Knowing Data vs Knowing THE Data – Ep. 379

Mike, Tommy, and Seth unpack how much ‘knowing data’ matters versus knowing the data in your organization, and what that means for your career in analytics. They also dig into practical ways to add narrative context to KPI snapshots—without losing the thread over time.

News & Announcements

• Dimensional Modeling Techniques (Kimball Group) — A hub page of the “official” Kimball dimensional modeling techniques, grouped by concept (grain, dimensions, facts, conformed dimensions, SCDs, etc.). If you’re trying to level up your modeling fundamentals (or explain why star schema choices matter), this is a great reference list to bookmark.

• Got an idea or topic for a future episode? — This short link currently redirects to a PowerBI.tips page that returns a 404. If you’re looking for a working submission form, the podcast page below typically includes a “Mail Bag” / question submission link.

• Subscribe / listen to the Explicit Measures Podcast — The home base for the show, including where to listen and how to catch episodes live. It’s also a handy starting point for sharing the podcast with your team (Spotify/Apple links and the general “what this show is about”).

• Join Tips+ (Theme Generator) — This links to the Power BI Theme Generator experience from PowerBI.tips. If you’re standardizing report design across a team, it’s a quick way to generate consistent theme JSON and keep branding from becoming an afterthought.

• Mike Carlo on LinkedIn — Follow Mike for Power BI/Fabric content and updates on what the team is building at PowerBI.tips. It’s also the easiest place to catch episode announcements when they go live.

• Seth Bauer on LinkedIn — Connect with Seth for practical, real-world Power BI perspectives (especially around how features land inside organizations). Great follow if you like “how would this actually work in a business?” conversations.

• Tommy Puglia on LinkedIn — Follow Tommy for Power BI feature takes, modeling/reporting patterns, and the occasional spicy opinion about what actually helps users. If you’re into the “why” behind the feature, Tommy’s your guy.

Main Discussion: Knowing Data vs Knowing THE Data

A big theme in this episode is the difference between:

• Knowing data (generally) — being strong on modeling patterns, visualization, DAX fundamentals, and how to ask good questions.
• Knowing the data (specifically) — understanding the organization’s KPIs, definitions, edge cases, and the operational reality behind the numbers.

Mike, Tommy, and Seth talk through how those two skill sets show up differently at work:

• General skills help you build and scale solutions.
• Domain knowledge helps you earn trust, avoid semantic landmines, and make insights actionable.

They also connect it to career growth: if you want to move faster, you can’t stay purely “tool-focused.” At some point, being credible means you can explain why a metric moved, what changed in the business, and what decision should follow.

Adding narrative context to KPI snapshots (and why it’s hard)

The mailbag question focuses on a common workflow: one team builds and publishes the report, then another team (often closer to the business) needs to add commentary before the snapshot gets delivered.

Key points from the discussion:

• PowerPoint isn’t just a necessary evil here—it can be a pragmatic “delivery layer” when you need narration, callouts, and a story around visuals.
• Mike describes the ideal experience as something like sticky-note annotations on top of a report state (think: bookmarks + comments + saved context) so the narrative lives with the data.

Power BI options: bookmarks, comments, and metrics/scorecards

They walk through a few “what can you do today?” options:

• Power BI in PowerPoint (with snapshotting) can work well for packaging a monthly narrative without forcing non-report-builders into the full authoring experience.
• Metrics / Scorecards / Metric sets can be a better long-term home for the ongoing story of a KPI: owners, status, updates, and the running history of “why did this change?” over time.

The big takeaway: if the narrative is important, you need a place for it that doesn’t get lost in a pile of monthly decks.

Looking Forward

This episode points at a future-state many teams want: a reporting workflow where context travels with the metric—not just with the monthly artifact. Whether that’s Power BI metrics/scorecards, a Teams-based experience, or something else, the direction is clear: the story has to be searchable, persistent, and tied to ownership.

Episode Transcript

0:34 good morning and welcome back to the explicit MERS podcast with Tommy Seth and Mike good morning everyone good morning and a happy Tuesday gentlemen that it is jumping back into our Tuesdays just a real quick notice here we are doing a couple pre-recorded episodes we had some people traveling this week so can’t do a full long episode so these are pre-recorded just FYI for those who are jumping in on live sessions that being said though we’re going to jump

1:04 being said though we’re going to jump right into our main topic today we’re going to do a little bit of a shorter episode here we’re going to pull right something from our mailbag Tommy you want to kick us into a mailbag here of what we’re going to talk about I would love to but we have our our default mailback speaker here just give us a topic Seth we’ll read it yeah all right so we’re going to talk about knowing the data versus knowing data and we’re KN and really going into you might know a lot of data you might know all things in powerbi but what

1:34 know all things in powerbi but what about an organization’s data what does it actually mean from building your own career career okay Seth want to give us a read after there’s really not a lot to read on this one it’s a very short one and and unlike other mailbags I I think Tommy’s summary and description will Encompass more detail than the question itself but okay probably why he could like hesitated for a moment the question presented by no one is to what extent does knowing data

2:05 one is to what extent does knowing data matter versus knowing the data in an organization when building a career in data so I’m not sure how to interpret this question a little bit I’d be curious Seth or Tommy how would you want to interpret this question what is what is the difference between knowing data versus knowing the organizational data maybe that’s where I’m maybe that’s what I’m looking for for the distinction here yeah I think yeah go ahead I my from my perspective it’s it’s a

2:36 from my perspective it’s it’s a different way of saying what is the difference between being a subject matter expert in a business area versus a bi developer okay and and that’s the same way I would put it too especially when we’re talking about a lot of times we may know a lot of where the tables are maybe we know a lot of the the base metrics but I think it’s that pressure points and what’s important to a organization or a team like hey I know what quota is I know what members are

3:07 what quota is I know what members are but I may not know how much that matters to certain teams and also if it goes up and goes down by how much how much that can actually influence the behavior and the mood or the the the culture of a of a team I’m interesting yeah this is an interesting question I you said subject matter expert and you’re are you comparing like a subject matter expert and the data analyst like what would be just a general analyst same I I would I would consider somebody who’s a data

3:37 would consider somebody who’s a data analy in a business area to eventually be a subject matter expert if they obviously don’t start off that way right but you’re you’re pouring through data to understand a lot of the Nuance of how things work decisions that are made what you need to do etc etc I think the Nuance of this question lies in it it looks like somebody asking like what when you’re building your career what’s what is more pertinent and my

4:08 what’s what is more pertinent and my mind goes instantly somewhere else because ba based P based purely on I think a lot of our conversations the center of excellence when you data literacy data culture directly impact one side of these thing one side of this question yes versus the other which is just just innate a lot of not or or or earned you you you learn things about the business and the more you learn the smarter you are about in

4:40 you learn the smarter you are about in making better decisions but how you really scale up in your career I think is directly impacted by the other I guess I I would I like your kind other I guess I I would I like your take on this a bit more if we’re of take on this a bit more if we’re talking about this in the career space of your data career right so how how do you Leverage The data are you do you just know about data or do data from the organization how do you use that to scale your career I guess maybe where I’m reading the line between the lines here a bit is I’m interpreting this as if you can

5:12 I’m interpreting this as if you can listen to what people are asking for and help them formulate the questions they need to answer to be actionable that sounds to me like what you’d want to be doing here right so I I know information about my company I am trying to figure out what are the next steps what do we need to do what do I look at a for a report how do I walk away from that report and then take action on things

5:35 report and then take action on things I was just talking with a company recently and that was one of their main goals was we know we can build a lot of reports but we want them to be important we want them to someone to look at them and walk away and do something after that and I think maybe that’s where I’m leaning a bit more of like the difference between knowing data and knowing the data of your organization do you understand which questions to directly directly ask will help you build the report in a way that you look at it you see numbers are down you leave and you go do

6:06 are down you leave and you go do something you go talk to a team you go talk to the salesperson more recently I’ve had a number of people just reach up like family members and other people that are near me like Hey we’re actually using powerbi and we’re doing this stuff they want to show me like their reports and things that they’re working on I’m like yeah that’s they’re like we want to look at the high level numbers let’s compare month over month I’m like yeah that’s what we should be doing right you should be able to walk away from that report and say I need to do two things what are those two

6:38 things yeah go ahead this is what I love what you’re saying here too because it’s that domain specific knowledge where I think a lot of people struggle with and I I it’s one of those things that I don’t think they realize that they do struggle with in terms of being able to relate to a team on what they want because it’s very easy to build a report when they say I want to see sales or I want to see leads or conversions I want to see it by month and most people if with the technical knowledge can do that pretty pretty straightforwardly but the problem is

7:09 straightforwardly but the problem is those reports I’m not saying they’re not insightful but is that really those pressure points or the the decision-making parts of a team if you build I think exactly how people say I don’t think that becomes very impactful for teams having that domain specific knowledge I think requires though too like what are you trying to do with that and knowing even sometimes without asking the questions like you without asking the questions like what that team does with X metric

7:39 know what that team does with X metric or Y metric why that’s important to them I think I’ve talked about on this podcast before but one of my coolest instances of This was was a giant call center and they’re like we just want to see every all these 18 or 35 metrics and we want to see in the table by day by Asian was like okay I can find that I can get that for you we can build those metrics but thinking about what that

8:09 metrics but thinking about what that would look like well what are you actually doing with that and actually realizing that the whole idea what they were trying to do even though that would have the data in it in that page be very hard to decipher what they’re actually trying to get out of that that they actually want to see what teams are available and who should they contact when something’s going wrong it’s like okay that’s very different from what you asked for so yeah I may know where the data is and create those metrics but it’s trying to also know like what are you trying to do with that

8:40 like what are you trying to do with that this person who’s talking to me this department what are they going to be doing with it one of the part this question that I want to emphasis on here and I think I I resonate with your point Tommy but I want to expand this a bit maybe is what do we do when we’re talking about the career of your data or you’re expanding your career into the space of data what should you be focusing on and I might my question in my mind is where do I go find this knowledge right if we’re talking about these things of how do I ask these questions how do I ask impactful things

9:11 questions how do I ask impactful things where do you discover this I’m curious have you read any books recently have you are there some things that guys have like impacted you to help you focus a bit more on this actionable based reporting for data and I think that’s that would be the difference between just knowing and knowing the data of your organization I think we’re plagued with the the experience like we we’ve learned through the the career experiences and that’s where where we land I I think the

9:43 where where we land I I think the conversation where you guys have had as a a like step two in my mind as far as like driving into better insights actions coming out of reports Etc because fundamentally what I find is people just don’t when I say they don’t understand how to use data it it’s specifically focused or pointed at I think more of the data in the basic data engineering right e even sometimes like what is a table what is a table of information how is that constructed

10:16 information how is that constructed right what what does that mean to like manipulate this table of data right how do I join it to other things and that could be like in my mind I think of a SQL right I’m join I’m using a language of tsql to join tables together that I can manipulate the data to have different outputs with other data in different ways but you can do that in Excel you can do that in in other tools so how do I combine data together and

10:46 so how do I combine data together and manipulate it and shape it and and to me those fundamentals of just understanding what is what the capabilities of tool sets are around tables of information to produce results that I need opens the door to the conversation of or or I should say adds the constructs needed to get to the

11:11 the constructs needed to get to the second place of like I need this report and I need it to show me this stuff or we need actions that come out of this but people have no idea how to get there and I think that’s where where this question leads in is like knowing data versus knowing the data it’s much more important to understand the structures of how to manipulate data sets and that’s why powerbi is a great fundamental tool with a visual interface

11:42 fundamental tool with a visual interface for you to do that using power query right and where we talk a lot is more in the advanced spaces or places that you upgrade Enterprise or whatever there’s a million different ways but ultimately I think that’s where you start is just do I know what a data source is right like how do I connect to something and get access to information and recognize that these objects that where we typically reference as tables are interconnected

12:14 reference as tables are interconnected typically and how do they connect together and what should I expect to produce when I join these things together in some way and how do I manipulate the data to remove things like adding filters by modifying an output by standardizing values and and why am I doing all this stuff because the end goal is well I want four clean buckets for the organization values and and why am I doing all this stuff

12:45 and why am I doing all this stuff because the end goal is well I want four clean buckets for the organization and I want to put the the the dollar amounts in those four buckets but I have 20 in my data set how do I get to the four four right and and I think those are all the the pieces that business intelligence teams and Coes and Center like all of this stuff brings to the table is those those technical pieces of understanding

13:16 those technical pieces of understanding to the lay business user so understanding the business is extremely important where you get these I think fantastic synergies in organizations is where those two those teams are working together to just inherently cross collaborate all the time and build Val more value to each other in the organization because it is purely this question it’s people who know the data the B the the the Nuance of the data and the people that know how

13:47 of the data and the people that know how to manipulate that or build that data model behind the scenes which is I’m taking everything you’re saying and this is code to me right it’s your business lot like are you business user thinking about your knowledge in the aspects of hey we stored that table here that what you’re saying is we need this filter and this filter and then we’re going to join it into this table and then we’re gonna have this filter and this filter like even if you can bring them closer to that thought process yeah I

14:17 that thought process yeah I think that’s what we say knowing the data right matters right because we’re bringing the technology aspect of all of this stuff to create things that scale versus somebody just spewing what they know about the business and I think that’s the the the really impactful part that business intelligence brings to companies is that scaling of people because without that they’re they’re just stuck in this Roo like room where

14:47 just stuck in this Roo like room where they know tons about the business but they’re inept at being able to take it further into the tools to help themselves and and the organization interesting when you were talking I I had maybe another moment here or another thought around this was you said something about Excel you can do all your analysis inside Excel which I’ve see a lot of companies just do that that’s the skill I think that’s maybe one of the skill of the business that they learn first or have access to it may not be it may be it may not be the

15:17 may not be it may be it may not be the thing you desire to learn it’s just kind thing you desire to learn it’s just the tool that you are given and of the tool that you are given and you have free reign to build whatever you want inside Excel but that being said one of the things I I’m hanging my hat on here I think a little bit is the automation level something you had mentioned like do it in Excel fine interesting okay well you want to be able to repeat that output over and over again like Excel is not easily automatable or it’s not easily automatable in a way that you can give to other people so I think

15:47 give to other people so I think if I’m talking about just the difference between knowing the data and being able to be impactful for the organization right you’re you’re answering those questions you’re working with the business you’re understanding what data they need you’re you’re figuring out there’s a raw form of data it’s in the transactional layer and you need to get it into a reporting layer you need to aggregate things build stuff you’re basically making a mental in my mind when as you were speaking I’m thinking there’s individuals making a me a mental data model and you have to take that mental mental data model and push it into automation I’m going to run this

16:19 it into automation I’m going to run this pipeline I’m going to pick up this table from here I’m going to transform it I’m going to put it there and then it’s going to have the columns and data transforms that I need to build the next thing so it’s more of that mindset of like listening to what people want and figuring out how to automate build a data pipeline to get from one place to what the people need right that if that’s simplifying things a little bit too much I I I think that I think that directly describes the outcome that

16:46 that directly describes the outcome that we want yes right is somebody who in the business who is thinking in terms of the back the structures of where the data comes from yes because that’s how they’re going to be able to answer the question or drive something further for somebody in building a report because they’re thinking in those technical terms of data like it’s the manipulation of of whatever I’m presented with and that’s where I think powerbi is so powerful because it opens that door and

17:18 powerful because it opens that door and even more so now fabric but to a much larger and more complicated scale to people yes is that it offers all of business users the different components that you would need to understand what we mean by are you data literate like can you work with data and if you invested time into the power query parts of that right to really understand like all the Nuance like you can join tables in there you can manipulate you can add columns it’s set based but also you can

17:50 columns it’s set based but also you can replace values you can combine things you can bucket anything from an ETL perspective if you understood what you were doing in there right right connected to multiple different sources like automated that process you’re so far ahead of the game because you’re thinking about okay well where is that data going to come from how am I going to go do this thing and and that is I think Paramount for people to make that next that jump from just being presented data to analyze or being a subject M met

18:23 data to analyze or being a subject M met expert of a business area yeah and understand the concepts of data and dat shaping and modeling to be able to leverage it and then there’s the whole second part of that where okay I’ve I’ve shaped all this like now I have extended capability right now I can I can further build relationships and data models and oh wait right what is this kind what do you mean I have to reshape my data what do you mean that like I should have it in fax Dimensions why would I want to do that and it’s like holy cow wait you’re

18:55 that and it’s like holy cow wait you’re telling me I can create like four different measures and have my sum of sales my sum of sales last year like this year it like all just with calculations mind blown right and that’s where I think people go hit this point where they’ve been working with what they’ve been given for so long and then they hit an aha moment and for me typically in the past it’s been where all of the sudden they like you did something that they were along this track Like great we’re building this I’m

19:25 track Like great we’re building this I’m going to get this data set it’s going to be awesome and then they go wait a minute you just did something with the same data set that would have taken me another two weeks to build and you did it in one calculation yeah right like or I wait I can compare these different time periods against each other right like that’s a common one but it’s like those are the things where like you you win people over but ultimately bringing that side of data modeling

19:58 bringing that side of data modeling manipulation just working with data is what I think programs within organizations can do because then data becomes powerful for everybody and not just the few people who have had the experiences and learned from the technical technical spaces I think we’re stepping into a new land here where a lot of what you were saying there sound spoke to me like you’re you’re talking to a business user or business you’re coming from the business user Centric into more of a it shop however if I if I think about

20:30 shop however if I if I think about what’s going on here in particularly this question it sounds to me like this is a business user question and and what I think Microsoft is doing right now is this is blur what they’re doing with they’re tooling is they’re blurring the line very deeply between business analyst and like data engineer and other tools you can get your hands on right so power query was the first tool that really Blended that world very cleanly right right or or it Blended the world

21:00 right right or or it Blended the world very deeply right a business user could write something in power query I could hand it to someone in the it shop and they would know exactly step by step how did I transform the data right here’s exactly what I need to do to shape this data from this raw form into something that I can send onto my boss and make tables of data that are useful that was huge and I think I’m still leaning on that but now it’s not just powerquery it’s a pipeline it’s a notebook it’s all these other tools that we now have at our disposal and Microsoft is throwing

21:30 our disposal and Microsoft is throwing everything that’s data platform esque and now with the addition of SQL Server inside fabric we’re now bringing the transactional systems directly into fabric as well so we’re adding another Persona here of the operational system directly inside fabric so it we’re I don’t know how to say this right but I’m I feel like what we’re talking about right now in this question is business user Centric however fabric is more of this tool that has many different profiles of users and I think

22:00 different profiles of users and I think if you want to make yourself extremely valuable to the business start if you’re starting from the business unit user level work your way into these other more technical roles data engineer data science app developer like if that interests you you have you have bandwidth to go that direction now that you may not take it all the way in there

22:19 you may not take it all the way in there you may not go all the way dep into the depths of that that area but if that interests you if you’re looking to grow your data career man the best data Engineers I’ve had have been software developers pure and simple like they understand automation they understand how to move things they understand data structures there’s so much fundamentals of app developers that understand what has to KN how to move around with data I give them almost little to no direction and they know exactly what to build like it’s it’s

22:50 exactly what to build like it’s it’s incredible I don’t know I I love the points but to the extent that it matters from the career point of view if going in an organization and again whether I’m FTE or whether I’m a consultant who’s always going to be working with new organizations new data set new data sets sure the automation sure is important those roles from the data engineering sure important but there’s also a skill here too on my ability either to Fast Track or fast learn on a new organization’s data or be very

23:22 a new organization’s data or be very intimate with going through each Department’s data right because I can automate everything it’s amazing I can have those Tech iCal skills but if I’m having trouble just again if I’m just going to rinse and repeat they ask for this I’ll build that in metrics and they’re like wow but I think there’s another step here on that the literacy part of an organization and Really Gonna diveing into those we’ll call them I don’t want to say the the soft side of things but more again around that idea

23:54 things but more again around that idea of pressure points are really measuring what matters to them again they make may say sales they may say member counts but probably it’s over some more obscure context of well I’m trying to see this over two weeks or what I’m really trying to do with this is X Y and Z and I think that yeah and I think that’s a really strong skill to have here because if I’m just the data engineer I’m moving yeah I’m not saying it’s not but you can’t get there without understanding how to build it and that’s

24:24 understanding how to build it and that’s a good point I’m putting this to the how the mailbag was actually phrase here I don’t think he was saying do either does one or like does one matter more than the other but he’s actually saying actually saying that like how much does actually doing the technical skills right Impact versus that knowledge domain I think I think I would argue here a little bit what you’re saying Tomy I think I resonate with Seth I think I understand your point here on the technical like you got to know how to do this I’m going

24:54 you got to know how to do this I’m going through here and I’m thinking like how do you get your knowledge around this one one there’s this is not rocket science it’s and it’s I would say no matter what department you’re in you’re saying time me like questions like hey do you want this data I find that when I walk between many different organizations even though they’re different Industries Financial energy Healthcare they’re all asking the same kinds of questions so if you just study like I’m looking right now at the the Kimble group The Kimble group has this dimensional

25:25 Kimble group has this dimensional modeling techniques page and I’ll put that in the descript description of this chat as well but it’s really it’s really well done and it talks about things like Gathering your business requirements what grain of data do you have what dimensions do you need what is a star schema what does an look like instantly right what is a dimension what is a fact what is like and if you don’t have those basic constructs exactly and that’s what I’m saying that’s where it starts yeah I would so I would say like the qu like yes you’re

25:56 would say like the qu like yes you’re going to cater some of these questions specifically to what one business unit or the other but I think the more you do this the more you’re going to find the questions are all the same they’re very similar in nature what’s my performance now how do I compare that performance to something else and that should be really our main driver we should be talking we should be talking about what are we measuring right now that’s important to us what what keeps you up at night what drives your value to the company then what do you compare that to do you have a goal is it a budget is it last year’s number is it last month’s number I don’t

26:27 number is it last month’s number I don’t know but all those things it’s it’s that stuff every single time and you’re either saying I want to be a higher number than I was last time the same number or lower you can boil it down to that simple question let people discuss and then now once how to once what they’re looking for like in the energy sector we want to bring energy usage down or we want to optimize things great let’s look at something over time what does it look like this month what was it last month same period last year like it’s it’s we’re bringing the numbers lower as opposed to like

26:58 the numbers lower as opposed to like sales you want numbers to go up so so what are we doing to make the numbers go up like to me it’s like very straightforward like what we’re trying to look for yeah I think this this is the difference between being the doer and the receiver right like and and what what didn’t strike me until at like right a few moments ago was yeah the the largest difference between KN like being the subject matter of expert of data versus knowing how to work with the

27:28 data versus knowing how to work with the data is you’ll hit a threshold where you can’t fix anything with like your your analysis like you can’t get to the answer that you need to and you’re going and searching for that answer requires other people it requires access or data manipulation or something you are very right on this and then all of the sudden where does it go it goes to the bi team right because they don’t need to know all everything they just know how to get it for you yep what’s ironic about

27:56 it for you yep what’s ironic about this it that that just struck me is this was was me oh I was the subject matter expert in the business and I I hit the threshold of the application I was working in could only go so far but I knew we had databases I just didn’t know how to access them and I had some really cool folks that were like hey if you’re actually interested we’ll teach you how to do this stuff and and this is where I think yes this is where

28:33 users who if you’re the subject matter expert these tools and Technologies are closer to your understanding than you think and the reason I say that is because the time and effort you put into the functions the the thing you have to do like to manipulate the the data the join the whatever right those you can grasp but because you’re a subject matter expert the data right so you understand whether or

29:04 data right so you understand whether or not the outputs you start creating are valid and that is significant in building something that a lot of business intelligence developers who don’t have that context at least are at a disadvantage because they can’t validate the results they need the subject matter experts to do that but it’s more just the Q& A it’s the analysis yeah if you bring both of those things to the table now you’re super valuable because as you’re learning you can tell

29:36 because as you’re learning you can tell whether or not the data is accurate and you’re you’re self-correcting right so you’re learning faster than others and you’re already validating that the outputs are what you would expect so everything you learn is a value to yourself because you’re learning new skills that allow you to shape and do these things on your own without the need of technical teams that are showing you how how to do that and I think that’s the biggest value behind business intelligence and these efforts to disseminate this information to people because even if you grabbed 10

30:06 people because even if you grabbed 10 other people in New YG that never knew what working with data was this is that how you level people up and all of a sudden a lot more people are serving their own needs Etc but I think this is what also points out is it important to be a subject matter expert yeah absolutely knowing your business being able to speak to it make decisions and articulate things yeah but that but there a threshold there that you’re not going to be able to cross and un building these skill sets in whatever Tooling in whatever method is Paramount

30:38 Tooling in whatever method is Paramount to to solving not just your own Next Level questions but the questions of your business unit or on on a wider scale the organization I like that you went that route I think that makes a lot of sense and I think your comment around when the data the the the knowledge you need to get to use other tools isn’t as far away as you think I think and and I think now with fabric again not me necessar talking

31:08 fabric again not me necessar talking about PBI but like talking fabric that’s exactly my point I bring that point a lot to users and say you already know how to shape data you already know how to do data engineering you’re already doing it in Excel with v lookups and multiple tables and like you’re building little mini databases inside Excel but something that’s smaller and you can manage and you can seal the data in table form you’re doing the same thing thing we’re just doing it at scale with more data that you can’t see physically in a table you have to like write queries against them it yeah I would I love that point Seth and I think that’s really the point what what we want to

31:39 really the point what what we want to talk about here if you’re talking about a career data take what as a business user go learn SQL go Learn Python go learn how to automate and take those common questions you’ve always been asking what are we comparing what’s the time period how do we what’s what is what is a measure of success or not and build around that space and that will I think that will definitely catapult your career further down that that that data space cool well the thing I actually this is a good stopping point for us we

32:09 this is a good stopping point for us we need to keep this one short because we have to record some more stuff as well so for that being said this is going to be a short episode I will put the link in the description here for the Kimble group really some good information here about talking about grain of data facts and measurements Dimensions star schema really good information solid information that I think that business business user should know in order to move into that data career further that being said we just really appreciate your time thank you for listening with us today hope this question was something that maybe is either on your heart or you thought about it or you’re

32:39 heart or you thought about it or you’re thinking about these things as well hopefully some of this gives you some food for thought push you maybe further towards that data engineering level or if you’re a data engineer maybe pulling some things out of this and looking and thinking about a business analyst level as well or what you can incorporate there as well Tommy where else can you find the podcast you can find us on Apple Spotify or every year podcast make sure to subscribe and leave a rating it helps out a ton do you have a question idea or topic that you want us to talk about a future episode head over to power. tip podcast leave your

33:09 over to power. tip podcast leave your name and a great question and join us live every Tuesday and Thursday a. m. Central and join the conversation all powerbi tips social media channels awesome thank you so much we appreciate your time we’ll let you go early today here’s an extra 30 minutes back to your day have a great day see you bye [Music]

Thank You

Want to catch us live? Join every Tuesday and Thursday at 7:30 AM Central on YouTube and LinkedIn.

Got a question? Head to powerbi.tips/empodcast and submit your topic ideas.

Listen on Spotify, Apple Podcasts, or wherever you get your podcasts.