 Hey, this is Christian Buckley with another MVP Buzz Chat, and I'm here this morning with Reza. Hey, good to see you. Hey, Christian. Good to see you. Thank you for the opportunity. Yeah. So folks that don't know you, who you are, where you are, what you do, why don't you give us an introduction? Sure. I'm Reza Rad. I'm based in New Zealand, the other side of the world for most of people. And I'm a data platform MVP from the time that it was more like, let's say, focused on SQL Server. My activities was like BI related. These days is more like Power BI, the BI part of data platform Power BI. And I'm also a regional director. I do consulting and training on Power BI a lot pre-COVID-19, speaking in many conferences. These days is speaking as well, but mostly to my camera. Yeah, online. Yeah, it's an interesting transition to that. But something that's interesting though, folks that I know that are like Power BI experts, you really see kind of a split of their MVP focus for those that are MVPs. And I know that there are RDS that cover multiple tops as well, but you see MVPs that specialize in Power BI that are business applications MVPs, office development MVPs, and data platform. Is there a difference between those? I mean, because I look at it from the outside as a non-developer. I'm an office apps and services MVP, focused on productivity. My primary customers that I work with are business users. It's more of the user experience and the front of the office. And so what's the difference between, you know, the MVPs within those focus areas? If I look at it as like y'all kind of do the same thing. Yeah. The thing is that like Power BI formally is under the data platform category. However, because it is related to many other things, it is related to dynamics. You see business application MVPs focusing on that. We have a lot of MVPs who came from like office and SharePoint category also to Power BI side. We see a lot of Excel MVPs nowadays working with Power BI because it can like serve all of those like analytics need that we previously did it with Excel, with performance point in SharePoint, with let's say SSRs in SQL server. All of those now can be done, let's say much better in Power BI. So we have like people coming from all different kind of like expertise into Power BI, which is quite a good thing to have because for example, I'm coming from like database development background. So I know that area good and I can add it into my Power BI experience. Someone who comes from let's say SharePoint has the good understanding of like the digital platform and things around it, which can help in implementing Power BI. So altogether that let's say collective knowledge of all of these MVPs coming from different areas and working with Power BI, I say that is a really good thing to have. This helps a lot in adoption of Power BI. Yeah, agreed. I guess for people that aren't that familiar with the MVP program as well, they do kind of shift like the areas of focus or the titles of the MVP. That's why a lot of people just say default is like I'm a Microsoft MVP because the reality is that you might be focused or your MVP may have been awarded in the Power Platform. So a business application MVP, yet you may also be a SharePoint exchange person who also does office development, you might work for an ISV or a mobile development company and are doing things in a number of different areas. So once you're and I guess it's an important distinction, it's like once you're an MVP and you're in the program and it is an opportunity to go much more in depth and get an inside perspective on many other areas as well. Great. Yeah. And we have... Yeah, go ahead. A lot of like dual MVPs, like people who are working at like across multiple... You're seeing that more and more, in fact, dual and even some triple MVPs. Yeah, yeah. Those are the people that have no free time. Busy all the time. They're single, they're only at work. Yeah. Well, you've been an MVP for a few years. How many years have you been an MVP? 10 years. 10 years. 10 years. Yeah. Congratulations. Did you get your blue ring yet? No. I think the shipment to New Zealand takes a little bit longer time. So... Yeah. Well, yeah. In the COVID period. So it's probably on a boat. So you should get it in 12 to 18 weeks. Yeah. And we have quarantine here as well. So... That's right. Well, that's exciting. Well, so what are some of the things that you're actively talking about and presenting on these days? Yeah. As I mentioned, it's more like Power BI focus. And this is like a wide range from visualization in Power BI to data modeling, to data preparation, architecture of the whole implementation, like putting different bits and pieces together, how to share, let's say, content with the users and users or all other types of users, how to put together a self-service architecture and things around it, all of that together. But what I have seen most of the requirement is in my, let's say, consulting works and coaching works that I do for a lot of clients is that they have most of their needs in putting an architecture together. Let's say we want to adopt Power BI, we want to implement Power BI, but we don't know, let's say, which architecture we should use, should we use live connection, should we have our data modeling SQL server connect to that, or we should build everything in Power BI, what should I do with my self-service users? Things like that is one of the areas that a lot of people need help, which I do as of my, let's say, architecture advisory things and the data modeling calculation and data preparation. The visualization part is also important, but the visualization is something that you get to learn it quite fast, especially the amount of content available in the internet, either free content like YouTube and things like that, or paid video courses. There are lots of courses, which if you just go ahead and go through some of those, your visualization knowledge goes up quite fast. But the other two areas, the architecture and the modeling calculation data preparation, are, let's say, it's like the iceberg, that's what's underneath the water. Exactly, it is. So I've been through, because we have the background, the first half of my career was in the data warehousing space and worked in the telecom world, worked for very large phone company in the US, formerly Pacific Bell, lots of acquisitions that happened since then, and doing data center consolidations and dealing with massive amounts of data is people, I think, don't fully understand the generous amounts of data massaging that has to happen to be able to do that pretty application layer, that presentation layer of, you know, not that Power BI is just a presentation layer. There's a lot more that it can do, it's an analytical tool, but yeah, there's a lot to kind of get it to that point. I think that it's almost deceiving when you're watching a demo or something of a lot of these tools, and just go and connect into this data and like, oh, hey, and I'll just point to it and click, and there we are, and look at that beautiful graph. There's a little bit more that's involved there. Yeah, that is mainly showing, as you explained, like the tip of iceberg, but let's say 70% of the things that is under the water, those are like something that people need to focus more, and those takes quite a bit of time to learn. There's a learning curve for that. Well, I do like how Microsoft is focusing more and more on creating experiences that are almost, I'm thinking of it as like a smorgasbord of a restaurant, of options, a buffet of options, of providing some sampling. For example, the expansion of ideas in Excel is a great way for someone with zero experience with the data, but may have a fairly complex spreadsheet of data at the beginning of their data management process, but they can use ideas in Excel now and it will actually leverage some of those visualization things and create and generate like the starter visualization of that data, which gives you an idea of what can be done over in Power BI. Obviously, you're more advanced. It's a big step up to jump from Excel to Power BI with some of those capabilities, but at least to give you an idea to start thinking about, hey, this is close to the graphical representation of this data that I need to see. So when we build the dashboard, this is similar to what I need to see. I don't know if you are spending a lot of time building those kinds of solutions if you've started using it with any of your clients to get them thinking about that. Yeah. In my, let's say, work experience, we don't really build that much of solutions, but those solutions are really helpful. Like there are lots of template apps that you can use for different, let's say, scenarios for financial scenario for different scenarios, which are really helpful. I've seen a lot of our clients actually go ahead and use those at their beginning type of work and then extend it at some area rather than reinventing the whole wheel. My part, when it comes to, let's say, working with clients, usually comes after when they come to, let's say, customize it, because then they want to do some extra analysis. They have a question in, let's say, DAX calculation or how to bring their own data combined with this data and the architecture piece, as I mentioned. So those are things that I usually help my clients with, but those templates apps, those pre-built templates app are definitely a great first step to get things rolling. Well, one of the things that I really liked about, well, let me jump over to another idea, I'll come back to that other thought, but one of the, we spent so many years struggling with the capturing of data. So having worked, so my first 10 years of experience working in technology where so much of it was about capturing that data and do we have enough storage for this and we're now going to be merging these data sets and upgrading the hardware to be able to handle that. And then there just was this decreasing cost of the storage and suddenly we had the ability to inexpensively store anything and everything. And then you start having big data problems of we've got all this data, all this transactional data, this live data that's coming in and what do we actually use? What do we need? How can we better leverage this? And we're coming into an era where I think more and more organizations are taking the time to think about and really look at their data assess and say, how can we better leverage this to do more to go faster to decrease costs? Where 10, 15 years ago, I mean, we just weren't able to do, we were just trying to keep the servers and the primary systems up and running and lights on on the servers. I don't know if that's, if you look at the world the same way, but we just seem to be at a kind of a golden era for data analysis, certainly knowledge management in my world. It feels like that we're finally able to do a lot of what we dreamed about doing 10, 15 years ago. Yes, definitely. That is actually a good era of data to be in because as you mentioned like decades ago, we had scenarios of let's have this operational system, let's gather the data from let's say everywhere. And now we have the data. We even have like in many scenarios that I work with clients, they have more data that they can analyze. Like they say, let's put this part of data aside, we'll just analyze this part and then later on we'll bring that part. This means that we have like so much operational systems, so much data sets, so much databases that can be analyzed, but probably like they are all scattered around. The, the thing that we had with like master data management again, few years ago, and let's say having things into let's say an enterprise data warehouse, those helped to have something like a solid one place to bring all of these things together. And that helps a lot, but definitely this is an era that we have, we don't have let's say that much of problem of getting the data. Data is already populated through different systems. We just probably need to integrate it if the integration haven't been done already and start analyzing it, which that analysis can be different, depends on how the data is integrated and things like that. But one big challenge of not having that data is already solved, which is a big challenge always for the BI systems or even for let's say data science systems when we talk about like finding algorithms in the data, finding patterns in the data. It is important that you have quite a lot of data already gathered, which is something that we already, I guess, passed that step for many organizations. Well, and it probably helps that we share that we're speaking in generalities and share some examples like I spent a few years working the supply chain and telecommunication spaces. So you think of supply chain where you have, you know, your ERP data, so all of your products, all of the components, it's state within the manufacturing process, logistics data, geographical information system data, like working with the phone company. We, you know, we, I did played a lot with the integrating GIS information so that when you send people out in the field and they're digging up a cable to repair a line that they, you know, know exactly where to go to dig, you know, geographically and but having all that information as well as, you know, your customer data, all their demographic, psychographic information. So you really understand your customer, what they're buying and of the products they're buying, the state that they, those things are and the product and being built and delivered and kind of all those things, massive amounts of data, massive amounts of data. And then you just had that little issue of needing to bring it all together and get something, some interesting insights out of that little job of pulling it all together. Yeah, that's a small little job. But it's yeah, it used to be though, like we'd spend all our time just with how hard it was to capture that and store that and connect those. I remember getting in the mid 90s requests from our internal customers for data, we're like, you know, you're asking for things that are across three different physical data centers and systems. It's going to take us a while to pull that together. And then to query it the scope of the query, what can we do to bring down the scope of that query so it doesn't take seven days to run a single query kind of stuff. And we don't have those problems in the same way. I know that those still exist, there's still those kinds of queries, but mostly, yeah, we don't have those, especially like master data management. That was like a big challenge always when we did data integration. We had like coming data from different systems, like what you mentioned, and there was no single let's say version of throughs. So we need to have like, let's say a few people to take care of like these. This is right. This is not right. This is things like that. So those are much better implemented these days. But still, there are many companies who don't have that. I mean, it's like an ongoing process. People are adding things into that, which is great. But all of these are, let's say, big fundamentals of then having that little analytics at the top. It might just be my perception, but people aren't throwing away data anymore. I think that's a, you know, I think again, because of the it's so inexpensive just to store everything and like, we don't know what we're going to need later. But are people getting sloppy in their, you know, capture and keeping holding onto data? Yeah, I think because probably because of storage is is not expensive these days. And analytics is expensive. However, like you need servers with like good CPUs, good memories, like, or use good software as a service providers. But the storage, I mean, if you just store things, storage can be also expensive. But if you just store things, this can't be, let's say, that much expensive. And I guess one of the reasons people tend to keep it is that they want to go like 10 years back data and see what they have been doing at that time. Let's now compare it with the trend that we have now. Well, I guess that's why you it's, it's good to have people that own to know that data. And so part of that process is, you know, what needs to be archived, know what needs to be purged from the system. And certainly there's certain data types where there, there should be expiration dates that can actually be a, you know, a, it can add unnecessary risk to operations on going like by holding on to this, this data. There's, you know, chance that data could, you know, be lost or that it could be the wrong data leveraged in where it should be newer and and skew results. I mean, there's a lot of things that the reasons for keeping your data optimized. But, you know, it's, I know, I'm just, I'm not working directly within that space anymore. I don't get to have those conversations to keep up to kind of where things are. And so again, I just have that, that that kind of perspective that, well, when I, when I got into the Microsoft ecosystem formally, so back in 2005, and I was my first MVP was a SharePoint MVP and so I was in that space. And my observation was that, you know, in the Microsoft world, there really aren't DBAs that the way that I had experienced elsewhere. And or else they wear different titles. It's just not the same. And so it was just very different for me. One of the things that I recognize that when Satya became CEO and really started to push the product teams to be more, you know, data centric. In decisions that are making about products to understand how the various products were being used. So when you go in and work with a client, I mean, what's your conversation with them about optimizing and leveraging that data and being becoming more, you know, data centric in the way that they work. It all starts with, let's say, focusing on a specific data analysis scenario, because usually they have like 10 years of data, 15 years of data, some of them like over 20, 30 years of data. And the behavior of another just behavior, the way that they store data is kind of different across different years. So we'll start with first asking them to like limit that, let's say not to go over all of these data, you can bring them all eventually later on, but let's start with only the latest part that you want to focus on, let's say the last three years, last five years, things like that. Let's move that into let's say a data warehouse structure, and that is the part that usually we get to work with either the internal teams, they might have a team of let's say developers, like let's say, a TVA as well, or even a team of let's say software developers will work with their team, and then provide solutions of let's say how this can be implemented. Now this is like the bits and pieces that you can put together, and then work on like what type of decisions they want to make. So usually we work on an outcome basis, like we want this kind of decision to be made. I want to know like which clients are clients that are let's say what are my customer churn rate and what I can do about it. So this can be a good, let's say, example to understand what type of reporting is needed. And based on that reporting what type of data we should look at and then those data are coming from all these different systems. There are some obstacles, however, like things such as sometimes there's no let's say good documentation on the data and like you need to find some some subject matter experts within the organization to understand how their data is actually working in that business so that you can convert that requirement to an actual implementation of a BI system. Yeah, it's your documentation knows it's going to crack a joke about well, well, that's rare. I mean that you don't find documentation. Yeah, you'll see a lot of a lot of systems without let's say I mean there are documentations you'll see documentations on the software developments but you don't really find much of documentations on the database itself like they are using the software as a service but they don't know how that is stored data but or even they are using like an operational system on premises. They just know that this stores the data they just use the data but they don't know how the data is stored in that's in that systems database and that is the part that usually lacks the documentation. Yeah, no, I joke because that's where I started my career so I was a technical writer and business analyst. And I spent a lot of time going and documenting working with engineering teams to document a lot of this because it just didn't exist or capturing and to go and understand. What is this, what are you doing what's being captured. How's this being used and a lot of that we then iterated on it was always feedback says you should also be capturing this for us to be able to leverage this information. Here's what we're missing. But yeah, no, it's a fascinating space. I think that there is a, I think it's been like just a, like data scientists is on a lot of top 10 jobs of the future next 20 years kind of thing it's, it's like number one or number two across the board. Yeah, yeah, like, yeah, quite hot paid jobs as well. All of data science jobs that you see their salaries quite, quite higher than others. I've got I've got a son in university who's he is major is his focus is atmospheric sciences and, and I said you know, you know he's he's a stem kid is just a math and science was and I said, you know what you really need to do as a minor. Think about looking into data sciences it's, it's the application everything else that you're doing with your day job it's the application of the data that you're helping capture and that you're going to go in there through the analysis on. If you have a fundamental understanding of the capture and manipulation of that data and the visualizations around that, you're going to be so far ahead of all of your peers. And so he went he added that as his minor. So computer science with the data science focus for his minor but it's, yeah that that's a, if you asked me, you know, hey I'm looking to get into technology and computer science engineering. It would be, you know, at or near the top of my list of recommendations as well as opportunities because every company is becoming, you know how satya talked about you know every one of the partners every company is a software company. I don't know that I entirely agree with with that idea full I get what he meant he was talking like business applications and building and you know citizen development kind of maker type development activities. But I do believe every company across every industry is going to have massive amounts of data and has to be thinking about that, you know the data analysis side of their business. Correct. Yeah, a lot of sorry. A lot of a lot of jobs that are not it related at all like a finance analyst like an economist, like, like any types of job that you see they are at the end of the day, dealing with data they are analyzing that data to achieve some. Let's say result and say this is the analysis I made let's do something based on that. So something such as data science and predictive analytics even descriptive analytics all of these would be helpful for them so learning these skills is definitely getting them few steps ahead of others. Well that's how I got into knowledge management and into the SharePoint space was working in project and portfolio management and more and more of that job as I started managing teams and building and running PMOs was owning the data across all of these various projects and executives would ask those questions of what are the patterns you know that we were seeing project hundreds of projects that are happening across a year. How can we get better faster cheaper do more you know across these are we are we using our people, you know, in an optimized way what what can we learn from this information and it was it became, you know, a massive database of project related data and statistics and looking at and doing planning activities based on that so yeah we we we found ourselves in that that world and I was able to leverage what I'd worked in the data that warehousing world over in this project management world and then then found SharePoint and got involved that way so rust is history but anyway well hey really I've appreciate your time today and learning about what you what you've been doing folks that want to learn more about what you do get to know you get in contact what are the best ways to reach you. So radical website is usually the best place there and you can find all let's say my YouTube. Let's say videos of my articles and and there are like links to Twitter LinkedIn everything all the connection information are there so radical websites like is has a lot of free materials over there so go ahead and use those materials and also if you want to get in touch with me any questions I'll be more than happy. And you've written a few books as well. Yes yeah some some books on Power BI and actually one of them is free available in in the website so it's like a 1200 pages it's a little bit like small but feel free to go and download it. And there are lots of like Power BI deep technical stuff in it and some other books that you can find it in Amazon I press some other places. This is a marketing guy that's a pretty powerful call to action say visit my website get a download a free 1200 page book you like whoa hey you know people like free. Yes yeah well I appreciate your time today and hope to hope to see you in person soon. Thank you thank you for your time Christian and thank you everyone for watching.