to put the database servers on IBM Flash Storage. This IBM V840 Flash Storage delivers more than 400 thousand IOP’ s. The other servers have been placed on Easy Tier Storage, resulting in an optimal mix of speed and capacity. In practice the generation of reports, lookup jobs and batches are processed much faster. Databalance is a key partner of Beeztees in the field of automation. Throughout the whole migration Databalance has been involved and has advised and supported us. The result of the last months is a very modern and “state of the art” ERP platform based on SAP software and IBM hardware, which enables Beeztees to stay a few steps ahead of the competition. IBM Spectrum is based on software-defined storage and it enables users to obtain increased business benefits from their current storage products whether from IBM or another vendor. IBM has pioneered in this field since 2003 and supports more than 265 storage systems from several brands. This give you more value from earlier storage investments. Databalance is making use of the IBM Spectrum family in serving its clients. The IBM Spectrum Virtualize is a giving maximum flexibility and reliability by virtualizing the storage. You can also get more benefits by using features like Real Time Compression and Easy Tier. And of course you can create a disaster recovery environment by implementing remote mirroring. With IBM Spectrum Protect you enable a reliable, efficient data protection and resiliency for software defined, virtual, physical and cloud environments.

What is Business Analytics?| Career Growth in Business Analytics| Introduction to Business Analytics

Business Analytics in essence is talking about how do you use data how do you use IT and how would you use conceptual layer in business logic to understand how the problem would be resolved right so that in a nutshell would say that this is Business Analytics now you can refer to this as Business Analytics if you make it a little bit longer with some experience nudged into it it would be called as data science right so that in essence would be all about business analytics so I wouldn't restrict this field of study to a specific domain not that an engineer should only do it not that a BSC guy should only do it not that somebody who has come from a Beacom background or commerce background should do it in fact it can be done by anybody I have had students who have come from music from the music industry saying that they like creativity right so if you say that there is creativity in music the same exists in data patterns also right so it doesn't restrict us so anybody can learn it the only thing is that do you have the interest in you do you want to use it for your domain right so the application is varied it can be applied in pharma it can be applied in manufacturing consulting any domain you talk about it it is applicable the one important attribute a person should have is his interest in learning right so you should be somebody who is ready to learn so I wouldn't say although analytics is a complicated subject no doubt about that but I wouldn't say that you should restrict yourself by saying that I don't know IT i don't know statistics I don't understand anything about business so I shouldn't be stepping into this no I wouldn't say that is what stops you what stops you is whether you are ready to learn or not ready to learn right you should be able to adapt you should be ready to let learn you should be able to be agile and understand it right so that is only thing which is required in a student to learn analytic the core structure is with an assumption that a person doesn't know IT it doesn't understand statistics and has never dealt with business case study situations right that's the assumption right eventually people come with one strong background like somebody might be from an IT field is another person might be from a stats field somebody might just be into sales and marketing and he doesn't know anything about statistics or IT right but we can group this person up to a limit which is expected from the course right it's an advantage if you know something but that doesn't stop you from becoming a resource in business analytics that's how you would see it right so nothing is stopping you there is no barrier you just have an advantage you in fact it makes more sense for a student to learn it the reason being that we know that this subject this notion of analytics is obviously coming from the West they're in essence 80 to 90 percent of the work is done using analytics see over here do our jobs by our gut feeling right now corporates and vice presidents of major companies they have started thinking that yes we want to incorporate analytics in every decision we make a person who is just graduating may be doing his engineering college course or maybe he was doing his BAC course or be a become course or maybe even an arts course it makes more sense for him to learn this for a fresher it means that he is getting an edge or existing candidates within his college premises because he knows analytics and others don't know and frankly speaking today most of the engineering colleges and all these guys have started rooming their candidates on some aspect of analytics but the only thing is that it's restricted because of the curriculum which they can be exposed to it's more more of theory and less of practical typical data analysts in the US culture would typically mean a data scientist a short version of a data scientist whose job would be to read patterns into data nevertheless it doesn't mean that he wouldn't be working with data he would start from scratch from extracting data cleaning the data reading patterns out of that making sense out of that pattern and trying to put up the case in front of this know if I have to look at a data scientist specific job description in the Indian market per se it would talk about right from creating a requirement for analytics right because today the market doesn't see a requirement for analytics so you might have a company which is into retail which doesn't think about doing campaign based on the customers buying pattern but just as a campaign which is blanket I would want to customize this according to the requirement of the customer so it's a win-win situation for the company as well as for the customer in this case analytics would help the data scientist can be of great help to the company to reduce the cost of marketing increase the efficiency of marketing and this applies in every domain marketing sales HR right so going forward we can see an application wherein and data scientists would be working along with the HR team to actually analyze which set of employees should be retained for a longer period of time right so this would be some typical things which a data scientist would be doing for the company okay now if you look at a freshers perspective right first if she does some course which is about data science or about business analytics it gives him an edge to the companies when they are recruiting for a marketing professional also would expect that the candidate should know about analytics right today if a company which is in retail is looking at customer analytics they would obviously expect a guy who knows about marketing sales plus analytics they don't want a person who just knows one domain right so it's imperative that you will have to add up all the skills so do you see any cricketer today who can say that I can only Bowl No today we expect that a player should know bowling batting fielding everything which can be done on the field so that is a data scientist he can do excellent presentations he can talk in front of a big crowd he can work with codes he can work with data he understands database it's not that he will manage the database but he is in a situation to guide a database administrator also as to how the database should be for him to work in his favor so that's about the skill set level now from the freshers perspective obviously the first job would not be a very high-paying job but it will be reasonable somewhere around 4 lakhs to file lakhs that that's what you can expect if he is not from a very a great college right obviously if you are from an IIT or something like that or from an ion it gives you an edge it's because of the college brand now initially you will have to work with a lot of data so you will have to work with data you will have to extract data do all the preparation work on the data and present your report later on as you accumulate more knowledge you might get some more additional knowledge about how databases work so today we are talking only about Oracle data scientists might talk about no SQL Hadoop and all those things so he will enrich himself she will be an asset to the company and that is what a data scientist is about and that is what separates him and that is where he creates value for himself I wouldn't quote it to be the next big thing to happen because the next blip or the boom usually is followed by a drop right so this is not going to be followed by a drop this is there to sustain itself for a longer period of time the names might change we might see some more innovation right we are adapting right and we will be adapting more right slowly as we go forward analytics will be a common thing and obviously if it is becoming common thing it's not just a boom it's therefore staying for a longer period of time I would recommend students to take up a course like this irrespective of whether they see a career for themselves in an analytics or not the reason being that going forward no matter what domain you are working in analytics will come in play it will cross paths with you right and at that point in time if you don't know about it it's a problem someone should come to a mess pro school because it has a holistic approach there is a lot of content depth a lot of research has been done before the course has been launched it is followed by a strong examination path so which make sure that the student or the participant has thoroughly understood the subject so we have the NSE certification which comes with this so when you clear that it's an indication that yes you are ready for the industry along with that when you are taking this course but I am is pro school you are doing a practical mix of application along with the theoretical content which is taught in the class right and this gives you an edge over others today when I see a critical combination of something which is creative and analytical is nothing but a data scientist so here we are