Are we data driven? – Ep. 244

Everyone wants to be data-driven—until it’s time to agree on definitions, change a meeting agenda, or stop using the spreadsheet that ‘feels right.’

In Episode 244, Mike, Tommy, and Seth talk about the real signals of data-driven behavior: decisions that reference a shared metric, teams that trust the semantic layer, and a culture where insights actually change what happens next.

News & Announcements

• Companies are failing in their efforts to become data-driven — A strong reminder that tools don’t create a data culture by themselves; incentives, process, and clarity on “what good looks like” are what make metrics matter.

• Submit a topic idea — Share questions, debate topics, or real-world scenarios you want the crew to tackle.

• Subscribe to the Explicit Measures podcast — Catch new episodes and browse the full back catalog.

• Tips+ Theme Generator — Generate consistent report themes in minutes.

• Mike Carlo on LinkedIn — Episode updates, demos, and Power BI/Fabric tips.

• Seth Bauer on LinkedIn — Practical engineering perspective on analytics delivery.

• Tommy Puglia on LinkedIn — Leadership and real-world BI lessons from the field.

Main Discussion

‘Data-driven’ is easy to claim and surprisingly hard to prove. The real test is whether the organization’s decision process changes: do people show up with the same metric, trust the number, and act on it—or do meetings devolve into debates about whose spreadsheet is right?

Key takeaways:

• The most reliable indicator of being data-driven is decision traceability: you can point to a decision, the metric that informed it, and what changed afterwards.
• Start with the decision, then design the metric: if you can’t answer ‘what will we do differently when this moves?’, it’s not a KPI yet.
• Shared definitions are leverage: a trusted semantic model and documented measures scale faster than a hundred bespoke reports.
• Adoption is engineered: put the metric where the decision happens (agenda, workflow, alerts), not just in a dashboard folder.
• Measure impact, not clicks: cycle time, rework, exception rates, and fewer “which number is right?” conversations are better success signals than page views.
• Governance is an operating model: ownership, change control, and communication are what keep a metric stable as the business evolves.
• Fewer, better metrics beat a giant catalog: a small set of agreed-upon measures compounds trust and accelerates decision-making.

Looking Forward

Pick one recurring decision, define one trusted metric for it, and build the habit of reviewing that metric before the decision gets made.

Episode Transcript

0:09 thank you [Music] good morning everyone welcome back to the explicit measures podcast with Tommy Seth and Mike hello hello hello and welcome welcome back another day another dollar another day

0:40 another day another dollar another day holler holler I have no idea what day it is is Tommy is just in that work mode no I Tommy is just in that work mode no like for us when we’re actually mean like for us when we’re actually doing this that’s true what day is it it is Thursday it’s a Thursday okay I only know things by numbers now based on episodes episodes we’ve been rocking up a couple of recorded episodes here recently so we are traveling and and people are moving around so we had to record a couple episodes prior so this is a

1:10 couple episodes prior so this is a recorded episode for those of you who are following me along on podcast people are on the on this on my brain’s still warming up apparently this morning it is a morning so I’m still getting things fired up here I need some more coffee oh boy anyways let’s jump into some of our article things for today today we’re talking about companies are failing in their efforts to become data driven an article by Randy bean and Thomas Davenport off of the Harvard Business review

1:40 Business review Harvard Business Review comes out with a lot of really good stuff I I feel like whenever I read things from there it’s it’s fairly insightful and I like where they’re going with a lot of these articles and things so so I guess high level is companies are struggling to become data driven or there’s this initiative that has been trying to get this data-driven culture started and they’re doing some surveys and they’re finding that while we say or talk a lot about being data driven companies are

2:11 data driven companies are finding it’s harder than they thought or it’s a struggle an uphill battle to some degree degree so so some interesting survey points at the beginning of the article which we’ll probably get into and talk about a bit more there but let’s go let’s start with what is data driven meaning I guess that’s kind data driven meaning I guess that’s a core concept of this article a of a core concept of this article a company that is data driven how does that look for you for you guys mommy what does that look like to you well it’s going to get into the fun part of the article it is the buzzword right we use that in our Consulting we totally people that’s like we want we’re helping

2:42 people that’s like we want we’re helping to be data driven yeah make decisions on data and I think there’s there’s the difference between like the macro like me as an individual and then what the company is actually choosing to do do I think interesting so can you elaborate on that a bit Yeah I think for an organization they have whatever data-driven numbers are doing they’re acting within different departments in the corporate goals I we have a corporate goal for the sales we have a corporate goal for increasing here therefore I need to know what’s affecting that in my performance

3:12 affecting that in my performance something micro is number of reports I turn out doing things on time so it’s very micro but for an organization to be data driven it’s dependent on their department is dependent on the corporate goals itself okay Seth how would you define a data driven company and what does that look like versus what does that not look like maybe maybe yeah I think I think my definition is probably different and it it relates more to the

3:43 different and it it relates more to the outcomes of activities that are trying to make it a company date driven I think I agree with Tommy from the standpoint that obviously all this should line to an organizational goal but part of part of what makes a company data driven is I I think two factors one is automating data data and data processes so that it’s timely like you’re getting the information you need when you need it or without really long delays

4:13 it or without really long delays and you are you are building infrastructure or processes that remove opinion opinion versus versus application of source right and and I think that’s the biggest thing to me from a data driven perspective is throughout an organization you’re always being tasked with putting numbers together or coming up with a story based on information in systems and

4:43 on information in systems and either that’s a lot of the times that’s manual it’s like I keep my own spreadsheet on the side like or this is how I’m running my business but it doesn’t tie back to Source systems and then that’s an opinion right I got that point and the worst cases are like okay I’m gonna I’m gonna put together a a slide deck and as I enter in the table of information I’m just going to manually put it in based on hearsay right like this that is significantly different than having a

5:15 having a data source that an organization is using to store the information that everybody uses a lot utilizing and that’s where you’re pulling your your numbers from I really like your your definition there of automation or the automating how how much automation I think think if I had to put data culture or data driven driven but could we is this is this part of the conversation around trust in your data so what I hear you saying is that there’s a lot of there’s there’s regular patterns of data getting two people who need to do the analysis

5:46 two people who need to do the analysis whether whether it be a report a cube that’s refreshed constantly but there it seems like there’s this this idea or this rigor around does the data come out correctly correctly can the data be refreshed or repeatedly updated with with new information and then making decisions on that automated or pattern of there’s a trust level right we’re actually making trust trust business-based decisions on top of that that pipeline of information

6:16 that that pipeline of information yeah I think quality is certainly a component of that yeah I wouldn’t say it’s all of it because it’s basically it’s it’s that accessibility nature right okay you are curating data sets to a point where it makes sense to the business and through that is absolutely quality checks right yeah because otherwise you’re not going to be data driven because people aren’t going to trust the quality of the data and they’re going to try to Source it from somewhere else and that’s probably one of the death nulls of like trying to spool up an initiative

6:46 of like trying to spool up an initiative right around the business unit if and I I think one of the distinctions between like this article and many others that drive into this challenge which is a failed attempt at doing this usually is a result of Technology teams going and trying to build it with on their own without input from the business yes right and if you’re trying to be data driven and the business then starts to interact with data they

7:16 starts to interact with data they haven’t seen or talked to or touched in like a year yeah it falls off the rails pretty quickly quickly yeah so it’s funny because the what you’re talking about I would basically assuming that the data is already there the alliance that we’re actually getting the data in allow me to cut I don’t think that’s always I think I think it’s a good assumption but I would say that’s probably a portion of where a lot of these companies struggle with yeah is is to that point like that’s you can’t really be data driven if you don’t have rigor around being able to build regular data pipelines of information

7:48 data pipelines of information to regularly show up but even then I think I’ll take it just a quick higher level there’s really all simplified as best I can there’s really three elements that are going to make a company data driven An Origin need is measuring it and acting and I think even with all the technology they have to be able to say why they’re going to be looking at a certain metric or number measuring it and that’s going into the actually getting the data fed in but then the acting too like that we’re actually making decisions off of this and I think

8:20 making decisions off of this and I think those three components with go into what makes up a data driven organization because with just the automation then it’s just it’s more that way our data feeder but we might not be acting on it that’s a meaning that’s a that’s a good point I I think there’s what your comment alludes to me Tommy is there seems to be something around investment upper leadership investment in the the quality of the data and I think in the

8:50 data and I think in the beginning part of the article it talks about companies want to be data driven they want to compete on analytics we’re trying to become AI first type organization so there’s like these buzzwords that we’re hearing here competing on analytics I think can totally make sense and I definitely had some portions in my career where I definitely felt like I was competing on analytics that was the main thing that we use to to bring people along and particularly as

9:15 bring people along and particularly as we were selling products to our customers we were using data and analytics to go summarize sales that were coming from your from the customer back to them and saying here’s what you back to them and saying here’s what here’s the actions we took XYZ know here’s the actions we took XYZ here’s what this resulted in sales of the last quarter what should we do next to compete again in the next quarter or the next year or what’s our strategy so there’s there’s some high level goals I think that really have to be used in data engine for this thing and I feel like at a parallel I’ll be throwing

9:45 like at a parallel I’ll be throwing analogy to you guys I feel like this feels very much like what I was doing in engineering before I left the engineering engineering phase when when we would do tests in order to have products made you’d had to do physical tests on those products make sure things didn’t break right so if you’re building a plastic case or cover right you’d have various features of that cover and you would go build a mold you would inject it with some plastic or other material and then once you had it you’d have to do all these

10:15 you had it you’d have to do all these tests around it to make sure that it wasn’t it wasn’t going to break in an inappropriate way or was going to actually meet those needs you can do a lot of testing in engineering space like in the computer Upstream but at some point you actually have to make the product you have to start doing real tests on it and so a lot of times we would have to State okay here’s the test we’re going to run this is the test that we deem is going to be accurate as a part of this product whatever that it may be right you would describe it here’s what I’m going to do here’s my setup bing bing bing bing bing okay

10:45 setup bing bing bing bing bing okay here’s pictures of what happened bing bing bing bing okay we ran these products on this test machine for you products on this test machine for months at a time know months at a time what came out was Data like we had actual information numbers okay here’s here’s this we applied a lot of Statistics to it here’s the bell curve here’s the things that fell out of the the bell curve here’s what’s minimum acceptability for this product based on how many rotations it did or whatever right there’s there’s some math there’s some output that produced a result

11:15 result and so we use those numbers to go back to the design and fix the design of the parts physical parts to update them so they could last longer or do what we want to do so it had tolerance that feels really close to what we should be thinking about when we’re talking about a data driven culture we should be able to articulate in that pipeline here’s where the data Came From the Source Tommy to your point right here’s the source our need is and we have to specify what is the objective of the data our need is XYZ things those

11:47 of the data our need is XYZ things those these are the kpis we care about and then the output of that is you run the data through you evaluate the need and then from there you’re taking action do you fix something do you take like it’s it feels very similar then you would be in the minority based on this article well I I well I have an understanding of it but when you so that’s that’s an understate in my head like my mental model of what I feel like that looks like like but how do you get that mental model into everyone in your organization

12:17 into everyone in your organization everyone who’s doing data in your company and how does this look like from a a one word that’s not used in here is this idea of self-service I I’m sometimes I get disillusioned by this self-service word of of data you this self-service word of of data we want a self-service bi know we want a self-service bi do you do you yes I think you do want people to be able to find Value in their own information and their own data but if you don’t have a data-driven culture or if you don’t have practices and teaching people this process of

12:49 and teaching people this process of rooming data down to actions you’re going to get a mixed bag of results I think sorry I said a lot of things there I’ve just putting a lot of ideas together yeah I think the the one last thought I I had around like the end there was like having this idea of everyone within the organization doing self-service is isn’t it is a fallacy right from the standpoint that it’s like Mike you could have these

13:19 Mike you could have these whatever things you’re testing for months on end all this data and some some new hire comes in he’s not going to unders he or she’s not going to understand yeah all of the data coming out of it but they sure could do self-service reporting on it right what it takes is somebody who understands the domain the business totally to to buy the rules and things that you’re trying to measure and test Etc so I I agree with that with like your assessment of like hey like yes all

13:49 your assessment of like hey like yes all of this to some degree deserves bigger and testing and like probably iterative different ways in which you refine and create the best product because it’s not the same test you’re doing over and over yes it’s different types of tests yes to see which is more successful and I think that’s one of the the things we’ve talked about even in larger organizational efforts is the importance of doing tests right like being iterative in your approach of like

14:20 being iterative in your approach of like okay is this going to work how do we apply this how do how do we reach out to these business units and yes get adoption etc etc I think the question I have with Tommy is and maybe you just want to peel this up on a high level but when when we’re talking about like what is what is data culture I guess I was already passed like do we need data culture and that’s where your comments seem to align like origin need and acting on data yeah well if you really the article 72 of the participants said that the reason they

14:51 participants said that the reason they have yet the board to data culture and we’re talking a lot about the technology about the measurements but that’s always that’s never going to be the solution there’s some really gold nuggets in this where even though people are looking for an investment with driving data 72 of the participant participants you have to have a data culture but the big part here is what is it 40 say that the reason why they

15:22 reason why they have not implemented a data-driven initiatives is because of organizational alignment and only seven percent of this survey site technology as an issue only seven percent that was the number I was looking for where we have the technology we have people to do it whether or not it’s in the right place is fine but self-service whatever it is is just access and communication if people do not know what to do with the data the they actually and then also to

15:54 data the they actually and then also to communicate with that they’re doing something because all the things if I’m an executive and I know there’s all these reports how do I know the measurable difference of us having the data and having access to it compared to not I have no way to tell and I think that’s a huge part of this okay I think I’m I think I’m at where you’re going you jumped way like probably five steps down from where I thought I I wanted like I thought we were first right like so yes if we’re

16:24 were first right like so yes if we’re talking about data driven B in the context of the article all over the companies want to be data driven I think what you’re outlining are some of the like the key up front alarming results of the survey where essentially participants are saying like so let’s just go through those 72 percent of the survey participants report they have yet to forge a data culture and that’s where I think Tommy some of your comments are aligning more so so but it makes sense and we can that’s roughly one shift into it right one and four wait one in four companies have

16:55 four wait one in four companies have some semblance of data culture or Yuri out of every four are starting to figure out what that means for them yet to forge one right yeah so they’re struggling already like trying to make decisions based off data yes I agree and report that they have not created a driven organization and to some degree I would say being data driven first is a first step to creating a data culture right so if you’re if we’re yes mental modeling this 53 State they are not yet

17:26 modeling this 53 State they are not yet treating data as a business asset to me that’s more of a bigger miss here two well that that that speaks to a much larger fundamental problem that needs to be yes addressed yeah and then 52 admit they are not competing on data in Analytics Analytics so I think all would now mind you right this is in the context of 2019 right true and problems where big data is exploding there’s a lot of challenges around around access to systems that are designed

17:57 access to systems that are designed to handle that in a a relatively straightforward or easy way but I I contextually speaking where I was addressing some of my initial comments are like what are the outcomes we would expect from a technical implementation of data-driven culture I have access to data as a business person to start making decisions right I think overall origin need acting on it like yes these are the ultimate drivers of

18:28 yes these are the ultimate drivers of why companies like in the past and now because these are very relevant conversations even in today yes because if you look at multiple different other articles really to like this it’s the same type of a problem that keeps coming up it’s not just a technical solution and I’m not saying that just finding Technical Solutions through automation or making data available to customer internal customers is the answer because you would need alignment across the business and and framing it in like how

19:01 business and and framing it in like how you go about building a data-driven culture because this article is saying like like a lot of companies are failing at this right and I think you allude to the reasons why Tommy so I just like I’m mentally caught up thank you I needed to walk through that in my head but you walk through that in my head but ultimately know ultimately there are many ways in which organizations can build build these systems and I think Tommy you started to allude to like why what their findings were in

19:32 to like why what their findings were in here that here that were part of the major major aspects of why it was not working or or doesn’t today today and I think that’s a huge part of this and to to your point I agree with everything you’re saying for the technology part that factor is ever more essential now even through years ago our ability to whether it’s through AI or through web apps to be able to connect and manage and touch our data just forget just power bi or what’s power

20:02 forget just power bi or what’s power bi’s updates have been but I think my own journey through this of being from the technology side and doing the integration I think it lends me towards maybe it’s the other way around where the initiatives where the actual systems come into play is actually having a culture having the the right people in charge who are trying building and doing everything based on numbers or what they’re trying to see and that trickles

20:33 they’re trying to see and that trickles down where you are measured off of this you are going to make decisions off of this and then okay now we need this systems in place to do so because just having the data having access I I that’s the least of people’s issues now be able to so I just I completely disagree with that it is absolutely a huge problem in organizations for people to get access to the right data sets still okay I I understand my experience yeah I

21:04 I I understand my experience yeah I would agree so I agree with both your comments to some degree right I feel like feel like totally to to to set to your point right I agree with there are still challenges with people getting access to the information and trusting the information that comes to them right there’s all these the business asks for reports or information to be presented to them someone’s got to interpret what those mean and go all the way back up to the Reporting System figure out where those tables come from and push them through somewhere there’s also a lot of really need here like when we’re

21:34 lot of really need here like when we’re trying to talk about like competing on data analytics when I start thinking about that level of detail about competing on the analytics that means you’re getting like my opinion here this this may not be totally true but competing in analytics means you’re actually getting competitor level data comparing yourself against industry-wide standards you’re looking at how do we compare against other companies and where do and you’re having literally thought leaders in your data space say what is our competitive Advantage around our data and how can we use the

22:04 our data and how can we use the information we already have or what can we collect to help people make better purchasing decisions or help our company do what we do better what does that look like and I think a lot of internet companies come in the mentality with data like these newer companies right Facebook is probably a bad example but like they’re using all the information of you your friends what you click on what you say they’re using all of that to produce advertising for you to get you to buy something the data the

22:35 to get you to buy something the data the the Facebook element here of being able to make connected people almost seems like a guys at this point it’s it’s all about making money at the end of the day and what they’re incentivized to do is they’re incentivized to get as much information about you as they can to present the best ads in front of you so they can get their ads clicked on bought product whatever whatever that thing is they’re selling that to companies and that’s how the companies are accessing you so to them their their product productized ourselves yeah we’ve productized ourselves and their their entire

23:05 ourselves and their their entire business model is only like compete on analytics like that’s literally what they do so so if I look at this article the basis do you guys so now mind you this is four years old sure a lot has happened a lot data is still grown exponentially we’ve been part of a technology curves obviously fabric even being part of that yeah true and the main do you think that the main failings that this article points out are still still hold true and that would be 93 of respondents

23:36 that would be 93 of respondents identified people and process as the issue issue 40 percent lack of org alignment in 24 cultural resistance still hold true or are like is it still the major problem in trying to build out data cultures and data-driven decision making within an organization so so give me the three numbers again because I got the 93 people in process this is how they they broke it down yeah yeah whether they’re directly correlated I don’t know 93 of

24:08 directly correlated I don’t know 93 of respondents identified people and process issues as the obstacle yes clearly the difficulty I’m just reading from there yeah I really had the difficulty of cultural change has been dramatically underestimating in these underestimated in these leading companies 40 identify lack of organizational alignment and 24 psych cultural resistance as the leading factors contributing to this lack of business adoption yeah I would I would agree all three of those oh yeah now the percentages may have changed but I would

24:39 percentages may have changed but I would still agree that those are probably the top three main issues that we see when we’re talking about data culture because we’re really we’re trying to change people’s behavior and this is something that we’ve always said I think on the podcast at least we talked about it for a while is there’s a three-legged stool it’s the technology it’s the skills of the people and it’s the processes you put in place right and you processes you put in place right and when one of them gets stronger for know when one of them gets stronger for example when you have a stronger technology for example in this case maybe Fabric or power bi you’re getting

25:10 maybe Fabric or power bi you’re getting more capability you’re bringing that closer to where those people need to use and manage and and edit data and again this is one of the reasons why I think we love power bi is the volumes of data that I can go after now with a desktop tool that was free is incredible we could bring in way more rows of data than we could in Excel previously we’re doing way bigger things that we could but again we’re doing all that all of that for a very low cost so we’ve we’ve commoditized the built the technology the technology

25:40 the built the technology the technology is getting so cheap and easy to use it’s become a commodity that is now pointing the finger at well what do the people know how to do with that information I can give them a big table of a million records but do they know how to go sift through that table and actually find what’s actionable what are we doing analytically what are we scaling up our team analytically to help them handle and have the capabilities of this what processes are we putting in place like how do we manage this data culture again I think to my mind here is I keep thinking about Matthew

26:11 here is I keep thinking about Matthew roach’s pyramid right we’re talking about the the different levels of data quality and we have the Enterprise edw it’s highly governed highly regulated for the entire organization then we go all the way down to personal reporting which is very unregulated the process of how do you get something that someone built or a team built into this this high quality important data set for us that’s important to know like how how do you grow up that thing what is the and this is a story that I think we’ve been using a lot Microsoft needs to enhance that grow up

26:42 Microsoft needs to enhance that grow up story story what does a well together report that Mike made and how do we turn that into an Enterprise report that the bi team takes over and we rely upon it for regular information yeah I I think it would I think the argument with Microsoft is it would behoove you to do that because it sells your tool better yeah totally I I think I think the point you bring up that I want to lean into a bit here is is and where I’ve seen data strategies shift shift significantly too right from

27:13 significantly too right from me hey we’re going to build this monolithic Warehouse which still is possible still something that’s valuable or could provide value to the business albeit there’s there’s more typical incremental things that are deployed along the way but like the prevailing theme is clear alignment with the business yes right agree rather than just having technical teams go build out all of the data from these different places and then yes into this giant

27:42 then yes into this giant repository and then create reports the challenge with that is it’s on the other side of all of that processing that you run into these domain business problems and one of the things that I’ve heard and seen a lot of lately and have like frankly started deploying is rather than like the biggest mistake I think organizations make it back in the day that are adjustments are being made to now is rather than building this

28:12 now is rather than building this monolithic single Source without the business business you have to engage the business in their domain knowledge into that process of building building or or alternatively of like find different solutions like data Marts or data mesh implementations and architectures where you’re splitting out those domains so like the true owners of the data quality are the business owners right

28:42 quality are the business owners right and I think that’s the linchpin in a lot of this because you absolutely like I think there’s two components on the output you’re absolutely going to find resistance to change for anything but if the things that you build are not up to Snuff you instantly lose people and so so that that those two like strategies of like ensuring domain knowledge experts from the business units are engaged right away I think is Paramount to these Solutions Solutions working within or being even

29:15 working within or being even having a good starting point to be data driven because all of that business logic is is built in from the beginning across the way so let me ask something and I want to see if I’m off but we’re a lot of our conversation discussion is a real focused on in a sense the the tables the generation of the logic the data itself but not so much on the actual measurements like what we’re actually going to be baking up the end result and

29:46 going to be baking up the end result and I I don’t know I think for me what I’ve seen I is there’s more of a discrepancy with an unclear true measurement of what each department is in a sense gear scene like what’s a win what’s a loss what can we actually move the needle with and how that trickles down obviously we have total sales but what are the other things that an organization is measuring there and are they clearly defined and understood by everyone in the organization are people

30:18 organization are people evaluated and again can in the sense are they Performance Based on that do they have an active role in that and I think that’s why you see seven percent or the 40 lack any initiatives and there’s a culture change it starts at the top but then doesn’t matter if you have the logic in your tables what are we trying to actually measure what’s the final result it’s it’s the same thing with sorry I was going to go on a another rant but to

30:49 was going to go on a another rant but to me I think it that’s where it should start well I think what you’re speaking to is another part in the article it’s halfway down Church talking about and I highlighted this term in the article I thought this was really good short-term financial goals pushes out longer term objectives like data based cultures right a database culture is a longer strategic initiative that is helping you that that is a I don’t know two three five year plan of

31:19 I don’t know two three five year plan of how you’re going to do that right that is a an education you’re gonna you’re gonna put people through training you’re gonna work with them you’re gonna you’re gonna communicate from the leadership level down into the team like we are data driven and here’s what this data driven means I think a lot of times if you say the word but I don’t think companies actually sit down to your point Tommy and Define data driven as this data driven is we have leadership that is caring about these kpis this is what we care about we’re we’re expected to get this growth we

31:49 expected to get this growth we want this Revenue we want these numbers and having just a handful of them Microsoft themselves talked about what kpis do they care about yeah and they had like a hundred and I don’t know what the number was don’t quote me on this one but it was like over like 100 different kpis that they thought was important to them and they’re like there’s no way we can have the entire organization handling and owning all of those kpis across the entire organization so let’s let’s sit back okay yes all these things may be important to various degrees but we have to simplify more and let’s get it down

32:21 to simplify more and let’s get it down to like 50 or 25 or like what are What are the are the less numbers is better in that at that highest level because once you have a clearer objective right we want to maintain this we want to achieve this amount of growth in our business we want to have this level of profitability in our growth when you have those clear objectives now you can then from that initiative roll out a lot of the other things Downstream let me give you just one really quick

32:51 let me give you just one really quick example when I was in engineering there is constant initiatives of building new products and then taking existing products and removing cost those are like the two things that I remember the most about engineering so one was hey there’s a new market offering and a company has a limited budget to spend on people and Building Things Things so so the company had to decide are we going to build something net new or are we going to go back and remove cost from something we already have that we’ve

33:21 something we already have that we’ve built and so those were decision points that were being made but once the decision was made to either build a new product or optimize an existing one that’s what we worked on like that was like okay we’re gonna do a three month project or we’re gonna do a one-year project or whatever the thing is those initiatives were very clearly given to us the team and we worked to get completion of those tasks because it’s a it’s a lot of money to build a new product again I was in the manufacturing space so it’s a little bit different than when you’re talking about like software and other things as well

33:52 like software and other things as well but it’s not right because time time of resources who are not typically cheaply paid resources is the cost true right true you kept sticking your finger up like you’re gonna say so sorry I’ll stop there yeah so the the Articles Mike’s referring to there’s actually three articles that turned out from that they went down from 150 to 35 but the big part of that the three articles are broken to 35 30 metrics kpis oh the ones from the Microsoft Windows yes but that’s correct

34:23 Microsoft Windows yes but that’s correct the other two articles about they because of the the change it wasn’t just oh here’s a new dashboard they had to radically change their infrastructure because of the measurements and what they’re focusing on yes since they were so critical is that we had to do migrations we had to make sure they’re up to date so they talked about pushing it’s a customer SAT scores right started with yes apis and what are what are our benchmarks well that’s interesting you mentioned that because that seems to me like it’s data driven culture right if you’re competing against if

34:53 you’re competing against if you’re Microsoft and you’re competing against Google and Amazon right you’re looking at where is their technology stack what do we have that they don’t have and what can we do to make our customers happy and purchase us over our competitors yeah I think I if we glossed over that then you’re spot on Tommy my assumption here is all of this is being driven by objectives within the organization but it also means that well so I I no no I think I think you’re

35:24 well so I I no no I think I think you’re gonna if you’re gonna if you’re gonna interrupt me go ahead no I just said fine you’re very fine I’m not saying I disagree with you but I see both ways continue to work both ways continue please have you been in our conversations about data culture and adoption that ain’t easy where where where have I come from a standpoint that it’s all technology driven in fact I’m making this opposite no no no no no no no no no no I think Tommy Tommy was Tommy was

35:55 no I think Tommy Tommy was Tommy was making noises and I think that the idea that that there are some like yours your assumption that the corporate objectives were clearly defined and articulated to the organization and I I would agree with Tommy no I think it will vary widely between organizations but your your point of assumption is totally valid because you can assume that that is true I’m not assuming outside of this conversation obviously yes companies don’t do this well yeah yeah correct right I’m

36:26 well yeah yeah correct right I’m assuming in the context of how I’ve been talking about it but Tommy you did a good job of like hey no one of the first things you would need to do if you’re building an organizational strategy around data culture and data driven things are it starts and it starts and ends with the business need the objectives that’s interesting at the top and yes and I agree with that and I’m saying it’s a good thing you you brought that forward into the context of the conversation because my what I was describing was

36:56 because my what I was describing was just with that North Star in mind yeah yeah since we’re talking about Microsoft North Stars yeah yeah exactly but I think I think the new monster is like too twofold one is this is why it’s very important to have a leader in the extended executive leadership team that has buy-in and can make those big cultural moves and say this is what we’re doing and why not in a mandated way but you need to empower people to go across business units to solve these problems

37:26 problems the other thing with clear objectives is not only does it give you the long-term strategy strategy and when you’re all building this stuff it should be it should be towards those goals some of them are near term right that’s where you find your incremental value right all of this should align to immediate needs immediate goals while at the same time serving the interim and long-term strategies and that’s I think the challenge throughout this is you can’t just look at

37:58 this is you can’t just look at one of these areas when you’re implementing but the business certainly wants value right away and all of like guaranteed there’s going to be like objectives that you can aim for that you can start to build the infrastructure and get value out of that in incremental ways but absolutely none of this happens without like the top agreeing to it and how you implement it for sure should all be based on okrs or ogsms or some major

38:30 based on okrs or ogsms or some major initiative I agree that is going to provide value to the business otherwise there’s no reason for the business to do it I think what’s what’s interesting to me is as I read other articles related to the same thing and probably why I got stuck in the back end a lot is you it talks about data quality right and if we don’t have the quality of the data those automated processes and ways in which we’re integrating the business into these structures right out of the gate then

39:00 structures right out of the gate then you don’t have anything as a foundation to move off of in terms of delivery because you could deliver on those objectives to solve a kpi or a metric but it’s going to be wrong and then what happens right the whole thing starts to dissolve because who owns what who’s responsible for the quality of data and if you don’t outline that right from the beginning like you’re saying Tommy that’s the importance I think of a disparate or data mesh or data

39:30 disparate or data mesh or data Mark approach where you’re integrating business and this is where fabric starts to make a lot of sense like integrating business owners because absolutely they need to have skin in the game and Thomas said Performance Based which is absolutely true because if you’re going to define the data source owner it’s absolutely carrot but there’s also a stick and I think that’s the biggest problem that’s the biggest problem in organization still is hey if you’re if

40:00 organization still is hey if you’re if you own this area you’re responsible for the quality of data coming through here but it’s the importance of our teams or business intelligence teams to then refine the and make sure that we’re monitoring the quality of those Source systems so we can identify whether or not somebody’s doing like ultimately are they doing their job and if it’s not then they know who to go to right well this is deep I I love your talking points there Seth

40:32 I I love your talking points there Seth and later on the article towards the end it started talking about hey here’s some ideas that might go along with this one but your point right there really hits on the Note here in the middle of the or of the article that you’re talking about many firms have established hybrid organizations in which they include center of excellences an analytical sandbox Innovation labs to try to drive that effort more rapidly and then at the end of this when they presented these results they said at a recent executive breakfast we organized to host the

41:04 breakfast we organized to host the discussion of the survey results one thing that really stuck out to me there was one suggestion was to not focus on the overall entire company data transformation in a large Enterprise but rather and this is your point exactly but Focus specifically on a couple key projects a couple key initiatives a couple key business initiatives that are going to be we’re going to spend effort and try to focus our data culture on these handful of projects that moves

41:34 on these handful of projects that moves the entire company in a better Direction so like instead of trying to like solve the whole thing at once let’s let’s pick some key initiatives things that we know we can affect work on those and improve the value of those and then you’re slowly moving the culture over time by engaging those teams teams developing and building this new cultural way of thinking around data analytics making sure that it’s valuable to the company and using those key projects as drivers to either to your

42:06 projects as drivers to either to your point Tommy earlier right to align leadership on okay we do care about analytics where can we use it these key specific projects help us get in the wrong direction and I think this is hard it’s very hard to to meet short-term needs I got to get this done today we need the data we need these reports out and also think about how does this data culture thing work in totality do we use deployment pipelines are we building premium per user we

42:36 are we building premium per user we giving everyone Pro licenses what does the organization look like from a workspace management standpoint how do we how do we build that part do we just give every Department their own workspace and let them go to town or do we have some workspaces that are Central to the bi team and then we slowly trickle that out what does that rollout plan or education action plan look like for these projects and that’s to your point Seth I think works really well with organizations where bring in Consultants or do those in Special Projects where you’re bringing in people that are way more knowledgeable in that space build the example and then roll

43:09 space build the example and then roll out across other departments or other initiatives across the company I think but that’s why that’s why it’s so important especially if you if you adopt an okr framework right like as managers As Leaders Etc like you would have to delineate percentages of your team’s time guess who reach near-term midterm and long-term goals agree and and if without that planning that’s where you get into trouble as a business intelligencing because you were saying

43:39 intelligencing because you were saying like especially if you’re responsible for developing all the reports of a business which I just don’t agree with in from the standpoint but it depends on the size of the organization I suppose but that’s where it’s Paramount that you have some execution tool where you can understand like are we allocating the appropriate time am I giving at least 10 percent in a Sprint or in a work like work week Etc towards these long-term strategies otherwise you lose sight of them and then six months down the road the

44:09 and then six months down the road the business is like okay well pivot again you this long this interim mid thing you should be three-fourths of the way through and you’re like we didn’t start it yet we didn’t start it yet well you guys were like we had fire drills all the time we everything was immediate right and that’s where setting levels setting expectations and we’re like the leg in it really helps with bi is like you manage during and you manage those expectations we are going to deliver we can’t deliver on your timeline and your timeline is absolutely ridiculous and unreasonable so if you need to go Cobble

44:40 unreasonable so if you need to go Cobble together something business user because you didn’t plan then you do that but at the same time let’s have the conversation where we build something automated for you but I’m like that’s where like bringing your fire to my doorstep and letting my house on fire it doesn’t work is is the the probably one of the top five hated conversations I have right like yeah like this is how my team runs this is how the organ this is how we stay on track and meeting the objectives of the organization because

45:10 objectives of the organization because we are this team is managed your world obviously is not right so if if the business says yes your your priority is top of and we’re gonna take it on and we’re gonna do all this stuff for you there’s an impact that decision that’s fine it’s just a matter of like pluses and minuses right if I can’t if I have to do this and that’s not part of the plan then you’re not going to get this down the road I I want to I want to jump on that

45:40 I I want to I want to jump on that comment there Seth I agree with this one and I’ve also seen this one occurring many times the bringing your fire to my doorstep your your lack of data your lack of planning your lack of whatever that thing is right the number of times I’ve been walked into or or conversation has walked into me and said hey we need this by the by this weekend okay you we have three other objectives that we’re trying to build for you which one do you want me to pick and and which one needs to drop off and also the idea

46:09 one needs to drop off and also the idea here is it’s important to document how your team works with a process so you can say well thank you for your request we haven’t had time to estimate it we don’t know how to get it done did you bring it to my standing meeting last week right this is the importance of like all of these back-end processes totally mean and everything else because it’s like you create areas for the business to understand that if they want to have conversations and bring their needs forward there’s absolutely forums to do that all the

46:39 absolutely forums to do that all the time and we’re bringing our levels of expertise to like plan your things you didn’t show up you don’t get to come into my world and blow it up without the appropriate levels of of authority authority no and that’s exactly why that executive leadership stands that’s the executive sponsor is the one who’s is going to help defend and like the another exam sample some sales guy commits to a report that needs to be delivered to a customer yeah and then they’re like kills me how long have you known about this one well yeah we’ve

47:11 you known about this one well yeah we’ve known about it for like three months okay this is the first time I’m hearing it and now you’re telling me it’s due in two weeks one we don’t have the data the right way two you never communicated the need earlier up in the process here so these are to your point Seth this is why the process exists this is how we work together as a data culture driven company and if you’re not communicating across teams when you need things I’m not will I don’t want to help and then that is and to me really the the value here is that once that executive sponsor shows up that’s where we can say look this is this is the process we

47:42 look this is this is the process we documented we agree upon how we do work this is the way we’re going to do it you have not asked us for these requests they’re going to go back into the backlog and it’ll be prioritized based on these things there may be other initiatives that are more important than your your item the the thing that strikes me and this is is exactly where the more front out in the sales you get like conflicts in a business come and why a glibly make fun of that group a lot but I I still love them we all love them we’re a team man everybody’s a team in your organization the customer is

48:12 in your organization the customer is obviously the most important part how about them you don’t have a job however with these processes like and that’s why you need process because it’s like hey we understand you’re making those customer calls we understand that you’re interacting with them and that’s not the most the easiest place to be it’s just not not but there’s two sides to every organization right like if a customer is coming in they’re demanding something it’s very easy for the the the sales person to be like I I completely understand blah blah blah like

48:42 understand blah blah blah like your thing our process to handle this request looks like this right because you’re empowering them with that process then they can say then they can buy themselves some time and say let me work with the groups this is how it works I have another meeting scheduled here let me see if I can escalate this right your needs are obviously very important but a challenge that that happens which is a customer’s upset or a customer wants

49:12 customer’s upset or a customer wants something and is demanding it and they don’t know any better they’re like okay I’ll get that done for you right because that’s the easy thing to say I’m your buddy and not have any idea always playing good that you’re creating on the back end and then you have these challenges with delivery because it’s not there ultimately the responsibility of that relationship is on the salesperson yes yeah if you follow a process but if you don’t then you have this like really hard conflict that’s happening all the time between those groups because there’s no concept of

49:42 groups because there’s no concept of like how long how much effort it takes to deliver something and then there’s just this constant expectation that oh they did it right and and it’s also I’m rambling I feel like it also goes on the other side so this goes on the other side like ybi teams and Reporting people who just like man oh we have this high high priority thing I’m gonna work 18 hours in the next three days I’m gonna get out the door and they do and then the expectation now has been set correct there’s no understanding that it took

50:13 there’s no understanding that it took like an insane amount of time yep for that person to do it and then they do it again and again and again and again and and that’s taking all the time away from those other bigger initiatives that’s a loser that’s where you find yourself burning out and that’s where the business starts to not understand like how long things should take etc etc because you have Heroes coming through and just continually delivering and that’s why I say hey sometimes things have to break some sometimes I have to break if if you’re in

50:43 have to break if if you’re in that situation the song comes to mind and the hero comes along so we’re getting close on time here this has been a really good conversation I think this article is very well written even though it’s a little bit older I’d be curious to see that survey rerun again and see what happens again as they if it has this has the numbers deteriorated even further you numbers deteriorated even further have it coming out of the pandemic know have it coming out of the pandemic has has this increased challenges within organizations Are We Now

51:14 within organizations Are We Now addressing more short-term needs and not really strategically planning for long-term pieces but I do think at the very end of the article article it was a very good wrap up here talking about analytical decisions this is a quote from from the article again analytical decisions and actions continued to generally be superior to those on intuition and experience based decisions 100 and I will I will end with this though like there are some more recent articles I was reading to bring context to this yes and

51:45 context to this yes and like the definition of data Savvy right yes up in the air but it was a CIO magazine and it essentially said companies that are data Savvy showed a 3X increase in Revenue yeah so these strategies and the the need to bring and utilize and understand data which is a big challenge because of the volumes I think are definitely worth the efforts even though we talked a lot about the challenges and like how to like it’s very real like yes not an easy thing to just slap them something on the benefits

52:17 just slap them something on the benefits are absolutely there in in terms of Revenue Revenue and I I like this the very final statement here is firms must be more serious and creative about addressing The Human Side of data if they truly expect to derive meaningful business value and it’s right it’s again there’s a lot of people involved here there’s a lot of emotions there’s a lot of things working around around this space and I think if anything the technology has accelerated this conversation just because it’s now so easy for people to get their hands on so

52:48 easy for people to get their hands on so much more data and it’s becoming way more clicky clicky draggy droppy to steal the words from Christian Wade and and it potentially creates a false sense of delivery on we could skip a lot of these other processes and again when I was when I was focusing so much on the business I was like oh who needs Dev test product I’ll just build everything prod like that’s it this works let’s go fast let’s make value like that was my initial thinking but now as I get more into the I. T side of the space in the world there’s definitely a lot of value

53:19 world there’s definitely a lot of value from what comes from that process that rigor that comes along with those things awesome awesome any other final words or thoughts Tommy any wrap-ups on your on your side for final thoughts the article anything you you picked out that was important or think you’d want to campus with no I I the the ending part of this to me is just the Crux of it when it comes to you’re seeing more and more leadership more investment with cdos but then also understanding we have to work together with the the from Tech having these SWAT teams

53:50 these SWAT teams if you see more of that that’s only going to be a good thing I agree any any final thoughts Seth you said all the thoughts all the thoughts are said all right excellent we appreciate your time I hope this was helpful go read the article the article is really good we’re very well written I’d highly recommend you go read the article it was awesome so definitely recommend you do that also I will say if we if you like this conversation if you like us arguing on a podcast and disagreeing about things and you have disagreements as well we really appreciate those

54:20 as well we really appreciate those opinions it helps refine more of our thinking around what data and analytics NBI looks like and it’s honestly this conversation will probably be different in every single organization because everyone’s at different stages in their data culture some are thinking about it some are getting there some don’t have a clue what it is like it could very much widely in your organization so hopefully this gives you some thought maybe the article will give you some practical examples on how to get moving through your data culture please share this with somebody else if

54:50 please share this with somebody else if you like this article and then tell me where else can you hear about the podcast you can find me so anywhere it’s available on Apple Spotify make sure to subscribe reliever rating helps us out a ton if you want us to talk about something that you’ve been thinking about you have a question an idea or just a just a good topic go to powerbi. tips slash podcast and submit your question there and finally join us live every Tuesday and Thursday 7 30 a. m Central Central thank you all very much and we’ll catch

55:21 thank you all very much and we’ll catch you next time

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