The Intersection of Knowledge & Data - John Neeson, Founder Emeritus of SiriusDecisions

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

Founder Emeritus of SiriusDecisions, Board Member, and Adjunct Professor

John is a well-recognized thought leader in business-to-business sales, marketing and product management. He is an Adjunct Professor at Boston College and Lecturer at MIT Sloan. John was Co-CEO/Founder of SiriusDecisions a research and consulting firm focused on business-to-business sales, marketing and product management. SiriusDecisions was sold to Forrester Research in 2019. John was often cited as one of the top 10 most influential people in marketing today by the B2B News Network. Prior to founding SiriusDecisions in 2001, John was chief executive officer of Skila, a SaaS provider of business intelligence solutions for the pharmaceutical, medical device and biotechnology industries. John also spent 14 years at Gartner as a member of the company’s senior management team. He was one of the five officers of the company and was instrumental in helping Gartner grow from $25 million in yearly sales to $1 billon. John has BA in economics from Ithaca College, and where he established the Neeson Business Analytics Lab and Neeson Digital Marketing Lab.

We live in a world of exploding data. Think about the election we just went through and the amount of data that came at you from a variety of different ways. And you were faced with, "is this real? Is this something that I should be concerned about?" And took an enormous amount of work to trust the information that came to you. Now, think of your own business and the amount of data you have to sift through, and look at that data and wonder, "Hmm, is this something I can make a decision on?" Hi, my name is John Neeson. I'm an adjunct professor at Boston college, a lecturer at MIT Sloan, a board member to a number of privately held companies. And I'm the previous co-founder and co-CEO of SeriousDecisions, which is now part of Forrester research.

I want to welcome you here today, and I want you to go on a journey with me to talk about the impact of data on decision-making, and how data can actually have a huge impact on the knowledge creation in an organization. We're going to talk about the intersection of knowledge and data. I'm gonna make the case that frankly, if you really look at data and it's principled in the right way, it can accelerate an organization's ability to make decisions, grow effectively through the knowledge that it creates. So let's begin.

Let's start by imagining for a minute that you're part of a business review. Okay, you're going to go in and you're talk to a number of executives and you're the Chief Marketing Officer there for your quarterly business review. And the first person you see sitting in front of you is the CFO. And you can see from her steely eyes, if you were to have a word cloud overhead, it might say something like "skeptical". Am I really going to believe the numbers that I see? She's seeing numbers brought to her and she's saying herself, "should I believe these numbers?" And what she's really trying to determine is "how do I attribute the return on each of these marketing tactics that's presented to me?"

I'm reminded a number of years ago, I was presenting in Boston, Massachusetts to a group of CMOs, and it was showing some benchmarking data. Now picture a pie chart with lots of marketing tactics and showing here's what the average marketer typically uses during the course of the year. So it might have been 30 or 40 different marketing tactics. And I had a CFO come up to me during a break and say, John, can you pull that chart up again? I don't have too many CFOs come to a marketing event, but nonetheless I did that and she said, "Do you see how there's 30 or 40 things, how do we get it down to like three or four and just get rid of everything else? Cause that's waste." And I paused and said, "I hope you take this the right way, but I think you're part of the problem, not part of the solution. Because you would never put all of your personal money into three or four individual stocks, would you?" You create a portfolio and that's what we need to do here." And what I realized is what she looking for a methodology of how she should be thinking about this. So hold that thought.

Imagine the second person in this business review is the Chief Sales Officer, and you see he's got his arms crossed like this as you're presenting data to him and he's like, "wait, I've never seen this before. I've never seen this data. I have a CRM system that I trust and you're showing me new definitions. You're showing me new ways to think about this data. I'm not prepared for that. And my key question is, did it create revenue or not?" So again, we're throwing this information. He doesn't trust the data.

Now let's go to the General Manager where perhaps he's in charge of a product line that you're doing marketing for. Now, he's got his hands over his face. I think his word cloud is saying, "Hey, I had a tee time at one o'clock I should have taken it because frankly, I feel like I'm wasting my time here. I don't have competence in the process or the tools that have been used here. We have our own, and I've been doing this for a very long time." So think about this. You've walked into this meeting, you've presented data to them. None of them are buying it. So what could we do to change this environment? What would make the data more believable, more principle? What are the key things that would need to be in place and what would happen if that were achieved? So let's think about that for a minute.

In my teaching, I ran across this book called Restoring the Soul of Business by Rishad Tobaccowala. And it's a really great book, I give it to all my students in my digital marketing analytics class. And frankly, it's there to say, look, there's a lot more just to analytics. And what we're she brings out is first a quote by Plato that says "A good decision is based on knowledge, not numbers." Knowledge, not numbers, but what Tobaccowala says is if Plato were alive today, he probably would change that. And he would say, good decision is based on knowledge and the right data. So our quest today is to figure out what makes the right data and what is the impact of that right. Data, because the impact could be profound in the business review that we just saw because clearly all three of those executives don't believe that we have the right data, something is missing. So let's think about that. 

Let's first take a step back and say, well, what exactly is the problem? You know, why is it that no one believes this data? And first there's three phenomenons. I want to just share with you. The first is: There's Too Much Data. In fact,  Splunk and TRUE Global Intelligence did a study, looking at the amount of data that organization has doing surveys. And they found that 55% of all data collected is dark data. And what that means is it's never touched. It's never used, it's never harvested. So we have all of this intelligence that's not being put into place. So the question is have half of it. Isn't being used. No wonder you have challenges in an organization saying, gee, I don't really trust the information that's in front of me. 

The second thing is that even if you have data, you have to put it into an Analytics Operating Model, some way in which the data is going to be presented in an effective manner. It can be trusted. It's clean, and 73% of organizations, according to a center or investing in that, but only about 10% of the organizations right now have implemented a really good analytics operating model. Now that operating model is not technology. That's actually the processes, the methodologies that are behind it. We're gonna talk a little bit more about that because that's important. That's a non-technology related issue. 

So everybody has lots of tools. There's lots of toys for us to use to analyze data, but what's needed is a comprehensive stack that actually allows us to create this operating model that we can trust at the heart of it is data, which we're going to spend a little more time on. So the result is, KPMG did this study and they found that 43% of all senior executives trust the accuracy of the data in their analytics systems, 43%. So in that business reuse scenario that we just talked about let's suppose there were 10 people that means that six don't believe you, that's a problem. So that's a real, real problem. So how do we fix it? What are the things that we need to do in order to fix that and what could be the impact? So to do that, I want to go back in time a little bit.

There was a great professor and a consultant in the nineties called Ikujiro Nonaka, who built inand studied how knowledge is created in an organization. And he built this model that a lot of the modern knowledge management systems actually are based off of. And it's really quite interesting. And I want to explore that and say, well, "what would happen in today's world, as we begin to apply it, and what could we do to enhance it with data?" And what Nonaka found was the first thing that we do when we, first avenue a business, can create knowledge is kind of a tactic to tactic experience through socialization. So knowledge is passed through practice guidance, imitation, observation, it's where we can interact. And actually we're challenged right now without with COVID we're an organization, culturally has to do this to be a zoom, which has some challenges to it. So that's the first thing that new Nonaka found. 

The second was that knowledge is created through the Internalization, through the movement from explicit to task it activity. So you collect and reflect on information. So think about things that you learn, maybe at a meeting and you go back and you think about it, you internalize it and suddenly it becomes knowledge that you're going to use on an ongoing basis. 

The third thing Nonaka found was there is a combination. So I take, you know, documents, experiences, combine them together. And here's a new piece of knowledge that's created from that it's additive. So it's a combination of those things. And finally there is the externalization. So we're suddenly the task to explicitly knowledge is codified. It documents reporting, and frankly becomes part of the fabric of the organization. Now I have an interesting experience in, um, in this with our, with our serious decisions, uh, business, we had a, a metric called N CVI stands for net contract value increase it's really growth, but it became part of our fabric of our organization.

Everybody can tell you the impact of NCBI could be department could talk about how they were contributing to NCBI. It was something that was part of our language and part of the organization. And this is where Nonaka saying, if you look at an organization and see how they learn, if they see if they have these elements in place, it really can impact an organization and how they develop knowledge. Now they accelerate their ability to make more effective decisions. So let's explore it a little bit further. This is the Nonaka little model on the right where he's saying, look, these four elements when they're working in an organization there's energy that's created. If you actually can find ways to help exploit their ability for employees to leverage that. And that energy feeds on one another, the explicit, the tacit experiences, and it accelerates that organization's knowledge.

So what I think is interesting is if Nonaka were with us today and he wrote a look at this model, much like Plato would change his quote Nonaka would say what the center of this is data. Really, what it's saying is that data can accelerate the creation of knowledge, the right data. We're going to talk about what that right data is and the principles that are really random. But this is what Nonaka would would say is, "yeah, this is, this would be a way in which for an organization to impact how they make decisions much more effectively. It can accelerate knowledge." Now the wrong data can do the complete opposite can create silos, opinions, flogged reporting. I mean, how many times do you have you walked into meetings where one department has one set of data, another department has another set of data, and you're looking for the truth, that single source of truth, that you can make decisions. If we could achieve that, if a business could achieve that, suddenly those meetings are no longer trying to debate "Okay, what's fact here and what's fiction?" But rather they would spend more time on strategic and operational imperatives to have their business grow. 

So this is something that we as business people, we need to find a way to help an organization exploit, to put them in more of a knowledge generating capacity. And the data is the key that intersection right there of the data. So what makes it unique? What would actually create that data to be much more effective? And the right data, I think comes up with a couple of different elements to it. 

The first is let's go through the socialization. If you look at the socialization, imagine if it were more transparent. So imagine, you know, as you're got something on social media relative to the the election and you went, "Hmm, that's an interesting stat. Is that just cherry picked? Is that something real?" Imagine you could drill down into it to try to understand its origins, where it came from, its relationship. That's what we need to do in business. When you look at a data, you need to go through this exploration so it becomes more believable, has to be technology you can really to enable, but that transparency becomes important. Particularly today when we look at the explosion of the way that we can socialize online. 

Second is when you look at the internalization, there's trust. So you look at those insights, there has to be trust. And when there's trust, really when you trust someone, you believe that they have integrity, there's a moral higher ground with that, personal data is the same thing. We have to be able to trust that we have to see a variety of the elements that create trust for us. And when that happens, we're gonna internalize quicker. Much, much quicker. We're not going to spend the time trying to figure out, okay, is this fact/fiction? I trust the data that I'm looking at.

The third is when we look at the combination, that really means you got to have confidence that I can take these two elements and put them together. So what would do that for you? What would do that for you if there was a methodology that you could see. A method that was consistent, that was put in place that, "ah, I understand how those elements can come together." You know, in those, the business review that we just shared had there been a methodology that the CFO was seeing that you could take her through, she would say, "okay, I understand where it came from. Now I understand that perspective." 

The fourth area is looking at the externalization and as the data and insights are codified, do you believe the quality of the process that has been put in place? And if you do, there's a resiliency, that's where it becomes part of the fabric of the organization. But what's kind of to be in place, sort of make that work is process. Process becomes really, really important. So think about this for a minute. Let's think about this one. If you suddenly had really, really good data and if it could put this in place, it suddenly would have a very big impact in your business, no longer you're going to the meetings and trying to have great discussions relative to here's the set of facts that you really should believe. Instead, you're talking about the key strategic implications. 

Let's talk about four principles that we just went through. Kind of say, we're gonna apply it to the Nonaka model and believe that that's going to accelerate the ability for an organization to create, maintain, distribute knowledge within the business. 

The first is there's gotta be transparency. So to have that on the far right, the requirement, it's just got to be technology. You have to have the right tools in place to create that technology. The impact in the middle, there is speed, the speed at which the organization can make decisions and create knowledge in a much, much better place. So we need to give people that ability in which to experiment and play with data.  I'm the first person to tell you in previously running a business, when I would get data for a lot of years I would spend time going, "okay. I need to make sure that I really can trust this." When suddenly we put the right tools in place, put the right processes in place, put these principles in place. I no longer needed to do that. Like I certainly would explore it understand it's meeting, but you have to have these tools in place to be able to go through the  exploration. 

Second is trust. So there has to be integrity in how you're managing your data. That's data quality techniques. You've got to have the right data, quality techniques that are in place. Got to have the right privacy in place. You've got to look at the data to say, Hey, I'm harnessing the best the organization has. If I have that, then I have conviction. You know, I can feel very, very comfortable that I can make decisions based on what I have here.

The third is confidence and I get confidence through methodology. So a great example is how do you attribute, let's go back to the CFO. How do you attribute the marketing tactic one versus the other? How do you do that? Marketing attribution? It's really hard in business to business, not easy in consumer marketing either, but really hard business to business. But if you had a methodology that was followed and you had the right tools and the right integrity, you'd feel much, much different about it, you'd have the confidence. That gives you a much more comprehensive way of making decisions, because what will happen is you'll be comfortable in making the decision, not simply on one element, but rather multiple elements as a result of which you, which you have put in place.

And finally, resilience. And resilience only comes when you have process, when you've put really, really good process in place. So when that happens, then it becomes language of the organization. And that becomes really, really important. You know, it's, it's, um, always interesting when you watch organizations first put in dashboards, usually they're kind of staring at it going, "well, what do I do with this now?" And a lot of it's because they haven't put a lot of these elements in place yet to the data. And the process becomes really important where people can see, "I understand how it got here. I understand the process, the data, you know, got to this dashboard. So I feel comfortable that I can see the methodology. I see that there's integrity that put in place that I've got the right tools to able to do that exploration." That has a huge dramatic impact and how you can now we think about things. 

Let's look at what the impact potentially could be. If you have the right data, and you have this, this set of principles in place. I think you're going to find that the business will not only thrive, but also you'll see great results. One of the things we did at SeriousDecisions is we measured a lot sales and marketing alignment. So we looked at the different processes that were in place. We looked at the different strategies are in place and how well organizations can align. And they can't align unless there is really good data, and they have these principles associated with them. When they do achieve that, we found those organizations grew 19% faster than their peers, and they had 15% more property than three EBITDA or through gross margins. That's an incredible impact. 

It really is now McKinsey did some work where, what they did is they created this marketing analytics operating model. And they said, okay, there's units of change that that can impact when one unit of change happens. Typically there's a 0.39% increase in profit. Profit, to the bottom line. That what a huge impact millions and millions of dollars that that could make a difference on. Unfortunately, this is not linear. They found that about one and a half percent was the greatest impact that the organization really could make. But think about these two aspects for a minute. 

Now, imagine what would happen in that business review. If you achieve the principles of growth, you're focused on key decisions and the trade-offs those executives might make. You know, in that example, there might find a head of sales say, "Hey, I'm going to give my money to marketing because I think they need it right now, based on what the data's telling me." How many times have you seen that happen in your career? Not too many. You might have a CFO say I'm going to provide you more money based on what you're doing, because I can see you're going to contribute more to the bottom line with what you're doing in marketing. Don't hear that too often either. Right? So suddenly you have a very different set of decision-making that's happening as a result of what's been put in place here. So it's really quite impactful.

And I think what we need to do is to take a step back and I'm gonna ask you to give yourself a little test here. And ask yourself, do you have really good data in place? Is it principled? Do you have the four elements of principle, data, trust, transparency, confidence, and resilience. If you do, you can answer these questions. Does the data have the integrity for you to make effective business decisions? A lot of that comes down to what's the time it takes to suddenly get to that trust. Is that something that you really have in place. 

Second, does your technology allow you to explore and share? Do you have that level of transparency? That becomes really, really important? You know, what's happened in our world of social media. We were we have the internet, we have so much data that we can see, but it's unprincipled data. So you've got to have the right tools to do the exploration, to help you in a way to do that exploit, to do that sharing and transparency has got to be really key. 

Confidence. What is the methodology behind the numbers that are based on, does it just pop out and you're supposed to believe it, or is there a way that you can look at the methodology and say, I can see how it's come here. I can see the impact that it's having. And as a result, you can see that the organization is going to make much more effective decisions.

And finally are your processes becoming the language of the organization? This becomes really important. So if there are, then what you're putting in place is resilient. If you don't have that every month, they're going to have a new metric that somebody has popped up and come up with. So you're not going to have that NCBI metric that I had at Sirius that was quarter of business. Now we'd certainly have lots of other metrics as well, but that becomes really important. So these are what I believe in exploring and thinking about data in both advising thousands of companies, teaching students about the impact of really, really good data doing lots of research. I've come up with these four key elements that if they're in place, it creates really, really good principal data and the organization can make incredible effective decisions. And more importantly, they will create knowledge on an exponential manner. 

I want to thank you for the privilege of your time today. I have a few minutes for some, some questions, and I hope you enjoy the rest of the conference here. And I hope that, um, you stay safe in this unusual time. And I hope the next time we see each other, it will be in person. Thank you.

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