The Influence of Data in Automation Failure and Success

Dayle Hall:  

Hi, and welcome to our podcast, Automating the Enterprise. I’m your host, Dayle Hall. This podcast is designed to give organizations the insights and best practices on how to integrate, automate and transform the enterprise. 

Our guest today is a practitioner and scholar in big data analytics, data culture and data strategy, oh, the three big Ds of data. His expertise falls on the influence of analytics personality types to how data is analyzed in organizations. He has created a framework, that we will get into for sure, that gives companies a roadmap to harness data for business success. Please welcome to our podcast the founder and CEO of Logicle Analytics, Dr. Jonathan Fowler. Jonathan, welcome.

Dr. Jonathan Fowler:

Thank you, Dayle.

Dayle Hall:

Well, it’s really exciting to have you on these podcasts. We’ve done various different types. We’ve done some nonprofits, we’ve done some organizations, large enterprises that are doing different things around integration and automation. And we’ve done some people who definitely made me feel inadequate in my knowledge of all things scientific and around data. You will be one of those people, I’m sure.

But what I’d like to kick off with is just let’s get some background on you, like how did you get into this. And as we start to go through the podcast and we talk about this framework that you’ve created, I think your background and how you led up to this will help to ease into that. So who is Dr. Jonathan Fowler?

Dr. Jonathan Fowler: 

I think you’re right about explaining that because for the longest time, I felt like my background prior to getting into analytics was more of a burden, not useful at the least, maybe even detrimental at worst. So I started at Clemson as an undergraduate, and I was in computer science, and I wound up failing calculus three times. And so you can’t get through computer science if you can’t do calculus. So English literature was my second choice. And I got into that. I finished that degree, went into a master’s in education. And that’s where I did a lot of quantitative work in educational research. And you put the quant research with technology together, and you get sort of steered into data science and analytics.

Now, I didn’t land there at first, though. I was actually- I worked in public school for a few years. And then I started realizing that I was actually better at- I didn’t get so burned out doing the analytics work and really exploring the space that was, at that point, evolving quite a bit. And it’s still very new. But the hard part for me was, well, how does somebody who has no formal education in this but a lot of experience, how am I able to actually get a foothold somewhere.

So I did a lot of project work and finally built enough of a portfolio that I was hired on as a lead for analytics projects since I had to manage an IT company. And then I did some project work for Bank of America, worked for a few other companies in Charlotte, and then moved back home to Greenville. And around the same time I finished, I was part way through my doctorate in big data, I started my company. And that was where I really understood that they always tell you, turn your strengths into weaknesses- that’s not good, turn your weaknesses into strengths.

Dayle Hall:

That always sounds a bit better.

Dr. Jonathan Fowler: 

Other direction. And I thought, okay, well, I don’t know many people in big data analytics who have a background in Liberal Arts and Humanities. So let’s see if we can’t make that a good niche. And I think so far, I’ve done a pretty good job of exploring that.

Dayle Hall:  

It’s great. Well, I know we’re going to dig into that. I love that kind of background. When I talk to people, and you bring people into the team, you want people with diverse backgrounds, diverse opinions. Yes, of course, you need some kind of capabilities in a certain area. You said yourself you can do computer science if you can do calculus. But then different thinking, different backgrounds usually brings better results or more innovative results. And we see that a lot obviously within our customers and prospects that I talk to on a regular basis. And then what a time to be looking at data and analytics because this market over the last 10 years is exploding.

Dr. Jonathan Fowler:

It is. It absolutely is. My business partner, John Osland, he’s involved in a tech accelerator in the southeast as well. And we’re both with Logicle and then with accelerator. We’re finding that we’re hitting a good sweet spot with businesses in the accelerator that are keen to capitalize on big data and not just monetizing it to grow, but also really understanding how does it help them, how does it help potential partners, how does it help their customers. It really is the new gold, the new oil. And any company right now, if they understand that data is the lifeblood of their endeavor, then they’re in a really good position to grow.

Dayle Hall: 

Yeah. The couple of companies I’ve been at before, one was Lithium Technologies. Obviously, very data focused around customer journey, people AI that use data specifically to understand how sales and marketing can get more value. But I get these questions from my team, for example, they’re looking at new tools or whatever. They always say to me, give us this data.

But one of the things that I think is much more important is, okay, having the data somewhere is important, but is that something, a, you’re going to use, how do we use it? There’s no point having it, but we don’t know what to do with it. And there’s all this- and we’re going to have this tool, but unless we learn how to use the tool, we’re not going to need to leverage it. There’s so many opportunities and there’s so much technology out there right now, which is they’re all touting big data.

I just wonder from your perspective, as you talk to customers or clients or partners or whatever, are people still lost a little bit with how to use data? Do they know- we all know we need it, it’s a new oil. But analysis paralysis, are we paralyzed whilst there’s so much opportunity, and sometimes we don’t know what to do.

Dr. Jonathan Fowler:  

I think there’s an overload of two things. One is the amount of data that businesses collect. And it’s not saying we need to collect less, it’s just we need to understand how to use it. But the other is just the bombardment that we have right now in the analytics tools, analytics consulting, that whole space is very crowded, there’s so many vendors that make good products. And then also, I think there’s a lot of confusion about even something as simple as job titles, data engineer, data analyst, data scientist. If you take 10 companies and look at their job descriptions for a data scientist, you will probably get 10 different understandings of that role. And again, it’s a new area, relatively. So that’s understood.

But the thing with the first two, those are intertwined. So yes, data is there. But access and usability are two different things. So you can give me access to an operating room, but if I’m not a surgeon, then I have no business being in there.

Dayle Hall:

Yep, agree.

Dr. Jonathan Fowler:

I can’t operate on that patient. So if we have access to this data, people often might say, well, let me just see what I can- let me just play around with it, see what I can come up with. And we say that as developers and data scientists, but I don’t mean it like that. I’m just going in and digging around and saying, well, we have these data points, we should do this. Sometimes that’s okay, but that’s almost like building a solution in search of a problem, which a lot of times, I think- if you take the other overload, which is solutions and tools and things. Let’s say your company just got pitched of $750,000 a year for a new analytics tool, and they see all the cool stuff that they can do with it and they implement it. And next thing you know, nobody uses it, or they use it poorly. And the management is like, well, we bought this for you. Why are you not using it? The data is all there. Well, you really didn’t take into account how the people need to use it, what was the problem you needed to solve, and are you actually meeting people where they are instead of just giving them an edict to do this thing.

Dayle Hall:  

Yeah, for sure. I think a solution looking for a problem actually describes the whole MarTech space, right? What I’ve heard on these podcasts with a lot of other organizations that are using this is you always, always have to start with the business problem you’re trying to solve, and then figure out if it’s a lack of data or data and analytics issue, or is it just we don’t have the right data. But you start with the business problem. And I’m still amazed how many people will start a conversation if they’re looking for a new technology. Marketing is a great example, right? Okay, we need an ABM tool. Okay, why do you need an ABM tool? Because everyone else has one, and we want to make sure we get one. No, your problem is you’re not getting to the right people in the right accounts. And there’s multiple strategies to achieve that. So again, this is something I hear in marketing all the time.

For this area that you cover and you have expertise in, let’s talk a little bit about the business strategy problem that might actually be a data strategy problem. Have you got some examples around, maybe it’s reporting, maybe it’s access to information, something like that? If someone comes to you with that kind of problem, what is it that you do to help them solve that issue?

Dr. Jonathan Fowler:  

Yeah. One that I think is so universal that I think everybody can understand, imagine a direct-to-consumer company, online retail. And they are, by their own advertisement, a data-driven retailer. And one day, one of the sales leaders says, at my old company, we used to have these things called client personas. And we weren’t trying to profile people, it was more of today, I talked with a Rebecca, or- that’s this person they’ve created. In marketing lingo, I think everybody knows what I’m talking about, your client archetypes. And then you find out, wait a minute, you mean you don’t have these at this company, and you’re doing how many millions in sales? And part of your whole business model is to have private client shoppers. And you don’t have these things.

Dayle Hall: 

Why do you think you’re surviving?

Dr. Jonathan Fowler: 

Right. So there’s that piece. And then on the other side of the fence, so to speak, you have all of these, all of management kind of wringing their hands about, the numbers are not looking right and we see shifting patterns because of the pandemic and this and that. And I look at this, I go, if we can connect A and B here, I think that we might make some progress with this big divide.

The business case there is, yes, we need to understand maybe- typically, I think most businesses are driven by three or four archetypes or personas of buyers. So you could go about this two ways. One is you could go the pure data scientist tech route and just talk about all the different ways that you could model and create this stuff and go hog wild, oh, well, I need this, this, and this in terms of data points. Some of it, we may have, some of it, we don’t, which, that’s one way to do it.

The other way is talk with the business and go, okay, what data do we have on hand right now? How comfortable would- those personal shoppers, how comfortable would they be in gathering new data from their clients, if they explain why this is? The place that you were before, how did they treat these client personas? So instead of really driving it, hearing just one, maybe one sentence from the business, say, I need this, you go, oops, say no more, I’m going to go back in my hole and just run all these crazy models and come back for something that you’re never going to understand. You tone it down and say, all right, let’s work through this one together.

Because you have to understand, look, I might be the greatest data scientist in the world, but I don’t know jack about retail. And so you have to recognize that you’re only half of the solution here. Your business SME doesn’t know what you know about data, but you don’t know what they know about their area. So combined, you’re much more of a powerful force.

Dayle Hall: 

Yep. Yep. Do you find sometimes that- again, you said in our intro that enterprises are generating so much data, that’s a challenge in itself. Do you find that sometimes, either management- the leadership of the organization, even when they see this access, are they scared to do something? They don’t trust the data. They’re worried they’ll make the wrong move. Do you see management teams that are just, I don’t say afraid, but nervous because they don’t trust the data or they’re worried about making a mistake? And how do they overcome that?

Dr. Jonathan Fowler:

A lot of it is simply they don’t know where to start because there is just a mountain of it. And there’s a lot of pressure, I think, in business to do something with it. And I think sometimes it’s easier to hesitate and not do anything than to make an attempt and feel like you’re doing the wrong thing. So I think that’s one thing holding folks back.

The other, data is essentially information, and information is power inside an organization. And I’ve worked with companies where when we try to do the data discovery, we wind up kicking up some turf wars and politics and the multiple company leaders like watching your parents fight in the living room. You don’t realize that it should be that the corporate data is this neutral thing. But people- silos are there sometimes because people have resisted sharing. And it’s just the way it’s always been. So when you start pulling those rocks up, pulling them back and looking under them, people get antsy. So it can be some of that.

It can also just be, and this is the one I hate, where we just need to survive. We just need to produce. We just need to do billable hours. We just need to blank. And the analogy I use for that one is years ago, when Howard Schultz was running Starbucks, he went into a cafe one day and he said, everything was all wrong, the espresso shots were not being done right, the place melted in cheese because they were cutting sandwiches wrong. So they shut down nationwide one Tuesday afternoon. And it wasn’t like a, we’re closed for a couple hours. They shut down the whole afternoon. And goodness knows how much business they lost that day. But they took that time to step back and reevaluate what they were doing and get it right. So they reopened the next day much stronger. So whatever they lost in business that afternoon, they recouped unbelievable amounts of multipliers from that probably because if they had kept doing what they were doing with their blinders on just to keep the business running, who knows what would have happened.

Dayle Hall:  

Yeah. And let’s face it. How many leaders of organizations would recommend, let’s stop working for an afternoon?

Dr. Jonathan Fowler:

Right. It sounds horrible.

Dayle Hall:

I don’t know many CEOs that would make that mistake.

Dr. Jonathan Fowler: 

No. And even when you introduce the idea of research, when I was doing my doctoral work and doing the research, it was in the height of the pandemic, and businesses were already on edge about just man hours and time. So when I was trying to recruit businesses for the research, I’m short of going, are you deaf? Why would you not get this free research? Because essentially, I was saying, look, if you give me half an hour with your employees, then here’s what you will get in return. Here’s all the data and information, and I’m going to do it as though you’re paying me to do it. But I need this for my research. I got so many, we just don’t have time, or we don’t have time to do research because that’s something that we don’t really care about, or it was always, we’re too busy. And I just- I go, if you’re too busy to learn about your business, then you’re just too busy, period.

Dayle Hall:  

Yeah. And by the time you figure out you should have been looking at that, it might be too late for your business, something serious enough.

Dr. Jonathan Fowler:

Exactly.

Dayle Hall:

You mentioned the turf war thing there about once you start really looking into the data and people within the organization potentially getting sensitive to it. As a marketing leader, I learned a good seven or eight years ago is the closer I can be to the numbers, the- how we’re helping the business better. I’m- actually, I am, as a CMO, sometimes it’s not comfortable. Sometimes it highlights things that I don’t even want to see or admit. But it does help the business and ultimately helps me. But that raises an area, which is that cultural aspect.

And that kind of comes to the turf war question, which is, if you put the management challenges aside and whether they feel or want to address it, are the cultural reasons as to why and how an organization can potentially deal with it- and if someone’s out there listening to this and they’re thinking, yeah, it’s the same in my organization. You can’t shift the culture overnight. But how would you advise someone to say, you have to start thinking of it this way? What do people do?

Dr. Jonathan Fowler:  

That is where, first, understanding that data is not this black and white thing. We have to suspend that belief first. And really, data is this gray area that, depending on whose perspective it is, can tell different stories. And you can get into data is power, power is information- data as information as power, and then you have privilege and access and all that, which is an interesting conversation. But from a business perspective, the same dataset viewed by five different people can quite possibly generate five distinct actions or perspectives or conversations or points of view. And it’s all based on what baggage do you bring to the conversation? I’ll give a good nonbusiness example that was very public.

The COVID dashboard for the state of Florida a few years ago, their data scientist that was maintaining that for the state government, she was doing the infection rate by number of positive tests divided by the number of persons tested, which sounds reasonable, right? The governor’s office said, no, we want you to divide by number of tests. So if I had 100 people and they were all tested five times, then my denominator goes from 100 to 500. And so my infection rate looks a lot better.

Dayle Hall:

Ah, I see.

Dr. Jonathan Fowler:

Yeah. Now both of those are true, right? Both of those are true data points. They’re not making up numbers. They’re just saying- they’re just using different numbers. And that wound up being a huge piece in the news. It was really unfortunate how that all went down. But that’s an extreme example of how a narrative can be disrupted and changed just by one shift, one little change in one figure.

And in business, let’s say everybody has access to the same data, and no numbers are changed, let’s just think about- let’s just say the data is everything else is equal. Let’s think about perspectives for a minute. Well, if you’re in finance and you are the group that everybody goes to when they have questions because you control the ledger, you control the transactions, you are the place where- the sort of ad hoc data kings and queens, then you think everything’s great. But then if you’re in field sales and your mobile devices can’t get real-time database updates and you have to do stuff on paper, then- and this is a real example, actually, then your view of data maturity and abilities are way worse than finance. It’s the same company. A lot of people ask, well, how can those be so divergent if it’s the same company? Well, a company is not this single monolithic thing, right? A company has different people, different departments, different perspectives.

Dayle Hall:  

Yeah. And I think that comes back to what we talked about, which was that you always have to start with what business problem, what use case you’re trying to solve, get people aligned around- your Florida test example is make sure everyone agrees that this is what we’re using when-

Dr. Jonathan Fowler:

Yes.

Dayle Hall: 

These datasets and this is what we’re going to show. We know there are other data points of truth, but this is how we’re going to show it and be clear on that front. And one of the things at SnapLogic, we’re very- allowing access to data, pulling data from different systems, that’s what we do. So we hear this a lot.

What we also hear and what I see in the market is a lot of people are looking at, hey, you know what, I’ll use AI and ML to solve my problem. I don’t even know sometimes if they really understand what AI and ML means, but they see it. And once your analyst is talking about AI washing and so on. So it’s pervasive everywhere.

Dr. Jonathan Fowler:

It is.

Dayle Hall: 

But when you hear that, when you hear that we can solve problems quickly with AI or the machine learning piece, first of all, what is your reaction? And what guidance do you give to people around using that to solve- let’s say they have a business problem. But how do you say, well, hang on a second, this is what it means? What do you tell people?

Dr. Jonathan Fowler:

At the risk of sounding like a Luddite, I just get a little bit of heartburn every time I hear somebody say, well, just put AI on it. Or there was a point, it seemed like, if a start-up had the letters AI or ML anywhere in their investor deck, people just threw money at it. Yes, please, take my money. We want you to throw this. AI in that noisy space of business tools and vendors and products, AI seems to have all the answers.

And it was funny. I was on a panel this weekend, actually, at MUSC, The Medical University of South Carolina, they did a collaboration on AI with Clemson. And I was on a panel talking about AI ethics. And yeah, this is a fascinating space. And I’ll say there- I’ll say here when I sit there, AI, we’re not at that point of the Terminator where AI is truly sentient. AI is just a collection of algorithms. And if you peel back that onion, the algorithms had to be written and tested by some person or a collection of people. And then those had to be fed by datasets. And if the data is garbage, then the algorithms are going to be garbage. And then the AI is going to be garbage.

Case in point, IBM Watson Oncology was trained mostly on- almost exclusively on records from the United States. And so the international community said, well, we don’t want this because this was trained purely on American-centric patients. What about underrepresented groups? What about Norwegians? And I think Norway was one of the nations who said no. And it wasn’t like they intended to do that. It was just, I don’t know, they just figured, well, here’s what we have. A lot of times, the bias there is not really intentional, but it’s still biased nonetheless.

I’d say, okay, if you’re so hell bent on getting AI in the enterprise, then think of it as an employee. And would you bring that employee in if it did not have the right environment to succeed and to thrive? Does it have a place to work? Does it have resources? Does it know who to go to? All those things. Well, if the answer to any of those is no, then you are not ready for AI. If you don’t have data quality, if you don’t have data governance, if you don’t have standards, if data is siloed, in other words, if you’re still running your business on spreadsheets and you don’t have an enterprise reporting platform that actually works, then you are nowhere near ready for AI. And don’t expect AI to solve those problems.

Dayle Hall:  

Yeah, for sure. And again, I love that analogy, think of AI as an employee. Make sure they have the right environment to be successful. That’s a really good analogy. So let’s move on a little bit. And you touched on it a little bit with the ethics, the AI ethics. And I’ve done one of these podcasts with someone that was doing tech for human resources. And I was amazed at how they have to think about the ethics of AI, you mentioned it, the diversity, the different groups, the different ethnicities and so on. And there is some inherent challenges with that. But something that we talked about in our prep calls was around data empathy. I’ve actually never heard this term before. What is data empathy and how does that work?

Dr. Jonathan Fowler:  

Data empathy comes out of tactical empathy, which comes around the same working area here. Let’s take, for example, that I’m a clothing retailer. And I want to know what designers are selling well in Spain. I know when I formulated that question, I know what I want to get out of that. But okay, I asked that question, and then my data team, it figures that out, it runs the report, or either I do self-serve analytics to get it. And well, okay, first, Spain, okay, number one, stuff being shipped to Spain, or customers who live there that may ship somewhere else. There’s one question. The next question is, well, how do we define selling, do we define total amount sold? So in that case, it could be one product that was a lot of money, or a few products that were a little bit of money, or do a number of items sold? And then do I- in the last 30 days, last two years, what  do I mean?

So I know what I want when I say that, but I have to translate all the unspoken things. I have to translate that to either the person I’m asking for the information or somebody else that I’m sharing this with. The first principle of data empathy is that somebody’s definition of a metric or a concept might not match your own. So what products are doing well in Spain? I may define that one way, you might define that a different way. And a lot of times, I think we overlook that because we can say, well, let’s just grab those data points. Here’s the report. Here’s the truth. And you might think that I got to it one way. I know I got to it a different way, but we never share that information. So we already have created some implicit misunderstandings.

And then the other piece of that is when you present the data, what does it mean for somebody else? And there’s that second principle, which is, what you see in the data might not be obvious or important to somebody else. So if I’m looking at that and I’m worried about, let’s say, the monetary value of those orders in Spain, for example. Well, the marketing team is probably more interested in the individuals and customers that are purchasing those things and their demographics, what makes them tick, what campaigns made that happen, that sort of thing. And so, again, here, I’ve created something that answers what I want. But, well, what about what others can get out of this? And do I make this sort of a one-off thing for myself? Or do I make it and bring other people in so that we can now have this become a much broader conversation instead of a siloed full report?

Dayle Hall:  

Right. Yeah, I don’t want to use- it’s a clothing retail analogy. Beauty is in the eye of the beholder. I think that holds today too and itself is that, what it is that you might be looking for is different to someone else. And I think- and the data empathy part, I think, is understanding that there’s other opportunities to pull something out of the data. You have to look at the definitions, not just that you’re looking for where other parts of the business are.

Again, think about someone listening to this. I’m looking at data across my organization. How do you- I don’t want to say solve it, but ways that you get around making sure that organizations understand different perspectives, that there is less bias in it? And you understand, going back to our previous point, which is, everyone can interpret the data, specifically, like how are organizations solving that?

Dr. Jonathan Fowler:

A really quick and practical way is to make sure that you have a data dictionary and a definition of all the metrics. Because I remember, one company that I did some work for, I know this acronym when somebody hears that they’re going to go, oh, I know what that is, immediately. This was a new one to me, GOV, gross order value. That word or that acronym, probably the first couple of weeks I was at that company helping them out, I heard that a million times. And nobody ever told me what it was. And I finally asked and said, oh, it’s gross order value. Well, how do we define that? And the answer I got was, well, it’s whatever the computer says it is.

Dayle Hall:  

Okay. Well, good, as long as it’s done by people that really need the values.

Dr. Jonathan Fowler:

And I go, whoa, whoa, whoa. So we don’t have an actual formula or written down- because the computer didn’t make this up. Somebody actually wrote the code to calculate that. So we need to find out what that says. And that is a humorous example, but it’s one that happens a lot where you just kind of run on these acronyms and metrics that are defined somewhere. But if they’re not readily available to the average user, then it’s the same as not having it. So an accessible and regularly updated data dictionary is going to be number one.

More of a deep dive would be to understand, how does each team in the organization value and really work with data? And there, we have something- let’s take, for example, marketing and human resources. Well, although we belong to the same company, human resources is going to be more concerned with data points that marketing probably isn’t, and vice versa. Marketing would probably be more of a competitive tight group that data there is used to get to competitive edge and to mitigate risk. Whereas HR is more internally focused and data usage there is more interested in controlling internal ops and maintaining compliance. So understanding that those are two very different perspectives in the same company, not being afraid to call that out. I’m not yet ready to make this leap. But in some ways, I see this in the same field as talent diversity.

So when we talk about diversity in companies, there are so many rich ways that diversity is defined in companies. And that largely has to do with individuals and what they bring in their life experience, right? I think that you can make the argument that data personality types, essentially, are one element of diversity because we’re thinking about the diversity of thought and perspective. To me, it’s still a very politically charged notion. I just don’t want to go down that road. But I do think it’s important to acknowledge that it is a diverse area.

Dayle Hall:  

Yeah, yeah. But certainly a lot to think about. But I do like the data dictionary concept. As we’re wrapping up, let’s talk a little bit about this framework that you create. So everyone on this listening really understands what is an analytic- what is this framework around analytics personality types? How are you using it today? And where did this concept come from? How are people using it?

Dr. Jonathan Fowler:  

It started when I was working with a company and their first request of me was to take the tool that they had just been sold and make reports, make better reports. And that-

Dayle Hall:  

Make them better.

Dr. Jonathan Fowler:

Yeah, make them better. Forget about the data, we just want better reports. So I started working backwards. I said, okay, well, what’s your data look like? And then took a step further and said, well, tell me about the users and what they need and what do they have right now and all that. And I just found we needed to do some major homework here. So I did an early version of what has now become my assessment framework where I asked about their top data priorities, their top challenges holding them back, and just tell me more about their daily use. What do they use data for? What are the current pain points? Open question. And also rating maturity from 1 to a 5 on different subscales.

And that gave me a good picture of- I went to six locations across North America for that company and interviewed in-person and got about 40 participants. And we got a really nice representative sample of current state in the organization. We saved them a subscription for a tool they didn’t need. And we gained a tremendous amount of political capital because we were not just one more consultant coming in, we were somebody listening and wanting to know what are things like right now. And we delivered on that.

So I took that. And in my doctoral research, I added on to it what’s called the competing values framework. It’s been around since the ‘80s. There’s been some research around tying that to information culture in companies. But I didn’t really see anything that tied it to how different teams functioned. So I put those elements together and came up with the analytics personality types or aptitudes. And there are four types. There’s collaborative, creative, competitive, and controlling. All of us have all four types. It’s just a question of what’s the strongest. And every division has all four types. And you can actually map out each division how they score. And let’s say, you have five different divisions in your company. Usually, if you stack those on top of each other, you wouldn’t see much difference. But everybody has a slightly different dominant quadrant.

So for example, most companies usually are dominantly collaborative. That’s the space where it’s about communication and participation and all that. That’s usually where everything falls. But then you get into, well, some divisions inside the company are more competitive, some are more creative, some are more controlling. Then the question is, okay, if I am rolling out a new reporting toolkit or a new BI Initiative, or really anything, then how am I going to communicate to each of my divisions to make sure we have the most engagement? So for example, merchandising versus IT. Merchandising, probably going to be a lot more creative in their approach. They’re going to use data for new opportunities, managing risks, supporting innovation. IT is going to care a bit more about participation and communication and getting everybody on board, right?

The old way of rolling out these initiatives was, we had a project team, we vetted it, the executive said yes, we implemented it. Now everybody’s expected to train on it and use it. Two years later, we’ve forgotten what it is because it just failed. This way, you go, okay, number one, what are my pressing needs? Number two, what do my users value the most? Number three, where are we strongest, and how can we use where we’re strongest to really bring us up in areas that we’re the weakest? And then finally, how do I message this and engage each team in the organization so I meet them where they are? And that’s the assessment framework.

Dayle Hall:  

And when you identify different organizations and the four different types, is it preferable to have more of one than the other? Are you looking for a balanced approach between creative, controlling in those four that you mentioned? Does it matter?

Dr. Jonathan Fowler:

As we do more of these at companies and get more data for our own research purposes, I’m interested to see that exact answer, does it matter? Do particular verticals do better with a different mix? Or does it need to be more monolithic across the organization? This is so new. And like I said, I did my work in the middle of COVID, so I didn’t have as big a sample as I wanted. So I’m still in that phase of if the company is interested in doing this, I will do it for you for free as long as I get the data because I feel like it’s so important to test these hypotheses and really make a strong contribution to a new barrier here.

Dayle Hall:  

Yeah. We talked about was, start with a business case, start with a use case, the problem that you’re trying to solve. I think this, if you’re trying to understand in an organization. Are we getting most value from the data we capture, from the tools that we use? SnapLogic talks about it all the time. We have a tool that breaks down data silos, but that doesn’t necessarily solve everything. What you’re talking about is then you need the organizations to have those types, those personality types to be then, okay, then we want to hear it, yes, we want to share, yes, we’re open to it.

Dr. Jonathan Fowler:

That’s right.

Dayle Hall:

So solving the process with a tool or a platform, that’s just one part of it. And what I think we’ve covered today specifically is, then how do you raise that up and make sure that you’re using the data in the right way to solve the business cases? So that’s really interesting.

Dr. Jonathan Fowler:  

Absolutely right.

Dayle Hall:

We’ll definitely stay connected. I’d love to continue to hear about how this is progressing.

Dr. Jonathan Fowler:  

Yeah. One of the things I run into a lot is, what’s the ROI on this? And I go, okay, if we’re talking about ROI, we’ve already missed the boat here. We’re wanting to do this before you entertain any new capital expenditure because we want to make sure that whatever you’re about to put out there is going to be received well and you’re not going to have a $130 million mistake because you didn’t do your homework first. And that’s actually happened.

Dayle Hall:  

Yeah, I’m sure. Well, Jonathan, it was a pleasure to have you on this podcast.

Dr. Jonathan Fowler:

Thank you.

Dayle Hall:

A lot of insights. So everyone out there, thank you for listening to myself and Dr. Jonathan Fowler, the founder and CEO of Logicle Analytics. And we’ll see you on the next episode of our podcast, Automating the Enterprise.