MS Dynamics BI and Analytics: Tableau and DataSelf

Everyone, thanks for joining us. This is Johnny Girardi, Data Cell founder and CEO. This webinar is about Microsoft Dynamics products and we're going to be talking about BI and analytics. I'm sorry, BI and Analytics with Data Self and Tableau. The content we're going to cover today, we're going to start with some generic BI and analytics content. Then we're going to talk about Data Self approach, how we do it, how we cover it. Then we'll do a product presentation, we'll talk about pros and cons, and then we open a QA session. The way I like to talk about our story is pretty much covering how one of our clients went through this experience. So it's good to talk a real case scenario. This client of ours, a few years ago, the CEO called me and he said, Johnny, we are a very data driven organization. We like to make decisions based on facts. And right now we're struggling. We have several different data silos. We have an ERP system, we have a CRM system, we look at Google Analytics data, we have a different payroll system, we have social media, we have data all over the place with Excel files and access databases. And my management team is always waiting to get reports. There's a lot of people involved in putting the reports together. So it's very expensive to get data to my decision makers. It's usually slow. So quite often the decision makers have to either wait until they get the data to make a decision or they have to wing it. Either way, waiting or winging is not a good thing. I want to get out of this. Can you help me? So I said, well, I'm glad you came over here. Yes, absolutely. I think we can, based on what you told me. Let me go over some concepts of bi, how we can get you, or how you can get out of this situation into a better place, regardless of what Data Self does. So one of the main reasons why you're having so many challenges today is because every time you ask for a new report or a modified report, someone has to go three different phases. The first phase is what is called data preparation. So when you ask a new question, you usually have to tell a consultant, an IT person what your question is. And this person must find the data. Where is the data? What data silo is going to be looking at, what is the database structure, how you can pull the right tables and fields, how you can link them together. So finding the data in your data sources is typically a very complex job. Quite often every data silo has different people who know the data table. So it's not only one person who knows all your data sources. Sometimes you have several people to take care of that. So very complex and time consuming job to find the data to answer the questions. The second part as well. Now that this person found the data, found the tables and fields, they need to put in a report using Excel reporting services. Whatever the tool is, they need to build the grids, the charts and colors and whatnot. Building reports usually is also a complicated job that requires a lot of expertise in knowing the reporting tool. So again, it's a fairly expert centric task. The last step is just giving the report to the right people. So if this report is for only one person, it's easy. Just the IT person consultant emails the report to someone and you're done. Quite often it's not as easy. This report must go to several people, let's say division managers or maybe salespeople in every recipient they must receive their own specific information. So if I'm a division manager, only data from my division, if I'm a salesperson, only my own accounts, for instance. And then this job can also. The data distribution can become time consuming, error prone and slow. So these are the three steps required to get new reports completed. It is usually again slow and expert centric. Well, how can you get a lot of these steps automated, fully automated, sometimes completely automated? Well, the first thing is pretty much all organizations today, they have so many different data sources. So the first thing as a decision maker, you want to see data sometimes together, not from different places, but all together in a report, in a dashboard. In order to do that, quite often you'll need a data warehouse. A data warehouse is a database that has been optimized for reporting purposes. So you can bring all of your data silos, all data you need for reporting into the single data warehouse framework and do the data preparation so you can do the data blending. You know, you have multi company, multi currency. If you want to combine your financials with your payroll, with your CRM, with your sales, whatever it is, the data warehouse, you can bring all the data together, you can relate it. And now every time you need to pull a report out of the data warehouse, the data preparation has been done. It's automatic, it refreshes, you just need to drag and drop into a report much easier and much faster. So data warehouse is usually one of the best ways to tackle data preparation. Yes, it's very sophisticated to deploy, maintain data warehouses. Yes, sure it is. The beauty is once you do it, once you build the data blended consolidation, it becomes completely automated to refresh the data into blended. So it takes some time, you know, sometimes hours, days or weeks or even months to set up your data warehouse. But the beauty is it will be completely automated. The data preparation, once it's done and it's accurate, there's no error prone, it's going to be always the same thing done over and over by the computer. Then once you took care of the data preparation, then you need an analytics platform that will take care of your data presentation in data distribution processes. Now there are three main requirements for your analytics platform. First of all, people today consume reports in different ways. Some people are always sitting on their desktop, some people are working with notebooks, some people want to use mobile devices, some people want to receive reports by email or a blend of these things. So the first requirement of a good analytics platform is deliver information in these different devices, desktop web browsers, mobile devices, email and whatnot. Second requirement, and I think the most important requirement is ease of use. Most decision makers, they are not IT experts. So these people are selling things or building things or managing other people and whatnot. The easier the tool is, the more likely the decision makers will be empowered by the tool. They'll be able to license reports, they'll be able to drill down, download and even build new reports from scratch, anytime, anywhere, even on mobile devices. So ease of use is critical in the most important component of the analytics platform. The third is just speed. It must run really fast. Who likes to wait computers and phones and tablets to run? No one. So be sure that your analytics platform will be really, really fast. So this kind of framework will help you. I was talking to the CEO, will help you to get a lot of your challenges today. Very automated and hopefully completely automated. Now let me talk about dataself and what we do. I founded Data self back in 2005 and me and my core team, we've been working together for 16 years delivering enterprise grade BI for mid sized companies. When I found the Data self, the vision was quite simple. Decision makers in mid sized companies, they need a great solution that is easy and that is fast. But most options, most BI options available in the mid market, they are inexpensive but they are a little clunky, slow, not easy to use. And if you go to the Fortune 2000 world then you do find over there solutions that are really easy to use and really fast. So I thought hey, I can actually go and find the Fortune 2000 world technology that is really easy to use, really fast, really empowering. I want to make them simple, I want to make them better. I don't want you to break the bank as a mid sized company. So that's what we do. We simply bring best of the brick technology. Make it work for you without breaking the bank. That's our vision and that's our business. If you look at our technology today, and this is Gartner. Gartner is one of the most reputable companies doing technology assessment for Fortune 2000 companies. In every year they assess how well the Fortune 2000 BI vendors are doing. Make a long story short, Tableau and Microsoft have been leaders in the magic quadrant for several years in a row and that's what we bring to the mid market. Tableau and Microsoft technology, how does that work in this framework that I was talking about, the data warehouse and analytics? Well, what we do for data warehousing is we use SQL Server, which is pretty common, especially in the Dynamics world. Most companies that have data warehouse, they use SQL Server. However, we learned that building and maintaining data warehouses using programming such as integration services or stored procedures is very time consuming. It's slow and complicated. So we developed our own ETL tool and ETL stands for extraction, transformation and loading and allow us to build, build and maintain the data warehouse in a fairly code free environment. There is no need for integration services, There is no need for stored procedures. Yes, you can do it, but why should you use it if you can do it in an easier, better way? So we simplify significantly the data warehouse framework by adding our own framework that simplifies the whole process. Then on the analytics platform we've been using Tableau as the engine and I'll talk more about Tableau in a few slides. But Tableau has proven to be super easy to use, super fast and deliver information in all different formats. It's an amazing tool and I'll talk about very soon. Now not only we have all of these great software working together out of the box for Dynamics products and several other data sources, but every time we deploy the solution and we see a client building a report that could be valuable for our out of the box solution for other clients, we incorporate that report into the out of the box solution. Today we have more than 5,000 reports, dashboards and KPIs connecting data sources to the data warehouse as well as Tableau. So there's a lot of things as a starting point. So start from scratch building all your reports and dashboards. You'll get a ton of it. So you're starting a much more elevated level than starting from Scratch. So that's what we provide as an out of the box solution for your system. Now some people ask us, why did you choose Tableau? Well, we were working until 2011 with purely Microsoft BI technology on the back end and the front end and we decided to look for a replacement on the front end because we wanted something better. Back then, Tableau was not yet a magic, a Gartner Magic Quadrant leader. They were not yet. They were still in the middle of the pack. But when we look at them and we look at about a dozen of those companies or products, we found that Tableau was the easiest to use, was super fast, and their pricing model would fit the mid market budget. So we decided to embrace them five years ago. Now what we have found since then is amazingly how much easier to use it is than some many competing products. And the reason is pretty much because of the sentence. Actually the guys who founded Tableau, they were Stanford University professors and they were studying how business people look at data and have insights from it. It's a journey, it's a process, the discovery process. They hired psychologists, they hired graphic designers, and at the end of this study they realized that, huh, how about that? We can actually make a software that will help decision makers walk through this journey by themselves. And then they hire the software engineers. So they started with the real problem and eventually they hire software engineers to create a company around it. Pretty much all other BI companies that I know, they started with software engineers, thinking from the beginning, hey, I can do better, I can do faster, I can do flashier. And they created something while Tableau look at the problem, hire people to understand really well what the problem is, how to solve it. And then they hire the software engineers and this shows throughout their whole company, their whole technology. Major differences comparing to some competitors. Now let me give an example what Tableau does. If you're in the scenario, what should I do? We humans are very visual animals. We in this situation don't have to think what to do. I'm sorry, we just jumped out of here, right? If we weren't such a visual animals, probably we wouldn't have survived as a species. But here we are. What Tableau does is this without snakes, but with data. For instance, rows and column reporting. Absolutely. Tableau supports rows and columns extensively. And absolutely we do as well. So not a big deal from a rows and column standpoint. But if I ask you, how many nines are there on this table? If you're like most of us, you have to go to the rows and eventually you count the nines. What Tableau does is something like this. A simple easy change that will allow you to see in a few seconds the insight. There's about 10. So you shrink the time to get the insight so you have more time to decide and act on your decisions. If you look at table like this, product type, product markets, sales, gross profit, and I ask you, which market has the biggest sale, which product is not so profitable? Again, if you're like most of us, you have to go through all the rows until you come up with this insight. What Tableau does is something like this. Bigger bars, bigger sales. So central has the biggest bars across all markets. Easy to see that in profitability, red is also profitable. So in a few seconds you see which ones are not so profitable. Again, shrinking the time that it takes to get to the inside to have more time to decide and to act on. Now it's not about being just beautiful, is about being sightful. This is a business, after all, and you want to be sure that when your decision makers are looking at data, they have accurate insights. Let me show you what I mean about that. 3D charts, like in this case, a 3D pie chart. It's beautiful. Yes, it is. I love the look. But if I ask you which slice is bigger, the red or the blue? If you're like most of us is hard to tell because 3D charts can be deceiving. Tableau does not support natively 3D charts because they can be deceiving. So Tableau instead makes beautiful charts in 2D that will give you the insight in a very accurate manner. If you insist in seeing 3D, because whatever the reason is, it takes a couple of clicks to export into Excel or another tool and show in 3D. But for the insights, Tableau does not support natively 3D charts. It's really important that you go to the, to the inside, not only for the fluffy view. Now, some people ask us, after I go to this presentation, so if Tableau is so awesome, why do I need data self? It's a good question. Well, this is the scenario where you have your data sources, a lot of data sources, and then let's say you have Tableau. Absolutely, you can buy Tableau or you can buy competing products that do the analytics and then you start building the connectors from one system at a time, usually from the ERP or the CRM or Google Analytics. And eventually, if you look in the course of a few years, a couple of years, you're going to be building a lot of things, a data warehouse and Connectors and reports and dashboards. It's going to take you time. You're going to need to have expertise in Tableau, which is easy, but it's going to take some time to build it and build all of these things in between. And if you build a data warehouse, which you're likely going to be doing, you're going to be probably doing through programming, slow, expensive, complicated. What Tableau does, I'm sorry, what Data itself does is shortcutting the time significantly to get value from Tableau. So we add a data warehouse, comprehensive, out of the box, already pulling a lot of data from your erp, CRM and whatnot. We use our etl, which eliminates programming in out of the box. There's a ton of reports to get starting. So without Data Self, you do everything from scratch and you can do it with some time and the right people, but we can shortcut the time significantly and you'll know for a certain amount of money how much value get out of Data Self. So that's in a nutshell, why Data Self on top of this? Alrighty, so let's go for the product presentation. I'm going to be talking actually about the scenario of this company that I was talking about. The CEO called me. The CEO goes to these board meetings every month. And in the past he was bringing a stack of paper for the reports that were most commonly discussed. And he told me, johnny, I want to go with my iPad. He used an iPad. And I want to be able to change, slice and dice the data and answer a lot more questions. Because sometimes, actually quite often, the board asks me questions that I don't know the answer. And what I'm going to show you today is how it's working. Today I'm going to cover a few reports that actually he built himself. One of them was showing this month's sales details. This company is a distribution company here in California. They sell across the whole US and Canada. So they want to see sales by customer, by month, by invoice number, invoice, date, product, what not. So I'll show you that how he built a report. Then they do marketing, sales, marketing campaigns, and they want to see how the website visitors are influencing their sales or the relationship between website visitors and their sales. Then he also wants to see sales by state and how large the deals are by state. And then I also want to show some other reports he's using today in his presentations. So are you ready out of the PowerPoint? So what I'm going to show you now, I'm sorry. Is the Data Self analytics desktop tool. And most of the things I'm going to be showing you on the desktop tool can also be done using web browsers or mobile devices. So keep in mind this is the same thing you can do. Use it, let's say your finger if you're using a smart like a tablet or smartphone. And when you go to, let's say to build a new report, which right now I'm going to be building these first three reports here. The first decision you have to make is which connector you're going to be looking at. And here we have a few connectors, but you can have as many connectors as needed. In Data Self these connectors will usually come from the data warehouse, but they can also connect straight to the data source. Then as a user you just pick a connector. In this case is a sales analysis report. I want to pick the revenues connector then. Now what I need to know is to drag and drop and point and click and I'll be able to build a lot of reports. My first report is showing sales by customer this month, actually last month. I want to see customer products invoice number one not. So what I have to do is I have to find my customers, which is right here. Customer name I just drag and drop into my report. So these are all my customers. There's about 1,000 customers. I want to see sales. So I have sales just drag and drop sales to my report. So sales by customer in the whole company history. Well, I want to see last month sales. So let me take my invoice date and drag into my columns and right now I'm looking sales by year. Well, I want to see sales by month. So let me expand from year to go to the quarter level. Expand quarter to go to the month level. I could go to weeks, I could go to fiscal period. I can go to hours pretty much. We build these time buckets in the data warehouse. That's the technical work usually. And once they are constructed, then any user will be able to drill down and slice and dice the data by just dragging, dropping the reports like this. So very easy to drill into different time buckets. Well, I have a lot of data in my sample. My last data is October. How can I see only last month sales? Because when the CEO goes to the board meeting, he talks about last month sales. Well, I'm going to take my invoice date, drag and drop into my filter shelf. I could select a different time bucket or choose relative date and then I'm going to choose month and see previous month and click. Ok, so there it is. Now I'm looking at sales in October. Now the beauty is once I have this filter, if I want to change, I can come here and say show me the last three months, which would be November, which I have no data. October and September or maybe the last four months. Very easy, very simple for users to change the filter and get exactly what they want. All right, now I just want to see last month sales, but I want to see sales by product. So how can I add product? You just have to drag and drop. I find product here. Here's product. I drag and drop between customer and sales. So by customer, by product I see sales. What if I want to see invoice number, invoice dates, it's the same thing. You just have to drag and drop. Invoice number is here. I drag and drop between customer, product, there it is. Invoice dates is here. Drag and drop between these two. There it is. Many of our clients, users, they have learned how to build reports from scratch or slice and dice reports by watching a presentation like this. This tool is really intuitive, it's really easy to use. On average, 80% of the reports that people need. It takes little training, very little training to accomplish those 80% of the reports. The other 20% will require a lot more expertise, a lot more training. But a big chunk of the reports can be done by users with very little training or no training. If you are someone that watches this and experiment, you quite often can learn how to use this tool even without training. That's myself. I haven't been good through training with tableau at all. But I'm an expert because I experiment and eventually I become a power user, an expert with tableau. All right, second report, I want to see marketing versus sales trends analysis. So I'm going to build a new report. And now I want to see by customer address. Could be shipped to anything that has an address I can select. So customer address. I want to see the invoice sizes and then sales and I ask the tool, show me this on a map and there it is. So what I'm seeing now is bigger bubbles, bigger sales, and the bubbles are sliced by large and medium deals. So in a few clicks I was able to see this nice report showing throughout the whole US and Canada how the sales are doing. The next report I want to see is marketing sales. Actually, I showed the original sales by deal size. So sorry about that. Now I'm going to do marketing sales trends. This company that I'm talking About they were taking website visitors from Google Analytics and they were combining with their sales. And every time they were doing that was a lot of work from their marketing team and their IT team to copy and paste into Excel, massage the data, plot it and whatnot. It would take about a day and a half of work to get it done. Well, my team worked with their team to bring this data together. The data warehouse once is in the data warehouse and is being refreshed automatically. Now everyone can build those reports and slice and dice them. The CEO actually build a report. He said, hey, I want to see sales. So he double clicks sales by invoice date and I want to see sales on a monthly basis. So we're now showing the whole company history, sales on a monthly basis. Then I want to add website visitors. Well, since it's already in the data warehouse, is already in the revenues connector, it's just right here is another measure. As a business user, I just have to drag and drop. So these are what my website visitors, I want to see bars. So they are different than my sales. And then I want to put them on a dual access approach. So website visitors are the bars, sales is aligned. Now I can make an analysis of how my website campaigns and my website visitors may be affecting my sales in the long run. Very straightforward report. Beautiful. So the CEO actually built these three reports himself. Now they're completely automated. Once you build, they become automatically refreshed. And he wanted these three reports in a dashboard on his iPad. So he went and created now a dashboard and he said, hey, I want to put my map at the top of this dashboard. I'm going to put the trend analysis at the bottom and my detailed report on the far right. Just drag and drop. And he built his report from scratch. Now one of the interesting things is when he goes to these board meetings, sometimes a board member would say, how are we doing? California, Texas. In the past the CEO would have make notes and I'll get back to you in a few days, go through the report, ask his people to build a report and send to that board member. Nowadays on his iPad he says, ah, sure, with his finger he select those two states and he lets it go. The whole dashboard changes. And he can take this insight on the spot, on the fly, export to an email and send to the person to the people who want to discuss that. Actually some of the board members are also using tablets. They can also access the information right there during the meeting and also slice and dice. So it's amazing how much more productive their discussions are because they have access to data at their fingertips. And it's really easy to use to publish this report to the web portal, to the web server. It takes only a few extra clicks, like two extra clicks and you're done. So it's very straightforward to build and to share information with other people. And once you do that, the data is always available to the right people based on their user credentials. Now how most people will access these reports, well, they will usually go to a web portal using a mobile device or a web browser. When they log to this web portal, they will only have access to the reports that they have rights to. If I'm from the finance team, I only see financial reports. Let's say if I'm from sales, I only see sales reports. If I'm the admin person, I have access to everything. Not only that, but let's say if I open a report, let's say this report here, this report here is a sales report. And the security can be built in a way that is the same report for everyone. However, it can be contextual. So if I'm the rep for California, I would only see my top customers in California. The rep for Florida only views the top customers in Florida, while the VP of sales opens the same report and views the top customers across all territories. So security can control information even at the most granular level. And that's very easy to set up. Let's put some financials here. What if I open this financial report here? Suppose I'm the CFO and I'm analyzing some KPIs across my different offices or territories. And suppose I want to drill down into more details. Well, reality is, many users, they will not learn how to customize reports, they're too busy. Even though it's easy to learn. But thank God they're too busy, they don't want to. The good news is the people who build the reports, they can actually create drill down options, slice and dicing options that are very popular. So people who are less sophisticated with the reporting tool, they'll be able to make those changes, slice and dice the data without learning anything complicated. They can pretty much do it with almost no training at all. So let's say suppose in this report I want to go into one of these office and drill down into something else. When I build this report, I could, you know, the person who built the report could make a way that when I click on a bar, it goes straight into a drill down into that particular piece of information. Let's say from Atlanta, I could go to payroll in Atlanta or CRM in Atlanta into any report from the Atlanta office. And I can do this because in the data warehouse all the data is together. Doesn't matter where the data comes from. I can go from a financial report like in this case into anything else. In my demo here when I click this Atlanta bar, it actually gives me two drill down options. I could have 3, 5, 10, as many as I want is as a user, as a non sophisticated user I just have to pick which drill down I want to go into. So like say I click balance details and now I go into a completely separate report. In my sample is still a financial report but as I said it could be anything else as long as the data can be blended together in the data warehouse. People can navigate from one point to another just by clicking and here this report continues to be dynamic. Now if I am a power user, a sophisticated user, suppose I'm looking at this report here I have segment one, segment two, bunch of details. Suppose I would like to see segment three GL segment three in this report. Well, if I'm a powered user I have the edit button and now I can change the report. I go into the edit mode, then I pick the report. So monthly balances and I have gl. Let me go to yearly balances. Actually I have segment two and one here. In some KPIs I want to see segment three. Even if I'm using a tablet or smartphone I could use my finger pick segment three and drag and drop my report. Maybe the beginning. So now I have by segment three that report. Or maybe I want to put the segment three at the bottom in the middle perhaps just drag and drop. And now I have segment three where I dropped. The point is this tool is really easy to use if you know how to drag and drop. You can learn how to do a lot of reports into this framework. It's amazing user, I mean amazing user friendly tableau. Did a great job studying the decision making process and creating a framework where non it people can actually be empowered by this amazing data visualization tool. All right, so let me go Back to my PowerPoint here. And go for the next slides. I hope you have enjoyed the product presentation. Let's talk about some pros and cons of this kind of framework. First of all, data warehousing. Many people, they have a bad taste of data warehouses. Complicated slow failures. In the Fortune 2000 world, that happens a lot. But let's talk about it. So let's talk about some cons first. First of all to build A main take data warehouse, you will need skill people. So it's a cons, you have to find them and most importantly, you have to retain them in the long run. Because if you are building data warehouses, especially through programming, it's great if you have the same people to maintain it. Otherwise going to be a lot of time rewriting things as you move forward. Second is a lot of the data. The data warehouse is not real time, which is good for many things, but sometimes it's not. So you need to know that with the data warehousing, some data, most data is not real time, but yes, you can make real time as well. And typically it requires additional investment. So you have to put more time, more money into it to make it happen. So those are the main cons for data warehousing. Where are the pros? First of all, in one of the most important ones is the single version of the truth in the data preparation process that I was talking before. In the data warehouse, you bring the data together, you build your rules, how you want to see your financials, how you want to see your gross profit, how you want to see your payroll KPIs. You build those rules in the data warehouse, you do through a vetting process. And once you do it, they're going to be always doing the right calculation. And when it comes to build report, you just have to drag and drop them. So you're not going to have situations where you go to a meeting and people are coming with different Excel reports and whatnot, and you are not sure if those different reports are using the same business rules, different versions of the truth. With a data warehouse, you can guarantee that you'll have in a much better framework a single version of the truth. It's a single repository from all data silos. You bring all of your data, all the data silos that you want into the data warehouse, you blend the data and now is all your data in a single place and you can slice and dice it. You can drill down from one point to another without going to different people and different systems. It's an amazing benefit to get all the data together. The other is with data warehousing, you report to run much faster, very fast. And usually with the data warehouse, it's much easier for your users to become empowered and being able to slice and dice the data because the data warehouse will be easier ways to do reporting. So those are the most important pros and cons of data warehousing. How about Tableau cons? Well, Tableau is a standalone product. It's not built into any other tool. So if you want to see tableau into your erp, into your CRM, into any other system, you'll have to add the web views into it. It's possible, but it's not out of the box blend in. So some people consider that being a con for certain clients. Some popular visualizations are not built in. If you want to see 3D charts, it's very hard. You can do yes you can, but it's very hard. It's easier to do in Excel, for instance. So if you want to have 3D charts, you know tableau does not render them easily. So some people consider that a conversation. And it's not inexpensive, it's not expensive, but it will require some additional investment, especially comparing to some other BI tools out there. Where are the pros the most important and what I think is the most important across all BI client tools? Ease of use. You need to be sure that your users will be able to embrace your BI tool, will be able to access reports anytime, anywhere, drill down, slice and dice, download data, and even build new reports from scratch. And in that front, for the past four years in a row, Tableau has been considered the gold standard for analytics ease of use. So to me, that's the most important benefit of tableau being ease of use speed. Tableau is super fast. Tableau focuses on beautiful insights. It goes along with the ease of use, but again, it will focus on giving your users accurate, beautiful insights. And finally, being a standalone product is actually a benefit in my sense because it will easily allow you to insert tableau views into your ERP system, into your CRM system, any system. You can actually incorporate tableau views and make them very much part of the flow workflow of your people. How about should you buy a solution like Dataself off the shelf or should you build it from scratch? Well, if you build your own, the cons are slow. It's going to take you a while to build a comprehensive solution, typically months to years to cover your I don't know AP, AR financials, sales, payroll, inventory, purchasing, CRM, Google Analytics. You will take a time to put all the pieces together. You'll be spending a lot of money in people to put it together. And if you go to this route, be sure that you find and retain skilled talent. You'll need them. They might be expensive, but you'll need to be sure that the system will be up and running all the time. So those are the cons of building your own. And I mean this is kind of challenging Nowadays it's challenging to find the good people and even more challenging to retain them. So I'm putting as a con because it's usually a lot of work to find the right people and retaining them. What are the pros? Well, from the beginning the solution will be specifically designed around your needs and you have more control. So there are some good benefits. What about building off the shelf solution and then customize like from Data Self Cons? Well, from a license standpoint you pay more money, so there's a higher initial cost at the beginning or on the licenses side and you have some less control because you cannot pick whatever you want. You have to go with the framework provided by this off the shelf company. And if you're buying from an off the shelf company like Dataself, be sure that this company provides best of the breed technology like Dataself. We use Microsoft and Tableau, which are one of the best out there. So be sure that whoever you're buying from is using best of the breed technology. Pros is fast. You'll get, let's say From Data Self 5000 reports, a comprehensive data warehouse that will cover a lot of things from scratch. I mean once it's deployed, very fast to get a lot of value out of it. In the long run, to maintain this technology, let's say from Data Self, you don't need programmers at home. Even if you have change in your people, we can help you maintain as you upgrade your dynamics in other systems. So overall in the long run it will be a lower total cost of ownership because a solution like ours is less programming. It does a lot of things automatically and it reduces your need to retain and find people that would do otherwise. If you go your own way, it is completely customizable. So we can be as customizable as build your own. And we use best practices. We only do BI analytics. So we build a framework and we can teach you to follow a framework that will allow you to take the most out of this investment. So those are the main pros and cons of building versus buying. Finally, deployment approach. Nowadays a lot of people talk about public cloud versus on premises and how BI works in that sense. Let's talk a little bit about that. By the way, we provide both cloud and on premises. What are the main cons of public bi? So for many people it's just a legal problem. They cannot put their data, some of their data in the cloud. So right there, if you have some legal issues of putting your data in the cloud, a BI public cloud is out of the equation if you don't have a legal problem and be sure to check with your attorneys if you don't have a legal problem. Most of the other people, they complain about security problems and security is a double sword discussion. The cloud is supposed to give you the best security because they are maintained by expert security. And if you want to have equivalent at home, you have to hire equivalent people. Security is a hot topic and I'll talk a little bit more after I cover a few more things. Control. In the public cloud you don't have as much control because they manage the solution, they upgrade it and you just have to go along with it. And the other problem is uploading your data. If you have on premises data to the cloud, it's quite often slow and quite often you can have reliability issues. So for many people, uploading becomes a hurdle for having BI in the cloud. For some people it's not a problem. But for many people, these five topics are the ones that I hear the most people saying why they're not moving to the cloud. Where are the pros? Absolutely. It's lower initial cost, usually can get up and running very quickly, very inexpensively. In the long run, if the cloud works for you, it will have a lower maintenance cost because a lot of the infrastructure whatnot is maintained by the public cloud provider. So pros and cons, you have to see your case on premises or private cloud is the competing approach. The cons, first of all is higher initial cost than public cloud and in the long run it will also require more of your efforts to maintain the solution. If you go private cloud is lower, if you go on premise higher. But overall compared to public cloud, BI in the long run is overall a higher maintenance. Where are the pros? Security and I'll talk about that in the next section. A little bit more you have more control is everything. You can decide when you're going to be upgrading, when you're going to be applying things and changing. You have complete control. And the other main benefit that for many people is a big thing, is uploading data to your framework because you can put your data warehouse closer to your major data sources. It's very fast and usually very reliable. So those are the main points when looking BI in the cloud. Public cloud versus on PREM or private cloud. Now, security, it's a topic that I've heard people defending on both sides of the why security for public is better than on PREM or private or vice versa. I'm going to give my opinion, which I think is an interesting perspective, which is the following if you are targeted to be hacked, if there's some kind of large organization out there, a country, a large organization trying to hack you and they're going to put a lot of effort into doesn't matter if you're public or on prem, you have to have the best people security to prevent that from happening. So if you are targeted specifically by hackers, hire right people, good luck. I think you can do it, but it will be a lot of effort. We have seen so many large organizations with very reputable security frameworks being hacked that if you are targeted, good luck. Now most organizations, they are not heavily targeted specifically heavily targeted, they're just targeted randomly by robots out there trying to find flaws in your system to hack into it. So you are like most of us that follow this category of being hacked by random hackers, then my opinion is if you are in a public BI cloud like let's say there are many vendors out there that have a public BI cloud, then there is a public IP address that has a lot of BI data. So if I were a hacker and I want to go out after BI solutions to find flaws in tableau, in Power BI, in QlikView, whatever the BI solution is, if I find a flaw in this BI solution and I know the public BI framework, I can hack that IP address and get all the data from let's say all companies. I'm just making scenario. So having a public ip, it's a very big issue because it can be hacked across the board because of that. Now if I have a private cloud or on premise cloud, my IP is not public. So these random hackers just seeking for flaws in the system is much harder for them to find my ip, much harder for them to find what technology I'm using to prevent hackers from hacking to my system. And it's so much harder to be hacked. So anyhow, I think from a security standpoint, the on prem and private cloud is safer just because of human nature and technology nature. If you are being hacked specifically, you're being targeted. Doesn't matter. You'll need to be on top of the things. If you're like most companies who are randomly hacked, I think on prem and private cloud are still a safer option. So I hope my opinion can be valuable for some of you already. What's next? Well, if you send us your data, we can do a test drive with your data and we can show you how easy it is. The data self solution with tableau to slice and dash your data. We are offering right now a Year end incentive. So if you place orders by the end of 2016, there's some discounts. So if you are in the shopping and you want to do before the year end, please contact me so we can help you get the best deal out of this solution. I'm going to open questions right now and let me check here if we have any questions. Yes, we have questions and the first question is from Sean. How do you compare Data Self and Power bi? Well, that's one of the most popular questions that I get when I'm talking nowadays, especially in the Dynamics world. So let me go here first of all and address this question this way. Let me just put here first of all, generically speaking, if you're considering using Power BI for your BI solution, the first thing you have to do is you have to connect your Power BI to your data sources. Yes, for Dynamics products, there's already many things covered out there, so it will shortcut you the time to get Power BI data from your source systems. But if you go with Power bi, eventually, after months and years of investment into Power bi, you'll be building eventually a data warehouse if you don't already have so, and you'll be building a lot of reports out of the box. The first main comparison between Dataself and Power BI is that we provide out of the box a very comprehensive data warehouse that covers Dynamics, ERP and CRM. So right there alone, dataself has a lot of value to add. If you want to use Power BI on top of our data warehouse. It's a great solution. It's much better for many people than building the data warehouse from scratch. In this sense, Data Self actually helps Power bi. And I'm excited that Power BI is becoming such a strong product because I believe at one point we'll also incorporate Power BI as part of our offerings. But still, at this point we are complementary, giving Power BI a very comprehensive out of the box data warehouse for your Dynamics ERP and CRM. And we even have other data sources that can be combined. Let's say if you have a multi company system with Dynamics, maybe some Sage and acumatic and whatnot products, our data warehouse will easily allow you to blend all this data in a single data warehouse for multi company analysis. Let's the next in the back end we are complementary to Power bi. Now on the front end we are supporting tableau. We still believe that tableau in so many instances is so much more refined, easier to use and powerful than Power bi. We have done not a lot of assessment in Power BI yet, but we've been around, we've been hearing other people using it, we've been reading a lot of posts and whatnot. Power Bi, in my perception, still is not ready for the kind of product that we're looking for and our clients are looking for. As Power BI matures and gets better, faster, more comprehensive, we might definitely embrace it. At this point. We believe Tableau is easier to use faster and more mature. Folks, we're running out of time. We have more questions, but I'll have to answer to you on a 1:1 basis. Let's put my slide here. I hope you had found this webinar informative. I just want to show you one thing very quickly. If you go to our website dataself.com click Solutions you can go to this 5000 KPIs. They will list all the out of the box solution we have with all the reports available. If you click Dashboard Samples you go to this other page that shows you a lot of reports we have out of the box how informative and insightful they are. So feel free to explore. Our website has a lot of materials also. Where is it? Demo Videos is another page on our website that are very popular for people trying to learn more about data self. I hope you have found this webinar informative. If you want to talk with me or discuss any of these things we cover or anything else around the analytics, feel free to call me. This is my cell phone, also my email. This is our website. I hope you have a wonderful Thursday and looking forward to being in touch with you again in the near future. Thank you.