2nd Age of Analytics – Ep. 206

Most analytics programs are optimized to ship dashboards. But the business doesn’t run on dashboards—it runs on decisions, and decisions create outcomes you can learn from (or ignore).

In Ep. 206, Mike, Tommy, and Seth break down Ryan Dolley’s Super Data Blog post “The Future of Business Intelligence Part 1: The Mangled ‘Supply Chain of Analytics’” and use it as a lens to talk about the “ages of analytics.” The headline: the second age (cloud + modern warehousing + self-service BI) made data more accessible, but it also reinforced a one-way mindset. With AI showing up everywhere, that model is about to get stress-tested hard.

News & Announcements

• The Future of Business Intelligence Part 1: The Mangled ‘Supply Chain of Analytics’ — A sharp history lesson on how we built the modern analytics stack—and why “data in, dashboard out” is a dead end without feedback.

• Suggest a topic for Explicit Measures — Send the crew a topic (or a spicy hot take) for a future episode.

• Subscribe / episode archive — New here? Start from the archive and subscribe so you don’t miss the next part of the series.

• Argus PBI — Tools and resources from a friend of PowerBI.tips for people living in Power BI every day.

Main Discussion

The episode starts with a simple premise: the industry still treats analytics like a factory line. Raw data gets extracted, shaped, warehoused, modeled, and finally visualized—then we call it “done.” That pipeline is useful, but it’s incomplete.

Ryan’s framing is helpful because it connects that mindset to history. The second age delivered huge wins (cloud scale, modern tools, self-service), but it also left teams with familiar pain:

• “Self-service” becomes DIY definitions.
• Dashboards multiply while decisions don’t.
• A report refreshes… but nobody knows whether it changed anything.

What to apply immediately in your own BI work:

• Start with the decision (and who owns it), then build backward to the minimum evidence required.
• Make metric definitions a first-class product: documented, governed, and measurable.
• Add a feedback loop: track whether a report’s recommendation led to action (and what happened).
• Design for exceptions and conversations, not just “executive summary” pages.
• Optimize for time-to-learning (iteration speed) over time-to-dashboard (publishing speed).
• Treat AI as a multiplier: without strong foundations, it will scale confusion just as efficiently as it scales productivity.
• Shift governance from gatekeeping access to enforcing standards and guardrails.

Looking Forward

The next age of analytics will reward teams that combine solid data foundations with fast, outcome-driven learning loops.

Episode Transcript

0:29 hello good morning and welcome back to the explicit measures podcast with Tommy Seth and Mike happy Tuesday when it is Tuesday it’s only happy Tuesday on Tuesdays don’t let Tommy fool you he wants me to say it every day no I don’t I want I just want to make sure for the future this this is beginning to remind me of the the Spaceballs now we’re looking at now when just then what did it happen just now who’s on first who’s on first What’s On

1:00 who’s on first who’s on first What’s On Second excellent well this is going to be a recorded episode people are traveling around going different places so sorry we’re not live today things have been busy apparently conferences are back on and people are moving all over the place these days so stuff’s happening anyways that being said we’ll get right into the topic for today today we’re running an article from what’s the gentleman’s name I actually didn’t catch the name on their per se Ryan Dolly and Dolly super data blog

1:34 Ryan Dolly and Dolly super data blog yeah which is super data I like that like the like Yeah The Blog name there super data so talking a little bit more around the second age of analytics and why it’s ending why we’re moving on to the third age of analytics which I think is actually very timely and appropriate was seeing that every other day there’s like another article about some more AI That’s being populated somewhere across the ecosystem so it may trickle its way into data and bi and insights yeah this is this is going to

2:04 insights yeah this is this is going to be one of those that’s I think going to be difficult for us to just stick to the the like this article article because it’s it’s in a series and he already put out the second right so this one is a precursor and I think we’ll we’ll end up talking about the other two as well but it sets the stage for I think a lot of the history of analytics or business intelligence in general and like why we are where we’re at and I think it does a really good job of it

2:35 think it does a really good job of it I’d agree with that as well let’s jump in link will be in the description below of the episode so you’ll be able to catch the link there and see what we’re talking about there and then let’s jump in let’s just talk about he he opens with the the history of Analytics and I think this is another he started talking about like in in the the early 200s we saw all these you early 200s we saw all these monolithic data warehouse know monolithic data warehouse activities thousands you mean all right did I say

3:05 thousands you mean all right did I say 200s yeah yeah the 1900s on stone tablets would chisel in our data data could not get your x-axis wrong or you’d be oh man be oh man you spend a lot more time thinking about how much what you needed to build when you chiseled it in there because it was permanent at that point but the guys who had a when you clicked on the bar chart it just took forever to get the results because they had to Chisel it all out again dude had to go in the cave with his

3:36 dude had to go in the cave with his paints and put the whole new meeting to data to Ink ratio yeah exactly oh goodness sorry 2000s 2000s I remember working in the 2000s I remember remember doing work against the database business objects was very applicable we had teams in I. T that would manage the entire data warehouse you had to go reach out to them to get access to information there was a whole bunch of SQL later on SQL servers I wasn’t allowed to touch them you had to go

4:06 allowed to touch them you had to go through some other interface or UI but it was like this web browser based UI you could grab and drag and drop your Fields I just remember people making lots of tables and exporting them like that was like the main goal make a table you still had a bit of the personal bi and then obviously Enterprise bi was completely different but personal bi was just Excel yeah and at least from from my experience was as an analyst was just taking all like the same thing taking on

4:37 taking all like the same thing taking on an internet taking exporting the data putting in a folder and that would be the historical data that’s all we were connecting to is Excel and that’s how we shared and honestly from a reporting point of view from a personal bi point of view was was slides was a presentation to the team on a weekly basis it was not any consistent report is how are we doing now this week put that in a PowerPoint based on the latest analysis that’s how I started I

5:07 latest analysis that’s how I started I think I think what’s cool to me is we we are word we’re we’re the the the folks that came in with Excel and Word and by the time we hit business like that was that was pretty standard like and it at least in its infancy versions right like even in grade school like that’s what you start learning on and that was revolutionary and that’s where we start with reporting a lot but what I love is he takes it like one step back where wave one being Enterprise reporting ultimately like was the real

5:39 reporting ultimately like was the real how he puts it is the real world process into the digital space where we actually started converting all of like think about this businesses ran on paper I know right like which is like I had an interact with the company who ran on green bar and that was like recently but that was like what is this paper computer right and and ultimately

6:05 paper computer right and and ultimately when you you look at some of the core systems and the ways reporting was done it it is it was revolutionary at the time right and and he goes into like what are the core components of that right like there’s a single model there’s a single way that you’re taking business processes and data that comes in in one way right prompts or like inputs like parameters like very specific things that people can like they they could

6:37 that people can like they they could thousands of pages of documents and it’s like I want to see this customer and then the data comes up right this is a really good point because I think you make it really like we started on paper to begin with right it was right it was paper reporting kind right it was paper reporting to start with and everything was made of to start with and everything was made to design that and then we started introducing like the web the computer the computer screen and then the computers became more capable and then we started taking computer systems to replicate what we were producing on

7:07 replicate what we were producing on paper paper and then eventually the paper got removed altogether and now we’re only looking at screens right what blows my mind is yeah the fact that we’re not already on a four-day work week like how inefficient businesses were with mail like mail rooms people walking around with mail and what is it now like one of his point distribution everything’s in email now that’s true I’m gonna report it like and this is why I think it got it was it became so important and why you still have people

7:38 important and why you still have people who are like yeah email me the report as opposed to just clicking and going and getting somewhere because of this mentality that like it shows up in my inbox and I it it’s almost interesting to me that like I think people are finally fed up to the point where it’s like I there’s too much email there’s there’s too much coming in my inbox now that I can’t manage and hopefully we like get out of this pivot of like email it to me like right it’s just gonna be this Wasteland you said didn’t start ignoring it you said distribution and I was thinking this same in the same

8:10 was thinking this same in the same coin like the communication of data did you guys back in the day ever print out a report and then actually walk over to hand it somebody because that’s what we used to do yeah we would print it out and then we would hand it to the chief marketing officer we would hand it to someone on the cheap but it was literally like the physical walk and that was communication and then we had to explain what what it showed me there was no whatever from the Distribution on from a digital point of view and think about where we’re at now and set to your point with email I think a lot of internal

8:41 with email I think a lot of internal orgs are going away from email with slack and teams okay I was gonna go okay yeah live communication yeah so Seth you were saying like okay I’ve already did a bunch of these email things people would move around letters now we’re getting away from letters now we’re getting bombarded with email now there’s so many emails people are overwhelmed and so now the the preferred communication is now let’s go to a messaging system right and now I’m seeing I just saw the other day a AI based response oh boy thing that will let you do like LinkedIn and I

9:12 will let you do like LinkedIn and I don’t know some other program but it was like an AI based thing that would Auto respond to messages or other things I’m thinking oh my gosh well with that in integration coming now my messaging system whatever that is is going to be inundated with with data and I was watching Linus Tech tips the other day they’re like yeah you could now take an email response you can run it through Ai and you can say generate a happy response like this you can say the answer is no generate a happy response and it will generate like five lines of five sentences of thank you for your

9:43 five sentences of thank you for your reply I don’t think that it’s appropriate and then I’ll just give you the answer but you can literally give it like the shortest amount of information and it will generate so we’re now going to have like we’re all we’re only gonna be talking like yes no yes AI will generate everything else we you and I need to create a Seth prompt for email what does that mean no it would be very simple no reply to an email no that’s a bad idea yeah yes that’s a good idea that’s all it would say it’s so simple done why why are you saying hi to me just get

10:14 why why are you saying hi to me just get to the point what’s your question I forget if one of you guys sent me the the cartoon where or or if I saw it online where it was the the first panel was the guy doing something similar like oh generate this email that says use AI to generate this email to whatever and then the next panel was like oh look I’ve set up this generative EI AI to like read these really verbose

10:44 EI AI to like read these really verbose long emails to convince it down for me it’s like yeah did we just do with AI right instead of just asking the question and getting an answer yes or no we have these AI generating all this verbose words so we’ll have one side generating all the verbose words the other side reading the and summarizing it and literally the people in either end will just be like yes no yes no yeah right right and then the world does become like Seth’s world that’s world anyways that’s that’s and and that’s

11:15 anyways that’s that’s and and that’s what I think what he talked to your point there earlier though talking back about the curated library of insights right that early 2000s was where we started talking about all these things like he has a lot of terms here that I think I resonate with centralized metrics modeling giving variable prompts input these variables get out standard reporting pieces all those are like they’re fantastic points right like server based right like server-based centralized in a central location where people could manage it you could create a consistent

11:46 manage it you could create a consistent experience right decentralize security and you make it auditable right like all of a sudden all these these massively important Enterprise side tools are there yeah and that was the backbone for all data within organizations even and that’s like when I got started it was definitely in that realm right yes like yeah all we have all this centralized

12:09 yeah all we have all this centralized data and you have tons of processes to back up and restore Disaster Recovery Etc because they’re so integral to the business but then at the same time it requires highly technical folks right yeah the technical level got way higher there at that time I agree with that 100 percent you’re you’re now dealing with SQL servers and database administrators and all this technology that had to be involved yeah so you had you had to make a direct investment on these people to

12:40 a direct investment on these people to hire those skills because this was a brand new technology that was required to be administered and I think for for most folks right that’s also what caused the most frustration from the business side was a lot of those like as the importance and availability of business intelligence and and data to analyze became more commonplace through different tools for me it was reporting Services right in SQL

13:13 reporting Services right in SQL the business started to realize the value there but was still locked behind it producing those results for them right and then and I think that’s where the Advent of access databases and Excel really became a thing in the business because as that data became available to them in reporting right if it’s reporting Services reports like essentially they just use those outputs as sources of information to go build their own stuff

13:44 information to go build their own stuff anyway because they needed that that information quicker and faster than it people were willing and meaningful business intelligence folks we’re using Source systems just to generate that for this well it goes right in the face of the common conception or conception that organizations are data illiterate where even from back then they wanted to contribute provide feedback provide suggestions and also adjust the data but they were at the mercy of whatever SSRS report they had

14:14 mercy of whatever SSRS report they had that was not going to change in a week if they needed an update which is why they needed those exporting which is why they needed that that hybrid or the idea of the hybrid between that data Savvy Excel person to provide something that can adjust and be flexible with which was already a need back then too that’s not the knew with what we do now I don’t I don’t I wouldn’t say it’s it’s not new it’s just I I think what we’re seeing now though is there’s this again

14:44 seeing now though is there’s this again I’m gonna I’m gonna let’s keep going through the article a little bit here so I think he has this idea in the article like the very first section is wave one was Enterprise reporting all these curated insights people I sat down and took time which is something we don’t have a lot of now taking time to identify what is important to the business really thinking about how your data is stitched together I think there’s a little bit more of a concerted effort around modeling and then he starts talking about like wave two and so in wave two he starts introducing the concept of here’s

15:14 introducing the concept of here’s Tableau here’s power bi these tools allow you to go grab data from wherever and now you can start now the business can start exploring those data pieces like doing some of the work that it previously would have done and I think that was one of my main complaints when working with like the centralized data bi models was it took so long for you me to explain what I wanted out of the information to an IT organization to produce a data warehouse for the tables that I needed to get information out and sometimes

15:44 information out and sometimes frankly I didn’t even know the questions to ask at this point right I know there’s data in there I don’t know what it tells me and it comes to me and says what do you want to do I’m like well I don’t know yet I haven’t I haven’t had enough time to look at the information to tell you one way or the other like maybe I need aggregated by product maybe I don’t maybe I just need to look around the information for a bit so I feel like there’s a lot more to discover that needed to happen and sometimes we have to come into these large Enterprise systems with what’s what is the final

16:14 what is the final solution and I didn’t know yet I needed more time to discover and plan out what that that engineering or data would look like I wonder if the challenge there or where the value broke down is the the lack of what organizations thought they needed something we and allow me to expand on this we’ve talked about this quite a bit where there’s a lack of like set titles in our industry like what’s a bi analyst what’s a bi developer what’s a business intelligence

16:44 developer what’s a business intelligence manager all those don’t really have a set name and I think a lot of times or what’s really caused friction is people understand what they need something they don’t know what they need and there’s a lot of people who either don’t have the skills yet or they’re ill-defined and I think that a lot has to do with the titles or a lot has to do with we need a bi person but they really don’t know exactly what that it calls for I’m not sure yet I do think

17:14 I’m not sure yet I do think there’s there are courses and actually programs that you can get run through that are analysts or nope classes or or specific skill sets around data analytics right there are data and I get a master’s in data analytics sure I think those those help to some degree I do feel like we sometimes miss the forest from the trees I guess in some cases we focus on certain things that are not really impacting the broader type of the business so sometimes I feel

17:44 type of the business so sometimes I feel like when I walk into these these situations we’re not asking the right questions questions right and I think this is where what we’ve talked about recently in the podcast was all this okr stuff right what is what is the goal you’re trying to reach and what does the data look like in addition to that goal and I think when I’ve done reports with organizations who understand their goals what is the expectation of your company right if we’re going to grow Revenue by 10 we would expect our

18:14 Revenue by 10 we would expect our operational costs to grow by eight percent right there’s some math there that like we as we acquire new business we should be efficient enough to be able to acquire new business without acquiring additional costs or a lot of extra costs to do the work there’s a ratio there and so people who understand that concept I think you get better reporting around tracking what really matters right if you see Revenue generating by 10 and you’re increasing costs by 11 okay now we have a problem right to acquire more work we are now incurring more effort to

18:45 work we are now incurring more effort to do that work that’s not the right way to go right right it’s high level concept like that I think people need to apply more well I think especially in this desktop analytics age there was immature goals for what bi should do and I think then you got immature skills in that and I think I was part of that too think about if someone’s going to invest in a marketing team they know what they want to get out of that I think in this age when people said they needed bi they weren’t necessarily sure what they wanted to get out of that which I think cause I think that caused a lot of skills that weren’t totally

19:17 a lot of skills that weren’t totally related to what they needed I think that’s a really good point because even even as I agree with wave one Wave 2 and where we’re going with Wave 3 potentially right where where are the vast majority of business users are they actually in the wave and can they like can they communicate in that style and I would argue like no the vast majority are still in one because tables are what it was however the challenge I have where we start to talk about like the transition from wave one into two is

19:48 the transition from wave one into two is that I don’t think is addressed in the article and as we’re talking about it I I I’m I want to voice to see your opinion is right so the transition was is essentially from taking reports it and the the generation from them was designed to replicate what what was known to people which is a piece of paper right like we’ve developed now reporting that is mimics the same thing but it’s much more valuable provides a lot more insights to you

20:19 lot more insights to you and now like the transition is well people are so used to computers now that the screen or the dashboard is much more a much more agile and better way to produce those insights because it can be interactive right people can people can find their own Answers by clicking on a few things as opposed to just have something generated for them and I agree that’s like extremely powerful but the question I posit is is Wave 2 actually tools like Tableau or

20:53 is Wave 2 actually tools like Tableau or was the Advent of Wave 2 actually when Excel got buried into the business because from my perspective Excel was the the method by which business started to take off and diverge from I. T to solve the immediate problems that they had which is why I became I think so wildly popular because you could it created the framework by which you could create your own calculations by which you could work with in in those

21:23 by which you could work with in in those terms very large sets of data right and then pivot and and look at data in different ways and aggregate it like is that a valid thought here before the Advent of like all these Cool Tools that allow us to visualize data which I think is is I’m not demeaning no but I almost wonder where the pivot where we separated back out and the business actually had the opportunity to run without it as much as they possibly could but we’re still

21:53 they possibly could but we’re still Bound By The Source systems I think there’s a fundamental difference there where when Excel became more prominent that’s like when they made in football the forward passed legal but by no means was that prominent I think the change with like Tableau and power bi that really brought us into the new wave was the access to the data because even with Excel you still had to export these files you probably didn’t actually have access to the source and I think that the large change was

22:24 and I think that the large change was one you could actually directly connect to those sources and then create something that would be in a sense the automation that it can refresh that to me is where that large you would say where the cliff is or where the okay so for the vast majority of business users what is their primary data source for power bi well before we go data on data sources I’m gonna I want to jump on your comment there real quick Seth I I think I think I’m very much with you Seth and this concept of it’s not power bi or Tableau per se

22:55 Tableau per se I think I think there’s two fundamental things that I think Tommy I’m going to jump on your comment as well right the the two things that revolutionized why the business is not doing more of their things on their own was this language called M which was a graphical interface that allows you to do simple Transformations on top of data things so I think your point is World taken doesn’t matter what the source is but I think the Tooling in place that’s what was my hook the ability to access and transform yeah that was my hook to say these this next wave of tooling is

23:25 these this next wave of tooling is really cool so yeah I think again I’m gonna I’m gonna go high level here for a minute like the the wave that we’re talking about here is there is this low code no code tooling solutions that are continually popping up it’s powerapps it’s power bi it doesn’t those are what Microsoft’s tools are there’s a lot of other tools that are low code or no code stuff as well like my son I caught him yesterday he was upstairs programming he’s like I’m trying to build a little funny app that plays a sound when I press a button but he’s using scratch it’s it’s block coding right you put these little blocks of code together and

23:56 these little blocks of code together and it has the statements in there but you’re now not writing actual code you’re dragging information across and saying these are the actions I’m going to do and here’s what that line of code will run there’s more code behind it but it’s allowing him to be able to like that but that’s what I’m seeing things are going to move towards and the and the further we go down this computer Realm you’re seeing more and more

24:16 Realm you’re seeing more and more abstraction layers away from the hard-coded writing actual code you’re doing more of this generalized coding so I think a couple things that I again that’s that’s M and Dax right but the real pivotal moment for me around this one why does Wave 2 look much more attractive it’s Microsoft bundled up an incredible tool called analysis services and threw it away to people for free to me that’s the linchpin it’s the modeling it’s this functional language that we already understood Excel

24:47 that we already understood Excel introduces concept of functional language Excel introduces concept of using cells to write a function to sum and aggregate so this functional language piece is something that we’re very familiar with and people are very comfortable with so that’s been 30 years of investing in people learning a functional language and oh by the way we’re now bringing analysis Services another Dax language that is also functional and does variously a similar thing but now we’re going to give you this I T so I would say I was purely consider and also Service as an I. T tool but

25:17 also Service as an I. T tool but Microsoft bundled It Up made it in a package that’s easier to build and said here you go here’s power bi with this analysis services for free in this tool and I think to me that was the big moment yeah I agree it’s the framework by the way the model all of it works right I guess my point was I I would have started the transition with Excel and we went to the opposite Spectrum where I I agree with him in the fact that power bi is the accelerator for wave two like it I think it took over there is no

25:48 I think it took over there is no argument now that it took the backbone of like what Tableau introduced to businesses and just ran with it and made it much more powerful because of the things that you’re talking about yeah ease of connections like like doing your own ETL cleaning up data model where all the like I have just really almost Limitless ability to like handle almost the vast majority of any business correlation right yeah we hit the limits but we’re also like confirming Enterprise level data at the

26:19 confirming Enterprise level data at the thing and it still supports it yeah so the Spectrum right like right yeah like the spectrum of just how how much power there is in here I absolutely agree with and the fact that I don’t have the right like SQL like the fact that I can do all that without writing like hundreds of lines of SQL code to get an answer out right that means that’s that’s the point you write use right abuse has gone really up and that’s the a great point I think he makes in the wave two transition right yes was wave to allowed a way around I. T and and really make

26:52 a way around I. T and and really make analytics for the analyst like tools for them that can be created buy them right so both of these tools do it but yes we love power bi for that reason and I like there is no competitor of that right now and you said it he said it there in the article yeah it’s an end run around I. T right it was powered to the business people here’s a here’s a tool that don’t doesn’t need a lot of extra coding power here you go and this is where I hate the term but people use it all the time it’s

27:22 term but people use it all the time it’s Shadow I. T is it really Shadow RT or is it just people looking at insights and I I’d really I was for a period of time I called myself oh yeah I’m the shadow I. T person right I’m in Excel building all these things and I’m like I don’t really like that term because the business is the people who needed the data to begin with to make things actionable and usable yeah so to call us Shadow I. T it’s it’s almost like a it’s almost like a slap on the wrist like you don’t know what you’re doing your Shadow I. T but is that really what’s occurring here

27:53 but is that really what’s occurring here or is it there yes but here’s the thing here’s the thing right like you say that and you don’t like it but yet one of the coin phrases we have is Think Like the business act like I. T agreed both it’s both of those things though and and I think that this moniker of like Shadow I. T comes around where you have business folks that aren’t thinking about the things that they’re building or their processes in ways that

28:25 building or their processes in ways that we would term I. T or development Centric and you get yourself into trouble there well we’re finally I think from the bi point of view evolving out of the I. T space I heard a friend I’ve told them what they’ve done and they explained to someone else oh he works with computers so we’re we’re still on that side so yeah we’re still on the it like people are still considering I. T space and I’ve said this from episode one I don’t think we’re I. T what we do I I think to your point though Seth I

28:55 I I think to your point though Seth I I agree with your statement there mean I I agree with your statement there I do disagree with the term Shadow I. T because I think the data is now just moving closer to the business side and now I do agree that there are differences there either are lent like this is where I like a lot of the governance piece of the talk right there’s a lot of data that needs to be just built just needs to be built it doesn’t need a lot of technically technically we’re all huge proponents of Shadow I. T according to Wikipedia but that’s a true statement basically it says in big order organization Shadow I. T refers to your

29:26 organization Shadow I. T refers to your information technology systems deployed by departments other than the central I. T Department to work around the perceived or actual shortcomings of central Information Systems there you goit often introduces security and compliance concerns Yes actually forget it the whole conversation where we are we are Shadow I. T but but while I while I understand that right I think and this is and this is where I would argue the role of it has now changed right so so a good

29:56 has now changed right so so a good degree it should be changing it and that’s I think if organizations who realize the role of it is now adjusting and I am no longer the ITT group that has to build the data build the mod models build the cubes build the universe build whatever the things and then output a single page or allow users in the business side to drag and drop variables together to make a report right I think this is but I agree with this and this is why Cloud platforms

30:22 this and this is why Cloud platforms like Azure are so powerful for businesses because if your centralized I. T owns the tenant yes you can provide these services in a security compliant way to all these business units right if your business usually wants to spool up the service and it’s useful for them go for it yeah it doesn’t care they don’t need to get involved but they would control who has access control the costs they would be having those conversations with you right and that’s where something like that I think is much more

30:52 something like that I think is much more like a much better design and there’s got to be like people who are Way Beyond the management of and structure of those things but that like that’s the scale that we I think businesses in general are pursuing now as opposed to to the way way Shadow I. T was be because in the past there was no work around there was no way to to do something so you just have a business you’re gonna be like I’m doing it anyway yeah I’m gonna create my own source of information which would be hugely problematic for an organization

31:23 hugely problematic for an organization because they wouldn’t have access to any of that yeah here’s the interesting thing about the new age we’re going to if you think about the first stage second age second age was the need for Access and now that’s no longer in a sense and I don’t say it’s a need visa to given and one of the things Ryan says is this new age is we’re going to tools and solutions that are a narrow way to tell a story which is interesting because we’re going we went from narrow because we had we we had to with Excel

31:53 because we had we we had to with Excel we went from wide with power bi where’s like all the data which we had reports that and now with the biggest issues were overlapped and according to Ryan we’re going to go to a narrow scope again again because we need not because that’s the only access we have I don’t know if that’s what he’s proposing but I’m sure we’ll talk about a second article I I think yeah so that to be continued what was it what was Mike the the one the one point you really liked was his his representation of the wave two in like a logistics yes

32:24 of the wave two in like a logistics yes a pipeline yeah yeah I I liked his analogy of think of it like a truck right we’ve we’ve hyper made an efficient pipeline to get data from where it was living change get it on a truck and deliver it to this to the house right so basically it’s like a logistics company right they’ve become an Amazon of data right there’s there’s data living somewhere someone’s producing the data is being generated somewhere it’s getting stuck on a truck and then we’re able to funnel it over pipe it out to individual houses where people can consume and use that data so I thought

32:55 consume and use that data so I thought it was a very good analogy when we start talking about wave three there’s definitely going to be new tools showing up it’s going to be very interesting to see see again I think we’re on a cusp of people are trying to not throw away anything that’s wave two but they’re trying to supplement Wave 2 tools with Wave 3 Type technology right I’m thinking like these are like AI this is chat GPT this is analyzing models like there’s some crazy really good models that can analyze your Excel sheet and spit out a whole bunch

33:25 Excel sheet and spit out a whole bunch of insights you can ask questions about your Excel sheet and it just tells you how it’s being done so the the stuff that it’s going to be able to do here in the near future is going to be mind-blowing because I think these new language models large language models we hope really help right well we’ve seen a lot of we’ve seen a lot of failed attempts so far right we’ve seen a lot of really AI based features inside power bi today that are just not very useful which I think it’s good though that means at some point Something’s Gonna stick I think if you look at the points of the

33:56 I think if you look at the points of the challenges of wave two he is spot on there are a lot of things that we’ve talked about right yeah like these centralized warehouses full of unused and unwanted data brittle pipelines that fail dozens or hundreds of dashboards that have slightly different metrics right conflicting records or metrics like which is the bane of Tommy’s existence extreme duplication of work or data content right like and this these these are a lot of the things that we talk about on the podcast as far as implementation and how do you solve this and whatnot so hey if somebody creates a

34:26 and whatnot so hey if somebody creates a tool to solve those problems right but it would be great because this is this is like I think one of the biggest wastes of current Wave 2 is you have this Enterprise knowledge and this this like a great theory of centralizing data butted up against the business taking off and analyst building their own things and how do you elevate that into a realm of like these teams working together I. E the business getting the most value out of the data that it has

34:56 most value out of the data that it has across these tool sets and right now there’s just a lot of disconnected pieces that we’re trying to figure out I’d agree with that well I think we’ve gone through a pretty good job on this first super data Blog the article is really round the the future of business intelligence and where we’re going to start here so I think this is a very good article I think we’re ending the near the ending the age of the second age of analytics we’re looking at there’s gonna be some new innovation coming across here that’s going to be very interesting

35:26 here that’s going to be very interesting and we’ll see where it goes it’ll be pretty fun if you like this conversation if you liked what we’re talking about here if this added some value to your day you like reading the article you like thinking about these things this is the water cooler conversation around power bi and data things we’d love for you to engage the conversation feel free to chat on the comment on the video and our only ask if you get all this content for free is to let somebody else know that you like this content share with somebody else put down your thoughts share on social media what was your what do you think

35:56 media what was your what do you think about the second age of analytics give us some other thoughts there we would love to hear those comments as well and your thoughts on this topic Tommy where else can you find the podcast you can find out on Apple Spotify Google podcasts anywhere they’re available make sure to subscribe on Apple and Spotify leave a rating helps list out a ton if you want to join the conversation Live join the community you can do so every Tuesday and Thursday 7 30 a. m Central on power bi tips social channels excellent thank you all very much and we’ll see you next time

Thank You

Thanks for tuning in! If you enjoyed this episode, please subscribe to the podcast and share it with a colleague.