The Intersection of Knowledge & Data

2020 has challenged businesses in many ways. It has required organizational agility, meticulous strategies, and difficult compromises for businesses to adapt to these unprecedented circumstances. The speed, accuracy, and agility needed for success during this time is raising the question: What will it take for organizations to collect the right data at the right time to make the right decisions? ‍

This presentation will explore how now more than ever, businesses must collect accurate data about their customer experiences to derive actionable insights that drive effective engaging experiences with their customers. Organizations need to validate their marketing ROI, message and channel attribution, sales productivity, and more—and can only do so with trusted sources, effective technology, and a proven methodology. The result for these organizations is an internal alignment and decision-making processes that not only accelerate growth, but can also weather any challenges the future holds.



John Neeson (00:06): 
We live in a world with an explosion of data. How many times have you heard, we're going to make data-driven decisions, or maybe, we're only going to look at fact-based intelligence. Perhaps you've gotten from your superiors, hey, I want you to go back, look at the data and tell me what the data directs you to do. Hi, my name's John Neeson, and I'm an adjunct professor at Boston College where I teach digital marketing and analytics, and entrepreneurial marketing. I'm also a lecturer at MIT Sloan, where I'm part of the enterprise management lab. I'm an entrepreneur, co-founder and co-CEO of SiriusDecisions, or was until we sold in 2019. And I'm here today to take you on a journey with me. I'm looking at the intersection of knowledge and data. And, you know, in my second career, teaching, what I find interesting is that we're in this world with an enormous amount of information and how we use that information is really, really critical. 

John Neeson (01:03): 
And I want to give you a great example of one of the biggest challenges that we have today, when we start to think about how we're going to use information. Imagine for a minute, you're in a business review and you're presenting to a CFO, a CSO and a general manager. Now look at them here. You know, I want you to match it to that. Now you're presenting, but you're going to present on a very, very difficult subject—marketing attribution. So think about the information that you would present. First, you're going to go and talk to the CFO. Look at the CFO, look at the steely eye. She's kind of looking at you. And I think if there's a word cloud over her head, it's saying, can I really believe these numbers? How do I attribute a return to each of these marketing tactics or channels? 

John Neeson (01:46): 
You know, and I'm reminded years ago, I was presenting to a group of CMOs, and I happened to have the CFO in the audience. Not too often that happens, but the CFO came up to me after the presentation and I was presenting on benchmarking. So imagine this beautiful chart with, you know, a myriad of different marketing tactics and channels. And the CFO said, John, could you pull that up again? They said, there's like 30 or 35 different things. I really want to get down to two or three things, get rid of all the other stuff. Because that's really waste. My response was, you know what, no offense, but I think you're part of the problem, not the solution. Would you put all of your investments into two or three vehicles? Probably not right, you kind of play and have a yield to it. 

John Neeson (02:28):
And that's how we want to think of things. And the CFO said, that's fair, but how do I do attribution? And that's the challenge. A lot of that comes down to the right data. So hold that thought. Second, you've got the chief sales officer and you kind of see him there with his arms crossed and his, you know, his head's probably saying, gee, I've never seen these numbers before. And why are your definitions different than mine? I have this incredible CRM system that I trust. I don't know if I trust this information. And now to the right, you see the general manager with his hands over the head, which I think his word cloud really would be, I had a tee time, but I'm stuck in this room now, and this is really difficult because I don't really believe in the information that I see. I have tons of experience. Why is it that I need to spend my time looking at these numbers? 

John Neeson (03:09): 
Now tell me how many business reviews you've been to, where you've had some of this frustration, if not all of this frustration and why is it that we have this challenge? How do you want that business review really to act? You actually want it to be about making decisions, solving real problems. So when thinking about this, you know, one of the things that, I've done in my teaching is I read this wonderful book by Rashid Tobaccowala called Restoring the Soul of Business. And I gave it to all my students last semester and said, you've got to read this because it's really about how you should be making decisions. And after teaching digital marketing analytics, I explained to them, it's simply not just those two. 

John Neeson (03:56): 
There's a way in how you present information, and more importantly, the data, that it's really, really resonating there. Now, Tobaccowala said, if you go back and you look at a quote from Plato, Plato said that, you know what a good decision is based on knowledge, not numbers. Think of that. A good decision is based on knowledge, not numbers. So on the previous slide, did we not really need to have that meeting? Did everyone have the knowledge they needed? Probably not. And what Tobaccowala has said, if Plato were alive today, he would rewrite that quote. And the quote would be a good decision is based on knowledge and the right data. Now that's the question. What is the right data? What is the right day? We're going to talk in a little bit about what are the principles of the right data, but also let's look at its relationship with knowledge. 

John Neeson (04:44): 
And this is really quite interesting to try to understand. How do we use data in knowledge, what is its place in this ecosystem? It's really important that we understand that before we try to figure out, well, how do we look at the principles of data. Now, before we kind of go down that path, let's look at what's the problem. And first of all, we have tons of data. In fact, if you look at a variety of different sources, this happens to be from Splunk, a group called True Global Intelligence. But Gardner, Forrester, all the research analyst firms talk a lot about something called dark data, which is data that you have, which you're not even using. It's just dark. So think of all that awesome data that you have on your website about your customer journeys, your prospect journeys, but you can't use them effectively. 

John Neeson (05:29): 
That's dark data. So we have too much data and we also don't know how to access it really well. Next is, let's think about the analytics operating model, and this is, you know, between my serious experience and my academic experience, spending a lot of time looking at how companies create their analytics, is really, really critical. And you can see here from this study, 73% are investing in it, but only about 10% really have implemented analytics in a comprehensive way—something that they would call an analytics operating model. And last you look at trust. You know, this work by KPMG stating that only about 43% of executives believe that the data that they have from their systems and analytics is accurate. Well, let me put another way. If you've got 10 people in a business review, six out of the gate don't believe what you're saying. That's a problem. 

John Neeson (06:20): 
Now we can fix the first two, but the last one requires deeper exploration. Last one requires—what is it about the data? What could we do about the data to make it even more effective? And that's an interesting journey for us to go through, and I'm going to go back a little bit. In the mid nineties, there was a Japanese consultant and professor whose name was Ikujiro Nonaka, and Nonaka did a lot of work looking at how people and organizations created knowledge. And he came up with four ways in which we create knowledge. So let's think about this. Then I want to take a little bit of a sidestep and say, well, what would data's relationship be in that? What would be the impact if it was really the right data? 

John Neeson (07:10): 
And finally, well, what is the right data? How do we begin to kind of think that through? The first thing that Nonaka found was we get information through socialization, a tacit to tacit set of exchanges that we go through, exchanges in which we're passing knowledge through practice, through guidance, through imitation and observation. The more and more people we talk to, the smarter we are. That's one way in which we gather knowledge, and in an organization, you know, we've spent time actually organizing ourselves. So it's easier to collaborate with created systems like Slack and others in which we can collaborate more. The second way is through internalization, you know, kind of an explicit where you're looking at data to a tacit, when you're looking at your experiences and saying, okay, I'm going to internalize this. Now I'm going to reflect upon this. 

John Neeson (07:58): 
And based on that, I'm gonna come up with some solutions. Actually let's be real. There is an aspect in that business review that I just shared with you, that, with those three personas, some of those might need more internalization. They might need to look at the information before they even show up. So they can go through this process and create their own knowledge. The third way that Nonaka found was combination. You know, this is where you're beginning to combine pieces of information. You're taking a piece from system A, system B and combining with the other and creating a third piece of knowledge. And finally, there's the externalization, tacit to explicit where knowledge is now being codified in documents. It's being spread around the organization and it's becoming language. You know, in my own experience, I spent a number of years at Gardner and then founded Sirius. 

John Neeson (08:47): 
One of the metrics that we used was something we called NCBI. Net contract value increase, which is essentially growth. There's a lot of more modern SAS metrics that are used today that sort of fall in that same spirit, but it was something that the entire organization could galvanize around, could understand. And it became part of our internal language. I realized this might be a little esoteric. Let's drill down a little bit further and start to look to see well, if we think about this, if we think about, you know, this information, we start to say, well, where would data really reside? And the right answer is the model that Nonaka came up with, but he was having in the center, essentially these tasks and explicit exchanges kind of going back and forth and creating energy and essentially through knowledge management techniques that you could improve the decision making through socialization, combination, externalization and internalization. 

John Neeson (09:42): 
In fact, a lot of the modern knowledge management systems were based upon his theories. I think that's appropriate. They're all good. I think the difference is now there's been this explosion of data and data now is at the center. Data actually is really driving a lot of those decisions and driving a lot of the knowledge creation from these different organizations. So in some ways, if you think about it, if you've got the right data, it's going to accelerate the knowledge creation that organizations have. If you have the wrong data, however, it's going to create siloed opinions and flawed reporting, which a lot of organizations end up with. And that's why you see, in some of the challenges today, where organizations are struggling to have a systematic way in order to create this knowledge. So this is interesting. I want to go back to, you know, not just theory, let's talk about data, you know, and what it really means. 

John Neeson (10:33): 
And this is quite interesting in that if I were to say to you, well, what do you need? What do you need in order to look at the data and actually feel that that data is actually helping in the knowledge creation process. The first thing, in socialization, when you look at how it's being socializing, it's gotta be transparent, right? So you know, imagine you're looking at some information that's coming to you via email. You're clicking on a system and you're clicking a number and you're drilling down and getting more and more information. And it's transparent to you. Now, you can do that in an organization. When you look at the socialization in social media today, how many times do you see this data point that's been taken out of context? And we get in this ridiculous discussion about, well, what are the sources of information that you really have? 

John Neeson (11:21): 
That's fake news. Well, you know what, we're going to live with that for a while until we resolve that. But in a business, we can't have fake data. Yeah. But if your systems are transparent, you have the ability to improve the social issue. That's the really important thing is, you gotta be able to have the systems, the technology in order to make this happen. Second is the internalization. Ask yourself, okay, well, how would you internalize information more effectively? Well, you know what? If you trusted the data, it would accelerate your ability to create more knowledge. And what would it take to trust? What it would take is integrity, right? You would trust it based on how you believe that someone is principled. That's why you trust them. They have a moral higher ground that you have an ability in which to look at them and say, I really trust you. Well, data's the same way. You know, if you have data that has a great hierarchical architecture, that's got taxonomy, suddenly you've got greater trust, right? It's got greater integrity. And when you internalize it, you're going to actually make much more effective decisions. The third is when you start to look to say, hey, how confident are you? You know, are you confident in the information that you see and confidence frankly, is deployed effectively. If there's a methodology, you know, and this is where I love the view of thinking about the customer journey and the buyer's journey, right? 

John Neeson (12:44): 
If you look at those two things and they become your denominator, can't we all agree on those? Now, if I have something where I'm looking at the buyer's journey and you know, start to attribute you know, some certain marketing tactics to making impact and showing that look, it's also having an impact in the retention rate, that is incredibly valuable. That combination becomes powerful, but I have to have a methodology. Without a methodology, this just doesn't work. So you get that methodology again, but you know, through the aspect of having some consistency and an approach of how you're looking at the data, and again, my favorite is thinking of putting the customer first, agreeing upon that. And that suddenly creates a very, very different way in which you're creating knowledge. Fourth here is externalization. 

John Neeson (13:33): 
To have confidence in data that you're going to send around to the organization, you have to feel that it's consistent, that it's got a resiliency that really is quite effective, and that it becomes language. You know, my earlier comment about that metric NCBI, that was so incredibly powerful for our organization. We had the ability to look at well, what is its impact on finance? What is it's impact on research? What is it's impact on every single part of our business? And we have that because we have this language that we created. We use it consistently. So it had a resilience. So what am I telling you? I'm telling you here, if we start to take a step back and we look at these principles that I have in front of you, suddenly we have the right data. So let's look at what those principles really mean in terms of its impact and what you need to do to achieve it. 

John Neeson (14:24): 
Because that's really, really important. First, you know, what you need is that data to be transparent, right? It's gotta have transparency. Therefore you have to have the right technology in order to kind of make that work right. And if you have that, the speed at which you're going to be able to make decisions, particularly when it comes to socializing information, is going to be extreme. You're going to find that to be very, very effective. The second principle is one of trust. Like when I mentioned earlier, to have trust you have to have integrity in the data hierarchy, a taxonomy, a way in which it's organized, it's going to be incredibly effective. And that creates conviction. Particularly when you're internalizing, as you're sitting there internalizing data, you feel much better. If you could actually feel that the organization has achieved integrity in the data that you're provided. The third principle is confidence, and you get confidence through methodology. 

John Neeson (15:22): 
And again, I'm going to go back to my favorite aspect of thinking about how do buyers buy, how do people renew? If you think about that journey, suddenly the discussion of the sales process, the discussion of which MarTech is most effective, your ability to create attribution is much, much better. And also it creates a very comprehensive way in which you can start to look at different combinations that make it incredibly effective. And third business resilience. And again, I'm going to come back to, this is a process. This is the process in which you're sending marketing metrics or any metric out into the organization. It's used in a manner that's consistent, becomes language. I can't tell you how many companies I talk to where they say I put in a dashboard and then we stare at it because we don't know what it means. 

John Neeson (16:10): 
We haven't had the process in which to say, let's look at what, if it shows A, what it means from a B standpoint. We haven't had a way in which to internalize it effectively. Now let's think for a minute. I'm gonna go back to the business review I just shared with you ,and ask yourself a question. If you came to a business review and you had these principles, what would it be like? Well, you probably wouldn't have the CFO, you know, kind of giving you the, looking directly at you feeling like time is being wasted. And they probably wouldn't have the head of sales feeling like, okay, I'm not sure I buy this. And maybe instead of this, you're going to get this from the general manager. You'd have much more openness. You'd spend time together, actually creating more knowledge, making better decisions. 

John Neeson (16:54): 
And that actually has a huge, huge impact. We waste time in business because our data is not really set effectively and we can fix a lot of it. It's not easy, but we can fix it by looking at some of these principles to say, if we had this in place, what it would really mean. What's its impact? You know, we could probably go and talk a lot differently about what the impact might be in organizations. My experience at SiriusDecisions—you know, SiriusDecisions did a lot of work looking at the impact of sales, marketing alignment. We did a number of studies finding that, you know, organizations that achieve that, they grow faster and you know what, they're more profitable. They have an ability to take a dollar and say, if I put it in sales and marketing, I know how it does a little bit more effectively in one versus the other. 

John Neeson (17:42):
But the relationship was because they have processes, they have methodology, they have consistency and they have the right data. They've created it by its very nature. I have to have it in order to achieve sales, marketing alignment. There's also been some great work done by McKinsey and a number of others. This just happens to be one I thought was really cool. It was in our business review, looking at quantifying the impact of analytics and here, what McKinsey does is they took 11 different elements, they called them units, and said, when an organization applies one of these aspects of marketing analytics, there's an impact to profit. In fact, just increasing one of them actually has a, you know, almost a 0.4% increase in profitability. Now it's not linear, unfortunately. So it's not going to get 11% increase in profit. Like the max is about one and a half percent, but that is significant when you really start to think about it. 

John Neeson (18:35): 
And by its very nature to make that work, you can't achieve it without the principles of data that I just shared with you. Now you might say, gee, are they ubiquitous? Are they, you know, is this a way of kind of thinking through that one relates together? There is definitely a relationship, there's no doubt that there was a relationship with each of these. However, when you start to really think about them, you have to look at them each individually and say to yourself, can I really achieve this? And again, the four were trust, transparency, competence, and resilience. Here's a couple of questions. I would, I would challenge you to look at your data and say, have you achieved that? The first is, does the data have the integrity for you to make effective decisions today? The answer is no. 

John Neeson (19:22): 
Then you've got a problem. Now I would also suggest that same question on business decisions. You know, you get requests to have tons and tons and tons of business metrics. I often say, if I look at each metric and ask myself, well, what business decision will I make with that metric? You're going to have fewer where you can have a lot fewer and it's going to be much, much more effective, but integrity. If you asked that question, it's going to be a retelling of what does it tell me about the data that I have and what do I need to do in order to increase that integrity? And my guess would be, it's probably going to be something that's much more about how the data is structured, your taxonomy, your thinking about its relationships. Now second is transparency. 

John Neeson (20:05): 
And this is technology. Does the technology allow you to explore? And there's a lot of different technology by the way, you don't have to have, you know, just one technology that does everything. You can have a stack and the stack works together. In fact, you have to have a stack in looking at today's market, but the reality is all of those need to achieve some transparency. You have to architect the systems in a manner that's gonna allow you to really be quite effective in how you're allowing somebody to explore and how you're allowing somebody to essentially be able to pull forward the right information so they can feel comfortable in what they're socializing. Next is confidence. And this is, do you have that methodology and the numbers, and that becomes really, really important. And again, I'm going to go back to my favorite area in thinking about, you know, the buyer's journey. You know, if you look at the movement and the BDB side with account-based marketing, you know, you can't do that effectively without having attribution and methodology and thinking about the buyer's journey. 

John Neeson (21:02): 
It is incredibly powerful to do that. And actually just as in my teaching, we work with a lot of companies working on little projects and we did a project last spring working on the buyer's journey, then COVID hit. So I tell the students, okay, blow it up. And now think of what the buyer's journey is, which was really quite fun because, well, I shouldn't say it was fun. It was interesting in that you've got a bunch of fresh minds now thinking very, very differently along with organizations that needed to do the same, but you need a method, you know, and that buyer's journey or the customer's journey, it seems to be, happens to be one. Then finally, resilience. You know, your processes becoming language. And if they're becoming language, there's a consistency in how they're using information that becomes really, really quite effective. So that's my take on when I look at the intersection between knowledge and data, that there is a very, very important role the data plays, but it has to be the right data and it has to follow these principles. I hope you enjoy the rest of this symposium. It's an incredible agenda. I think you really, really going to have fun and I want to thank you for the privilege of your time. I look forward to seeing you all in the future and I wish you the best of luck and be safe. Thank you.

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