Podcast 005 Building a Data Culture
Episode 005 Building a Data Culture Transcript
Heather McKee: Welcome to the Modern Polymath. Where we discuss topics in technology, economics, marketing, organizational behavior, market research, human resources, psychology, algorithm, higher education, cyber…
Heather McKee: Hey, podcast, the universe. Thanks for tuning in. On today’s episode of the Modern Polymath, we’re going to discuss the upsides and risks of using data in your organization and how to build a successful data culture from the start. We obviously now live in a data driven culture. I mean, data’s becoming pretty much the currency of the future. It’s been compared to oil like other currencies of the past. It’s not an option for businesses anymore to not use data or to not be involved with data, and thankfully that’s starting to become more understood.
Heather McKee: Throughout the business world though, while people do understand it’s important, there is a lack of understanding of how an organization effectively uses data. We want to talk about how to set yourself up for success from the start. It doesn’t matter if you’re a startup or multinational organization. There’s still pieces that have to be put in place to be successful and that’s what we’ll talk about today.
Heather McKee: Discussing these topics. As always, we have Dr. John Christiansen, John-David McKee, Will Callaway, and I am Heather McKee. Let’s get this podcast started.
John-David M.: It has been a lot easier in the past, because companies didn’t have to have a quote unquote data literate organization, because there wasn’t that much data to look at, or it wasn’t measurable. Now it is very measurable. People know they’re supposed to be doing it, but why?
John-David M.: When used properly, the businesses can add tremendous value to the strategy of the company and their overall bottom line. It makes sense why then that data’s jumped at the top of many leaders priority list. Using data, you can create competitive advantages. You can enhance your customer service, you can improve your marketing efforts, you can develop internal efficiencies within the company. You can identify new markets, grow under served niche verticals that can help you dominate your industry. There’s a lot of great things that can be done with it and we could go on and on about that.
John-David M.: But there’s also the flip side of that, like data can be misused or mishandled. And it can cause expensive and sometimes irrevocable mistakes if it’s not used properly.
Heather McKee: Yeah, data loss can be a major inconvenience that can disrupt the day to day functions of any information based business. Data loss also sets back productivity timelines and can cause you to lose customers if it’s associated with a security breach. In our cybersecurity episode that we’ll have coming up soon, we talk more about really the pitfalls of security breaches, security leaks and what companies have to do to really come back from that by rebuilding trust and respect from their clients and customers.
John-David M.: So as with anything powerful, if it has power, it can also be dangerous if not understood and not integrated effectively. And that’s why really it has to start at the top within the organization because it has to be embraced by leadership and then translated down and how it’s going to work within each element of the business.
John-David M.: If you let your departments report where they want to report to you, you’re not going to get the true story. Because they’re all worried about their job. That’s why the at the top you have to be saying, “These are the things that we’re concerned about here. And these are the performance metrics that are going to add up to a successful well run business.”
John-David M.: And I mean really it’s only a few companies that are actually thinking and formulating processes around data that they’re storing and analyzing. I mean an interesting data point around the, the issues though from a recent study, 77% of fortune 1000 executives were surveyed and they reported challenges with their businesses adopting big data and AI initiatives. That number is up, and that was in 2019 the number is up from 65% so that’s a 12 point Delta right there from 2018. Showing that the importance is there and there’s a recognition that they need to do that. But there are people who are realizing how hard it is to implement this and that’s why it has to be given so much attention.
John-David M.: Well, the reason we keep talking about it, is largely because it’s a very misunderstood concept. The way that most organizations build out their current data culture is in fact not a data culture at all, because when we say that we mean a data literate organization. A company that can use data throughout the organization that uses it to manage and improve, optimize the way that they run their business. And that is done across the organizations, different departments.
John-David M.: A data literate company looks at all the other elements of the business, the different components to make it up and has similar insights into each one of those as they doing their finance. It’s also equally important to look at leads generated and evaluating the success of your recent hires and your team structure and how departments can talk from one to another. Things like that. All those things have to be considered.
John-David M.: It’s a lot of things that we continue to see companies do such as not hiring the right people. That if the strategy is in place up front and they are data literate organization, they’re not going to make those same mistakes. Very often people think, “Oh, we need more data. We need to go get more data. We need to by the state over here. We’ve got to collect this data.” And yet they’re not even using the data they have now. You first need to start with, “What data do we have? What internal resources do we have? Are we collecting all the data that we need? Do we need to go add more based on whatever question we’re trying to answer or whatever goal we’re trying to achieve?”
John-David M.: In this same study, we saw that 55% of these companies, and these are larger companies, mind you, but if they can’t do it, it shows how with all the money they’re spending, it shows you how hard it is for smaller businesses do it if it’s not intentional. 55% of the businesses that were surveyed were spending over 50 mil a year on this and 21% we’re spending over 500 mil a year on these initiatives and are still having those kinds of issues. And whenever they’re asked, “What’s causing the issues?”
John-David M.: Everybody’s thinking, “Oh, it’s bad data. This data’s not working.” Or whatever. Only 5% of the issues were constituted a technology problem. The other 95% we’re first people issues and second processes issues. So it really it’s about getting your people working on the same understanding of what we’re trying to accomplish, giving them what they need. And putting the processes in place to ensure that they do with their job.
Dr. Jon C.: Like there’s data and then there’s data points. They are two different things.
John-David M.: Yeah. So I mean before we can define a data culture, we’ve got to define actually what data is, because that’s something we do see a lot. There’s a large misconception about what data is and people think of a way to narrowly.
Dr. Jon C.: Well largely because I think when we read articles, we hear data points. In a way, but they’re really a summary of data collected. 63% of people do X. Okay, well that was a data set they pulled that from.
Heather McKee: Data points that I’ve been familiar with. So, because if we’re saying that, data is becoming more important and companies need to move toward this data culture, but need to understand what the data points are that they’re collecting. And basically how to make sure that they can make sense of all of them and share them across the entire organization. Some of those data points, if we think of it by department that people are collecting. So if you’re in the finance department, you have invoices paid, not paid, recent purchases.
John-David M.: In marketing it’s, “How many clicks did we generate on the specific campaign?”
Heather McKee: Right.
John-David M.: “And lead generation that leads into sales. And have you followed up?” And there’s all these different tools you could use. And that’s one of the things you need to get to.
Dr. Jon C.: And I love the example of, “How do we know if we put on a really good show at the Peace Center?”
Dr. Jon C.: And it’s like, “Well, we haven’t done surveys.”
Dr. Jon C.: “All right, but do you have film of it?”
Dr. Jon C.: “Yeah.”
Dr. Jon C.: “Okay. Count the number of standing ovations that they got.” Something that simple.
John-David M.: Yeah. Good point. Yeah. There’s a lot of ways to look at it. It’s really that lens, that strategic lens you’re viewing it through. The financial example is important, because people understand that. People do understand that part of it. They understand that you have a finance department and the accountants do what accountants do. They’re looking at your balance sheet and your P and L and they’re working with the executive team to make decisions based on this and understand the financial health of the organization. Some do it better than others obviously. But pretty much everyone knows that they need to do that, because you got to file your taxes right. And you know why you have a CPA in there, because you know what they’re there to do. That’s effective data culture within the silo of finance. But people understand that, because they’ve been doing that for a long time.
John-David M.: What’s new is that that’s just one, one subset of all the data that a company should be looking at. So we talk about starting at the top and how important that is. It does mean that the CEO and the executive team has to be involved in this. It doesn’t mean the CEO is expected to know the technical elements of data analysis. Right. But I mean, far too often you see the leaders hire analysts or a data team without strategic integration or the process development internally. And they think they can just sit back and wait on results. Like they’ve checked that data box and okay, good. Now on to the next thing. And that couldn’t be further from the truth. But that’s what happens so often is the data box is checked and they think, “Okay, we’re good.”
John-David M.: But if it starts at the top, which a data culture should then it starts with asking the right questions and understanding what you’re trying to accomplish. And then from there, figuring out what data you have and what resources you have internally. And then going and finding the other parts that you need in order to answer whatever question you’re trying to get to.
John-David M.: But most people think that the approach it from the other way around, it’s a bottom up.
Will Callaway: Right. And when you’re talking about starting at the top, you’re specifically saying your top executive’s saying, “This is our value prop, this is our business model. How can we use data to enhance our offerings, product, sales, marketing and what not?”
John-David M.: Yeah. Maybe make your operations more efficient, maybe increase your bottom line. Maybe it’s expand your services or your products into a different market or deepen it into another market. You have to know what you’re trying to do.
Will Callaway: Right. And then you would define that as your business domain knowledge of that company. Where the analyst wouldn’t necessarily know every product or every move the business is trying to make, or where they’re trying to be in the future. They’re just analyzing data and if they’re just doing that, they’re kind of just shooting blind.
John-David M.: Yeah, definitely.
Will Callaway: You can find patterns, but those patterns might not mean anything to a specific product, sales, marketing, whatever it may be.
John-David M.: Exactly. And even further, if the executives aren’t involved in that process, if they’re not able to take the data that’s found by the analyst and work it into the strategy, into the decision making, they’re not going to get very far with it. So there has to be literacy at the top and understanding of what the data means. That doesn’t mean that everybody has to be trained in interpretation of data. But there has to be an understanding of how to use it and someone at the top, whether it’s the CEO or someone the CEO appoints needs to be the translator that goes up and down the organization. That’s able to take the executive decisions made by the board and made by the executive team and translated down to the different departments and to the analyst and to the people that are crunching the numbers. And be able to talk at a very tactical level. And then take the results from the analysis and be able to translate that to the executive level, that can then inform the decisions that are being made off of the data.
John-David M.: Somebody has to play that role, really is a champion within the organization. And ideally you have multiple people that can do that because it’s a data literate organization. To where different people within different departments are very fluent in the data that they have and can all relay that back to the top, so that these decisions can be made very fluently and with a lot less effort.
Dr. Jon C.: But the point of this is more to to stress the difference between, what is data? What is the specific data point? And then the difference between structured and unstructured data. And then ultimately what you can do with it.
Dr. Jon C.: So let’s say you were in the manufacturing of easels, that maybe you were hanging artwork.
John-David M.: Or whiteboards.
Dr. Jon C.: A whiteboard. Yeah.
John-David M.: Maybe that.
Dr. Jon C.: A photo of your-
Will Callaway: Dog.
Dr. Jon C.: Yes, your dog or maybe a wedding portrait of sorts.
John-David M.: More likely a dog.
Dr. Jon C.: More likely a dog. What you could do then if you wanted to evaluate the performance of your product is we could go on Amazon and look at, we look and we say, “Okay, How many ratings do we have?” Let’s say we have over 600, that’s a lot. That’s what we call robust, because it’s large enough for us to determine whether or not this is consistent among our customer base. The people that buy our product. We know after a certain number of ratings that this is… in other words, we are who we thought we were. Right. In baseball, you have to have a certain number of at bats before we know this is what kind of player you are.
John-David M.: If somebody has five stars, but there’s only three ratings, that’s not something you rely on.
Dr. Jon C.: Right, but if they’re 16 ratings and they’re all five stars.
Will Callaway: It’s probably a pretty good podcast.
Dr. Jon C.: It’s probably a really, really, really good podcast. And that’s, we’re speaking of us.
Will Callaway: [inaudible 00:13:40].
John-David M.: I hope there’s more than 16 by the time this launches.
Dr. Jon C.: Yeah, but so because then I can take portability, lightweight, easy to assemble. And test that against the overall star rating and say, “All right, what is the actual magnitude of these things? So portability is my highest, but what has the greatest influence over my star rating?”
John-David M.: Because you’re trying to take unstructured data, which almost all data that’s out there is unstructured. But when you’re trying to do is give it structure. So you’re coding it to say… the language somebody uses a one review is going to differ obviously from the next review. But if I say in my review, “The product did exactly what I needed and it it was well-packaged.”
John-David M.: And Jon says, “It was shipped in an effective way.” Or something like that. That’s saying the same thing. So we could code that as… and move it to structured by moving those and say those are two of the same comments saying the same thing, that effective shipping or whatever.
Dr. Jon C.: And that tells me what-
John-David M.: Right, what’s the main-
Dr. Jon C.: If I’m going to reconfigure my product, that’s what I need to focus on.
Heather McKee: So depending on your focus, how do you make sure that each department is aligned?
John-David M.: Every department is important to the way the business, but not everyone is going to be weighted the same if you were to put their data in a weighting scale, they’re going to be weighted differently. And what you’re trying to get out of each department’s going to be different.
John-David M.: Marketing’s going to look at a lot of metrics that are more in depth that will add up to the main metrics that then will lead into the executive decisions. But you have to start at the top to set the priorities correctly for each department across the organization, so they all work together to get to that end goal you’re trying to accomplish. That’s why you have to start with, “What are we trying to do? Are we trying to grow sales?” Well then we probably need to invest in more sales resources and track the leads more heavily than paying attention necessarily maybe customer service. If we’re trying to improve customer service, that’s going to have a higher emphasis.
John-David M.: And the emphasis in the strategic goals really help to set the weights of how you evaluate each of these different departments and start to blend them together into one narrative. Where right now companies understand very well that they can look at the financial data and they can make decisions based on that about the financial health of the organization. But every department now data is created everywhere. Data is everywhere. So, just like you do with financial, you can do that with your employee performance, you can set metrics that are relevant to your business. I mean if you’re looking at a specific data point and making decisions around it. Like if you’re looking at how many people went to your website and that’s how the marketing department’s being evaluated. But that’s not actually relevant to the strategic goal that you’re trying to accomplish. Why are you looking at that? That’s not relevant within the grand scheme of what the business is trying to accomplish.
Heather McKee: It sounds like really been part of this all drives back to, how can you measure that goal? And what data points are important to measure that and which ones aren’t? And don’t worry about the ones that are unnecessary. Just worry about the ones that are to be able to get to your desired goal or see that outcome.
John-David M.: Yeah, exactly. They leverage that kind of approach across the board so that marketing has its data. Sales has this data manufacturing or whatever the product.
Heather McKee: Customer service.
John-David M.: Customer service. HR has got their metrics, everybody has their metrics and the things they’re judged on just like you’re looking at your bottom line and your top line, the revenue generated. Then evaluate your team. And you may have all the right people, but maybe the right people aren’t in the right seats. Or maybe you need to go higher or maybe you need to outsource a certain element of this. Right.
Heather McKee: Or train and develop someone who’s already there.
John-David M.: Yeah, exactly. Exactly. But you can’t know that until you’ve decided what you’re trying to accomplish. There’s so much data out there now. If you don’t have that logical approach.
Will Callaway: Have fun with AWS fees, you’re going to be storing so much data that you don’t even need. Well, I mean, why do you have it? Get rid of it.
John-David M.: Well, yeah, you’re not going to know where to start. It’s like trying to find a needle in a haystack, but at least in that case, you know what you’re looking for. You’re looking for a needle. If you don’t make sense of all this data, it’s going to overwhelm you and it’s not going to do anything for you. And you’re right, the storage fees are be huge.
Dr. Jon C.: KPMG found that 84% of CEOs are concerned about the quality of the data, they’re basing their decisions on. So in other words, they know they need to be data driven and informed in business decisions, but they don’t trust what they’re getting is what they need to make an informed decision.
Heather McKee: Jon, why don’t you use that picture that you took from the 2016 election as an example. That was a good one.
Dr. Jon C.: On the night of the election, there were people posting this image-
Heather McKee: It’s on the website.
Dr. Jon C.: … over and over again. It was one of the last states that they were going against. All right, 60.2 to 36.4, Clinton’s got that lead.
John-David M.: In Pennsylvania.
Dr. Jon C.: It looks like a landslide. But then you look at that bottom number, the percentages that are in. Well what’s interesting is it’s 12%. on top of that the ones that tend to get in the fastest tends to be the ones that are the most technologically advanced. Whether or not that was heavy towards Clinton or Trump or otherwise is to be decided. I don’t know the answer to that.
John-David M.: It varies probably to different places.
Dr. Jon C.: Right, right, right. So, that said, those tend to be biased in one direction or the other. I don’t want to get into politics here, because I really don’t think our politics really matter. But you take that substantial proportion and then you bang that against an actual test to see whether or not you can call that.
John-David M.: Yeah.
Dr. Jon C.: I counted 38 people posting this exact image, pretty much calling it a loss. And guess what happened? Trump won 48.18 to 47.46.
John-David M.: Yep.
Dr. Jon C.: And got the 20 electoral votes. So everybody’s going to bed calling it a loss.
John-David M.: Yeah. Even though there’s 88% still waiting to be reported. I mean, that doesn’t tell you anything. I mean it tells you that what’s been reported is a lead.
Dr. Jon C.: Yeah. It’s a lead for those districts that have submitted.
John-David M.: Yeah, but it’s almost like saying that the person who has a lead with the first mile of the marathon is going to win the marathon. Right? I mean it’s important, right. If you’re in dead last place, it’s going to be hard to make up the top, but you could do it. But it doesn’t mean that person didn’t sprint their way to that first mile and are going to be dead by the fifth.
Dr. Jon C.: I’ve seen in horse races where it’s the guy in the back. Is like, yeah, “You all go ahead and like knock each other into the boards. I’m going to sit back here and when it’s time to charge I’m going to find my lane and you don’t even see me coming.”
John-David M.: Yeah, exactly. I mean if you watch a golf tournament, I go back to this, but I think it’s funny because somebody might shoot a great round on the first day and it’s like they’re going to win. Well that’s why it’s four rounds in a tournament. And you’ll see a Tiger, will talk about it. It’s like, “I put myself in position I needed to to be there on Sunday.”
Heather McKee: Yeah.
John-David M.: And it’s the same thing. Like you’re only reporting 12% of the data. You’re only getting a small piece of the picture here and like that really translates to what a lot of organizations are doing. They’re only getting 12% of the data that they could be getting and they’re making decisions off that 12% and leaving them obviously making bad decisions. If they put that together and get this strategy in line, they can make infinitely better decisions that have a true data-driven decision making process. Therefore they have a data literate organization and that data literate organization is going to far exceed what this 12% anecdotal best guess scenario is. And honestly in this case, this is where data can be dangerous. And that’s why if you’re going to do this, if you’re going to use data to make sure you’re using it correctly, know what you’re trying to get out of it.
Will Callaway: So going back to what Heather was saying when she was talking about the strategy and such. It starts at the top and then each person is delegated, “This is your responsibility, this is your responsibility, this is your responsibility.” Within a data literate company, each of those subsections is going to derive their own insights, which would maybe go to the next hierarchy of manager or translator. They’re going to have a strategy session based on the insights that were found across the different departments, to maybe try and make sense or ask further questions. And then they’re going to formulate a strategy based on that. And then they will push that up the ladder and see if it gets approved. And that’s where you get up to the C-suite.
Will Callaway: And then it’s kind of an iterative process based on how dynamic your data is, how quick your business model can turn over, how your customers can change, whatever it may be. Each company is going to be unique, but that’s kind of the process. When we say blended, we’re not saying everyone’s hanging out with the same data set and everyone’s just going ham on it. It’s still going to be siloed to an extent. It’s just these silos are now working to gather based on the insights that they find to help each other.
Heather McKee: Tell the bigger picture.
Will Callaway: Yeah, yeah, yeah. To, yeah. To paint the full picture.
John-David M.: So one thing that we’ve seen a lot is data being collected in different departments that the system didn’t even talk to each other. So they couldn’t compile it together to make sense out of what was really going on. So you’ll see the marketing teams using this tool over here that allows them to collect data on the leads they generate and has a CRM component to manage the customer relationship management component. Within the sales team has their own CRM tool. Or we’ve even seen where there’s multiple CRM is being used within the same sales department and you’re not able to unify all of that data together to make sense. But most businesses have Google analytics on their website. Most businesses have some kind of CRM tool to manage their customer base. If you have those things, you can start to piece together a larger narrative about what’s happening with your business.
Will Callaway: Yeah, yeah. And those are kind of, let’s say those aren’t simple examples, but people can identify that. The larger companies are going to have database engineers specifically creating pipelines of whatever data they’re trying to collect for the marketing team for the sales team for whoever, right?
John-David M.: But, a lot of smaller companies think that a data culture is only for big companies. And everybody has, it’s easier for a smaller company to build one because you don’t have as many layers of bureaucracy, you don’t have as many legacy systems that are having trouble talking to each other. You don’t have the complexity of the organization you have to manage. I mean we’ve seen a lot of, if you look at the number of fortune 500 companies that have gone away and been replaced by startups in the past 15 years or so. What’s you’re seeing is really the little guys who are more nimble and are more strategic about the way they’re starting it up, overtaking these behemoths who are bogged down in legacy systems and can’t get out of their own way to make decisions. And that’s what data lets you do.
John-David M.: I mean, everybody now knows the data’s important, right? There’s not a leader who should be a leader who doesn’t know the importance of data. People don’t understand what it really means, what it can do.
Heather McKee: Is it fair to say then that a data literate company or the definition of one is that it’s not a one size fits all? It’s different for every company?
John-David M.: Oh yeah. It has to be, it has to be. A large organization is going to have different needs than a startup. And one industry’s… A manufacturing industry has different metrics far different than a software company.
Dr. Jon C.: It’s all about your target.
John-David M.: It is, yeah. I mean there’s a finite number of variables that are important. Number of employees, target revenue, technology, your products and services, whatever.
John-David M.: We, we have a visual that we’ll put on our site. It talks about the different elements of a data literate organization. But the components to make it up and it’s in an atom, we call it the anatomy of a data literate organization. And at the nucleus, at the core of this is data literate. But the different components are strategy, marketing, finance and accounting, technology, human resources and operations.
John-David M.: Now, it’s going to vary somewhat in different organizations, but these are kind of the big buckets. Where if each of these are talking to each other and working around the same understanding of what we’re trying to accomplish and each of them using data effectively, you’re going to have an effective organization. There’s not a one size fits all. Every organization is going to have their own needs and it’s dangerous to go and try to copy Google’s model if you’re a manufacturing plant, right? Your needs are different, your circumstances are different, your shareholders, your stakeholders all look different. But if you narrow it down to these key six areas and make sure that each of these are running from a data-driven perspective. You’re going to be in a good spot as an executive to then be able to make decisions across the organization that’s going to put you in a good position.
Heather McKee: So what are the five key points that people should take away as they’re getting a start on this?
John-David M.: The five keys to getting off to a good start. This is if you want to kick off, and I recently published this in an article. But the the five must-do tasks necessary to get your project off on the right foot. Again, it starts with number one, the most important, defining the key business question or questions you’re trying to answer and the resulting goal of the project, that’s number one. That has to start at the top leadership, embracing that.
John-David M.: From there you appoint a project lead and this is that person, we call them the data translator. But it needs to be someone who’s involved in strategic decision making so they can convey the goals throughout the organization. And then they need to be able to translate the significance of this initiative to each department and determine their needs and what they have. Plus being able to then translate the technical elements of the analysis into actionable insights to the top for strategic decision making. So that’s going to be your second step.
John-David M.: Once you’ve got that project lead in place, then you’d want to get into auditing your current resources and your current capabilities. So your team, your data, your technology platforms, are they talking to each other, et cetera. To define what’s needed and where the potential hurdles or barriers are that could prevent the initiative’s success. You don’t need to introduce additional data until the existing data is understood, because then you’re just adding in additional and unnecessary complexity or confusion. And probably incurring additional costs on top of it, when it may not be needed.
John-David M.: Once you know what you’re working with and you have the person in place and you know a question you’re trying to answer, then he moved to the fourth step. Which is then determining the additional resources needed to accomplish the goal. So what data are we missing? Are we missing internal employees? Do we need to train somebody up? Like you said. Or do we need to outsource this to an expert? Do we have technological gaps or inefficiencies with our systems? Are we using a legacy system that doesn’t talk to others and doesn’t allow us to pull this data together in time to put it in maybe a dashboard where we can make decisions. Do we have infrastructural needs that aren’t being met? Do we have budgetary constraints that we have to work around? These kinds of considerations need to be made.
John-David M.: At this point. It’s basically what are the missing pieces of the puzzle to be able to put the puzzle together. And if you hire a consultants we’ll often come in and do this and help put these pieces together for the organization. Because we can help define all the components that need to be considered and aligned them together to make sure their success.
Will Callaway: Right, JD. So with the labor market in the analytics field being so highly competitive right now. Where the bigger companies are scooping up all the talented individuals and medium size or trying to figure out if it’s justifiable to spend this amount of money on an employee. Sometimes the consultant route is the better way to go, because your consultant can be on engagement for let’s say three, five years, something like that. And they’re under contract. You know what you’re going to get, you know what they signed up for. As were the employee in the hyper competitive nature that it is right now, I mean they might be there for a year, six months, like until they get a better offer.
John-David M.: Very true. Great point.
Heather McKee: Yeah. And even more than that, just with your data culture strategy as a whole to have to rehire, retrain people who turn over quickly after two years or so. That’s a lot of work, a lot of knowledge loss. And they could even potentially take your strategy to another company if they really wanted to. So you got to be careful about that and make sure that you have the people there who are invested in this and are all aligned.
John-David M.: Yeah, that’s a great point Heather. And that’s actually a perfect transition into our fifth point. To build the team to accomplish the goals. And this doesn’t mean starting from scratch. This means taking all your internal and external resources, pulling them together to ensure that all the capabilities that are needed are represented and all the right butts are in the right seats, and looking at the right thing. To make sure that you’re going to get what you need out of the project to ultimately get back to that goal you’re trying to accomplish.
Heather McKee: There you have it, the five keys. That’s all the time we have for today, but you can always read more on how to build a data culture on our blog, at insandouts.org. Don’t forget to subscribe to the podcast so you can stay up to date on our recent episodes. You can also stay in touch with us on Instagram, Facebook, LinkedIn, or Twitter. We love hearing from you and your thoughts on our discussion topics. Until next time, catch you later.