 This is Christian Buckley with another MVP buzz chat and I'm here today with Andrew. Hey Andrew. Hey, I'm well. How are you? Thank you for having me. Yeah, that's great to have you and I know that So why people that don't know who you are what you do where you're located? Why don't you give us the background of who Andrew is? All right? Well, the virtual background here is a Dropping a big hand as to where I'm located. I'm in New York City. I'm actually born and bred here in Manhattan and At one time I actually I had an office in that and that's all building I guess to my right Or to my left as the viewer sees it I'm very focused on data and analytics and business intelligence and more and more artificial intelligence And the whole data story has been the one constant in my career going all the way back a lot of that career was in Consulting and then about 10 years ago I pivoted to be more focused on actually covering it as a blogger journalist for ZD net and Also being an analyst kind of industry watcher in that space as well I do that directly and independently and I do a lot of Work in that regard with a research firm called giga ohm as well I've been talking about that for a while because I think We're in different Technology spaces what we focus on my background is collaboration technology But similar to you and I've worked at you know Microsoft to work for some ISV's and Went independent and I'm more on the marketing side But I do the the analyst in the independent research side of things Predominantly within the collaboration space and so I provide you know same thing I do independent research. I work with major University here and Do papers and and other kind of research? But I get called by reporters and things all the time and sometimes names sometimes not provide Input on different things that are happening around the collaboration space. So I'm just I'm fascinated by that side and obviously you do a lot of writing as well I do so it but like you the press was tapping me for comments all the time and then that kind of Commenting on things became a hobby and then I decided I really wanted it I really wanted to make it my legit thing. Yeah, how do you monetize that? That's always the question with those kinds of things where it's always a fine line between You know, you're asking a lot of questions. You're asking things that are more in-depth, right? These are things which I should be charging you for Other than you're doing your research your job for you, right, right? Well, I you kind of laid it out there even if you weren't explicit. There's going to be stuff where you're just covering Events and happenings and industry developments and maybe that is not something you'll monetize terribly Prolifically, but then as you do that you gain you get an awful lot of knowledge about the industry And then there are opportunities on the analyst side where those things can be Brought to bear professionally. So that's that's kind of my story Covering it as a journalist is not necessarily a huge jackpot, but because of the timing that I Started It ended up being where I was you know I was meeting a lot of the companies that we have now companies like cloudera companies like snowflake When it was early days and when the CEOs were very technical and because originally I'm from more of a technical background As a developer and as a database person and then as a consulting person it just led to fun conversations I think the folks were pleasantly surprised that I kind of knew what databases and bi were and And that the timing was just dumb luck I ended up really meeting lots of people in the industry early on and in the open-source analytics industry and then that just led to a good collection of people that I knew and and and a way to Tap into everything and keep current And of course, you know my background much like yours very much around the Microsoft stack in my case going back to the early 90s The first version of visual basic and the very first version of sequel server that ran on Windows Which even though it was the first version it was version 4.2 because of that early pedigree and because I got interested in Microsoft's business intelligence technology 20 years ago and it was new and I was on their partner advisory council for bi for about five years I just kind of grew up with all that stuff and I had a good understanding of the Microsoft stack and then really by being on the partner advisory council that's what opened my eyes to all the competition and Mary Joe Foley who covers Microsoft for ZD net knew that ZD net was looking for somebody to cover big data and she asked if I'd be interested and It took several months actually From that initial inquiry to when I started writing, but that's that's how it happened It all it all goes back to sequel server eventually Yeah, it's interesting because I saw I you know had a little bit of experience in that space and worked in the Data Warehousing World you know years back I worked for Pacific Bell and I was in a shared services organization actually for Telesis So we worked the parent company. We worked with PBIS the information systems was Pac Bell a primary customer And I was responsible for all the front-end applications business objects Data strategy sass kind of all those tools that were the front end of these things But I kind of moved away from those things in the project and portfolio management Which was kind of my stepping stone into Knowledge management information management systems and into kind of the SharePoint space like that direction What's interesting to me is that when you started to hear the phrase, you know, big data Being bandied about and I had worked for several years working with supply chain organizations And I owned project management side of you know that that world but was a product manager and project manager And and so when I started, you know Learning about these big data systems. I was like well big data that just sounds like so much of what I've been Doing, you know for these for these years. What's interesting is so you started to see companies like Splunk and others like major bi providers start to participate in TechEd and in other Microsoft, you know conferences My impression is that big data that term is kind of dissolving into because Yeah, I say this all the time that you know, you know any data issued in the modern Collaboration stack, you know, every problem is a big data problem You know massive amounts of data is the price of storage drop down and the complexity of all of these systems And it's really just a parallel to what we were doing and calling this separate space of big data, you know, five ten years ago Yeah, I mean even at that time it a Number of us understood that the notion of calling it big would be eventually would be kind of quaint and antiquated Yeah, and and really it was it was just data and in fact ZD not wanted to call the blog big data And I asked if I skewed it a little bit. I said can we call it big on data? and that is what it's called because Well, two things first of all, I knew bi was germane even then and I wanted to be able to talk about bi and not just What was all about Hadoop at that point, right? But also because I knew these things would come together at that time I was really out in the wilderness from relative to my Microsoft background Big data was all about open source and Linux And you know Apache Software Foundation projects and Microsoft at that time really wasn't there yet But eventually the two converged and I was I was pretty sure they would I also was pretty sure that That eventually data warehousing would become a legitimate big data Technology as well and and that happened to with Amazon with redshift and and snowflake eventually kind of made sure of that And yeah, we don't really say big data anymore We tend to say data and analytics and more and more we tend to talk about artificial intelligence and and machine learning Well, it's germane 20 years ago, too. It's just that we're called it data mining But one major shift and I think that Microsoft has a major role within that again My perception you might have deeper insights into this is that you know, Microsoft was seen as kind of slow to the party late to the party with a with a lot of this and whether that's true or not, you know Because as you kind of look back over technology I was just thinking of like the complaint now of slack against you know teams kind of thing and and somebody Wrote in our great article a couple days ago that said well If you go back and look at the history of teams and all of the products that you know It came from the link and communication server and kind of up through it's like Microsoft has been in this space for 15 plus years Yeah, and sure point for that matter, right? It's not just the meetings. It's the collaboration Which is and big data much the same way. I think one thing that Microsoft Has done really well is this idea of the democratization of bi and Bringing it to the masses and yes, there are I look I live here in the world like the domo headquarters is right down the street He's got you've got the all these other players that are out there, but so much of where we are with end-user. I mean I'm something I talked about in a webinar a few weeks back was the Ideas application the ideas functionality in Excel, which is like a gateway into you know the power bi link to your simple Simple Excel data and seeing these Visualizations and being able to map those and take those over to power bi and for end users that have no training in these tools to get kind of a jump-start in and Critic these visualizations. So Microsoft the whole Microsoft bi stack was also was always very close to Excel I mean really pivot tables in Excel were originally there to facilitate Having a decent interface to talk to cubes and SQL server analysis services and the two teams have had an intersection For quite a long time. I'm sorry to interrupt, but since you mentioned Excel. I yeah Well, better you talking to me I like I can hear myself talk all the time, but yeah, I feel the same way about myself But but in any case yeah, the Microsoft has been in the space for quite a long time Analysis services is actually kind of a seminal technology and it's query language MDX has been you know a thing in the analytics world For a long time and a real standard where they were weak for a long time Actually was stuff on the front end and and where they had the most success In fact was the intersection of Excel and SharePoint this thing called Excel services at the time That's really where they had their best bi stuff and it took Multiple iterations of trying eventually they got to power bi and that's that's where everything got really successful But it it took over a decade to to get there Well, Microsoft was always strong on the back end the front end was was much more arduous Oh, yeah, it's fine. It's my my way my path into the SharePoint world was through project server and deploying it I actually tried to talk my that first client where I my first real hands-on deployment Tried to talk them out of it going with a an early, but working Pure SAS Portfolio management solution that what they really wanted it wasn't like the interface side of it was the back-end It was the analytics around the data. We never got it working. We never really this was in 2004 2005 We never you know, it just wasn't I Know there's gonna be a lot of people that will complain about this it wasn't a working product in my experience It just it just didn't work And for those that will say is like well, you didn't have the right people working on it Like we've on Microsoft's advice hired the company that had deployed it in Coca-Cola And what we were trying to do which was pretty much down the brochure like this is what we want project analytics We want the data out of it We get the reality that we were asking for things which Microsoft made claims that they could do that Coca-Cola had never You know use they But anyway, I got to beat a all that integration back then was very brittle and very dependent on Kerberos authentication, which I think three people on the globe knew how to set up really well at that time Well, I think that in the end of that story there, of course is that I It was frustrated project server, but I caught the SharePoint bug Early and then you know the rest of it is history, but it's a but say I think it just it does go to say Microsoft's strength has long been while there might be very R&D dev ops focused solutions out there not that Microsoft doesn't do that side of it Microsoft has always been really good at taking these very complex concepts and ideas and technologies and Productizing it mainstreaming it for the business user and I think that's the evolution that has It's it's speeding up now. It's happening so quickly. I think that's right. There's there's iterations of that though So yes, they did that with straight database. They did it with bi They've largely done it with with big data with synapse analytics is is is bringing a patchy spark to bear and making it a lot More accessible than you know the the kind of open-source stack that you needed to get to it before although that works, too Where I think their next iteration is that they're really not that far down the path on is is making machine learning as Accessible to business users as the other stuff There is some integration between Azure machine learning and Azure cognitive services on the one hand and power of the eye on the other But there's there's a longer way to go And as your machine learning itself while I would say it's it's probably more business friendly than its competitors it is still largely kind of in the in the data science lane and Bringing it together with the rest of the stack. I think is the next the next important step For Microsoft and indeed for the industry because no one's really doing a great job at that yet Well, you think about you know, the probably the what Microsoft is working on now the next big thing that will be publicly available Mainstream will be project cortex whatever it ends up getting named and so there you have some of that It's the you know the productization of a lot of that technology. I don't know how much you're familiar with it If you're participating or providing, you know any input into some of the piloting that's that's happening now You know, well, what are your cortex? No, yeah, so that's one of those things where It's it's a way of going in and putting a front end and a dynamic Automated front end so leveraging kind of all those tools that the AI the machine learning the cognitive services the to be able to Better get you know surface intelligently data within your massive amounts of structured and unstructured content within your your system how that I will actually work and How it will be managed and how much will actually be automated versus curated are all things that we all want to see I'm I'm interested in that was kind of hoping you had a little more, you know inside view into what's happening with it But with that particular project, no But what I will definitely say the work that you know, I do directly through blue badge insights and and the stuff I'm doing with giga ohm and what's eating that I mean that is definitely, you know The desire out there is for machine learning to sort of enter the you know It's gonna be it's always gonna be technical most likely But to at least have it enter the mainstream kind of enterprise developer Technologist stack rather than this I was saying before kind of being in its in its own lane I mean there will always be a need for data science and data scientists, but we're not going to scale that population To the degree necessary to make machine learning really Accessible to the broader addressable market So we've got it. We've got a mainstream it more automated machine learning is part of the process of doing that that Makes it so that you can sort of bring your data to an ML platform And then the selection of algorithm and perimeter values and all the you know The nitty-gritty data science of it is something that can for a lot of use cases can be more more automated And then and then it really only takes a developer skill set or an analyst skill set to put it to use But there's even there. There's still a way to go Microsoft's auto ML is it's pretty good, but I Think on most most lists of like future job opportunities there, you know data scientists and just in any industry and So I've I've got three kids in college right now And I've said for to each of them and their respective areas like you know, think about one is kind of taking this up And like yeah, I'm gonna go actually added as a minor to his degree, which is a stem related. It's a atmospheric sciences Okay at the University of Utah and he added computer science with data analysis as a minor and so he's I'm like he's he's got of course all the licenses to all the Microsoft stuff and And so he's starting to pick up and learn about that stuff But you know, my belief is that if you take that data analyst route in whatever the field that you're interested in I mean, that's where the greatest opportunities are and that's why you see like where all the jobs gonna be in the next 20 years and Data scientists is like number one or number two and almost all of those lists Mm-hmm and and so anyway, it's so let me ask you the question about You know, what are you most excited about of what Microsoft is working working on? Stuff that's coming up that they've announced Obviously things that are publicly out out there Hashtag no leaks right right absolutely so I my answer might be counterintuitive to you although I alluded to it before but As long as it's not PowerPoint that'd be weird not PowerPoint It's it's synapse analytics, which is in large part a rebrand of what was Azure SQL data warehouse But what Microsoft is doing with it? First of all is adding data lake technology to the data warehouse technology and that's largely based on Apache Spark But beyond that what they're doing is they're tying in other services. They already had Including as your data factory and power BI and to a minimal extent thus far But hopefully a growing extent as your machine learning so my my biggest beef about the cloud You know time frame here whether we're talking about Microsoft or we're talking about Amazon or Google is that the cloud providers have been really good at putting out all these sort of disparate services all these building blocks But they kind of sit there as islands of functionality and there hasn't been a lot to kind of unite them into a single You know experience or just put it in a you know an integrated development environment where it can all be brought to bear That's actually the hallmark of synapse is that it's doing that And there's a decent possibility that similar integrations with some of the third-party tools will happen there as well so to me you know if we go with the cliche that the You know the hole is greater than the sum of the parts like that's the that's the upside here Most of what we have in there We already had if you put together as your machine learning power BI Azure SQL data warehouse and maybe HD insight which has spark as part of it You would have had a the same basket of functionality and technologies, but they weren't they weren't coordinated And so why is that though it just lost a ton of value and now now that value is starting to be realized Yeah, so so why is it why is it more fragment come together? Is that just the nature of the kind of the Microsoft research? Developing parts of it some from the product teams some coming from industry in that Microsoft adopts or Acquires in and it's just fragmented that way or or is it really that they're just they're going down Kind of the the paths with different areas They see the connections later, and it's just organically has to kind of come together. Is it? Kind of an all I think the last of those is is part of it I think a big part of it is just the cloud provider mentality, and it's a little bit strange for Microsoft because you know cloud is a pivot Obviously, they're an enterprise software technology by by trade or you know if you go way back they were building basic compilers for personal computers, but The cloud met the cloud mentality has been basically one of being a utility So we're going to put up service one. We're going to put up service two We're going to put up service three and we're in the business of billing for computing storage I mean I hate to sort of distill it down to that but that's kind of what happens and really we're going to leave it up to other people to Stitch those pieces together and come up with solutions But you know ultimately that's if you Even if your whole goal is to have pull-through on the basic services You need to have a value-added kind of experience that makes all those services more usable And it becomes a competitive necessity to do so so while for all of us who have been in the consulting field for a while It's great that you know we're we're relied upon to bring everything together I think it's still better if Microsoft provides a base level of integration There's always white space on top that solution providers can can build upon That's why Microsoft's partner, you know a network, you know the Microsoft partner network is just massive I don't know what the number with the claiming lately. I miss that on inspire last couple weeks was but it's What it's at least five hundred thousand global partners is probably more like, you know six to seven hundred thousand but yeah, just Massive numbers, but yeah, I was one CTO at a gold partner consulting firm and yeah, you know We'd every year we'd make the the pilgrimage to the partner conference now called inspire And yeah, you sure get you sure got this like humbling experience like I thought I was the you know this cool guy Partner with Microsoft and then you get there and there's you know, like everyone everyone special Everyone and the volume of special people's pretty darn high But it's always but I think to your point though. I mean one of the things that's It's just so exciting. I get your point. There needs to be that foundation You know people that are coming in they're looking to Microsoft to provide a foundation You get complaints when they don't find in one area that it's quite there There's not a working tool set that covers kind of all those different areas from a partner perspective of my Microsoft Always says is while they'll continue to innovate and add on and iterate on those things and maybe eventually Provide that tool set. I mean they they say like look There will be partners that are specialists in each of those areas Which will be better able to meet the customer needs But with that value add on to the core that we provide it's all yeah It's all a question of where you draw the line between Platform and solutions on top of the platform But also Microsoft's pattern largely as they go through like a period of lots of different teams innovating You know and building new things and everyone getting excited about that and then there's then there's another round where things get rationalized and brought together sometimes that's about branding sometimes that's you know down to the level of API's and standards but you know the The notion of integrating these things becomes a matter of fit and finish that tends to come as a sub subsequent Round of work not the initial round of work and the the cloud is mature enough where we're we're getting to that subsequent round And so that's what we've got you know It's interesting that with our with ourselves or our own companies and we give a lot of leeway to iteration to be able to kind of you know, it's failure find you know and then Kind of regroup Reiterate on that thing and push it back out there and we don't give that same Space to OEMs like Microsoft for those to go out there and learn and iterate But I mean that's just that we see what's going on. It's it's just happening a lot faster than it used to Which is an adjustment and I know my a Microsoft is struggling I think there's even been some calls to throttle some of the innovation that's coming out certainly in the Like teams the you know office apps and services space So slow down the slow thing that people can absorb it. Yeah. Yeah, so Yeah, and and as soon as they do then people will complain It's something's not happening fast. There's no way to win there So you can't please all of the customers and partners all of the time. No, no, you cannot but yeah And that the calls to slow down those happen every once in a while that to me That's a that's a that's an indicator of things that are good. I I've never really seen Microsoft heed that you know Eventually, they'll slow down. Anyway, actually, I believe big data was a huge Mostly a huge reaction to the fact that the entire Database and analytic space had been largely ossified for about 15 years. There had been very little change I mean sure a new version of sequel server every three years new versions of Oracle new versions of IBM DB 2 new versions of the bi platforms, but they were so incremental and so, you know evolutionary and You know things got, you know more and more expensive especially on the data warehouse side where a lot of the companies were really just Monetizing storage the big thing about Hadoop was they said, okay We're gonna make it so that the storage is just based on commodity discs inside of the commodity servers And we'll set it up. So if those discs fail, there's two more behind them and the whole reliance on enterprise storage and all of the Economic model that went with that was totally uprooted to me. That was what the big data Revolution was all about was just kind of upsetting that that High barrier to entry franchise of the way the data warehousing world worked Well, so that shift was happening around shift was happening like 2000 2001 You had a global grid foundation You had you're starting to look at that these compute farms and of ways of you know automated movement between those and just That that was an interesting time. I was in the IBM world at the time And working with rational software and and my own startup and that space and so saw a lot of you know interesting things But you're right It just it kind of got quiet for a while after that not that innovation wasn't happening, but it just it's cyclical I mean stability is good We need spit stability so that that slowdown that you were talking about can occur and we can digest everything and get good at it And you know kind of make things more navigable, but eventually you get too comfortable. I don't mean you Christian one gets too comfortable and And then what happens is there there's this build-up this pent-up demand for new stuff to happen and You know, it's you know, it's if you ever read the book by Thomas Kuhn the structure of scientific of Revolutions it's exactly what it is everything gets comfortable the theorems get all settled the science gets all settled and then There tends to be some big bang change that upsets the whole order of things and then a new a new order kind of Settles so yeah, it's you know, it's a fascinating space. It's you know Definitely a space to watch in the Microsoft ecosystem as well But Andrew people want to find out more about you get in touch with you. How do they reach you? Where do they find you? gosh www.bluebadgeinsights.com is one way and my first and last name all in one word Andrew Brust be our UST on Twitter Those are two good ways or just Google my name and the ZD net stuff and the giga-ohm stuff will will show up as well I have a lot of stuff out there. So the search engines are pretty good at flagging a lot of it Excellent. Well, I really appreciate your time this afternoon is great catching up with you and maybe one day We'll see each other again. I look forward to that and I hope it's I hope it's sooner rather than later This is this has been ongoing for a while and let's see what happens. Yeah, and enjoy that nice toasty weather there in New York City Every summer All right, thanks a lot. Be well. Take care