An Interview with Pollinate CTO, Giovanni Salas

Overview

Dr. Giovanni Salas
Dr. Giovanni Salas

The following blog article is the transcript summary from an interview with Pollinate Networks Inc. Co-founder and CTO, Giovanni Salas in June of 2024. In this interview, Giovanni describes the Pollinate Optirithm, what it is, and how his passion for researching the topic of knowledge sharing led him to the desire to develop this type of tool. Read on to discover what the science is behind this tool, why Pollinate Networks uses it to optimize our matches and how and why its use can be so beneficial for so many individuals, associations, and organizations.

Could you provide us a little bit of a background of yourself, your work, your research, your passion, and how this led to the development of the Pollinate Optirithm?

It’s quite relevant these days to talk about people collaborating and people learning from each other, particularly as we have a workforce that is global in nature that works in different cities, different provinces, and different countries. One of the key things for any organization to ask is “How do we collaborate? How do we share knowledge?”. I have always been interested in learning how people acquire knowledge, but more interested in how people share knowledge. I strongly believe that the more we can share knowledge among us, the better we are going to be as a society, and in particular for organizations. The more they can learn among themselves, and their employees, the better the organization is going to be. I went from just thinking and reading about that idea, to actually researching it more fully.

In 2012 I decided to join a program with York University to pursue my PhD. My area of research was about the acquisition of what is called “personal knowledge”, or “tacit knowledge”. This is the knowledge that people gain and acquire through experience and through doing things, not necessarily just reading it, but actually putting it into practice. With the help of other researchers, I extended my studies to include how people learn and understand better. From there I researched how people can share that information, which is a fundamental thing. Humans have been acquiring and sharing knowledge since the beginning of time. A lot of us have been part of mentoring, coaching, and knowledge sharing programs, so it was natural for me to start exploring those topics.

Christy Pettit and Giovanni Salas
Christy Pettit and Giovanni Salas

Pollinate has always embraced the challenge of how we can get people to share knowledge, and how we can help organizations implement a good mentoring program. Along with Christy Pettit, CEO of Pollinate Networks, we were able to develop several of those processes and tools that allowed us to continuously deploy better mentoring programs, which brings us to now. We continue to research all areas of mentoring, and in the last few years have done matching for some large organizations with mentoring programs that have hundreds of potential pairs. We started to ask ourselves how we can make those matches even more effective for companies, and how we can keep improving the way that we think about mentoring and the way that we think about matching pairs, which is how we developed the Pollinate Optirithm.

What is the Pollinate Optirithm? Is it an “optimizing algorithm”?

That’s a good way to understand it. The way that we view matches at Pollinate and the reason that we wanted to look at matching in different ways, goes back to what I said before about working with organizations that have many different possible matches. We’ve all experienced the feeling of wanting to be matched with a specific person, and when we are not, we are left a little bit disappointed. When you have, for example, 10 different pairs that you need to match, there are many combinations of these 10 different pairs. You might try to do this using a spreadsheet or just think them through. You may find some pairs that are very good matches, some other pairs that are OK matches, and then you also may run the risk of some others that are not very good matches. Nevertheless, you can manage these things when the process involves a small number of people. When you start thinking about 50 pairs, or 100 pairs, or with some of our clients, we have over 500 pairs, the possible combinations become into the hundreds of thousands, and maybe into the millions of possible combinations. We needed to start thinking more about how we actually do this process in a way that ensures the matching results are very good for ALL of the pairs, not just for a few. Some people might say “on average, everything is good”. But being on average could mean that a few pairs get excellent matches and a few pairs get very poor matches. On average, it looks OK, but it’s not really what an organization wants in a successful program.

When we started to research more about how we can make the best matches for everyone, we started to introduce scoring algorithms into our process. We worked with a university department of mathematics to help us solve these problems. From this work, we developed two different algorithms. The first algorithm allows us to score the pairs, and the second algorithm allows us to find the best possible match. The second algorithm is what we call the optimizer or the “Optirithm”. What it literally does is take the problem, and through a process of mathematics, solves the equation. It solves this problem of matching pairs, and it finds the best possible matches. We then test the outcome based on what we call the “fairness index”. We are very proud of the results of that process and we have received some incredible feedback from our clients saying how pleased they are with the results of the matches. And in the end, it is more efficient. It is not left to just one person to do those matches, but it’s actually solved by an algorithm.

Is the Pollinate Optirithm completely automated?

It is. If you think about trying to do these matching processes manually (and we know this from some of our clients’ experiences), the process could take weeks to try to find the right combinations and matches. We can actually solve those same problems using our mathematical models in 15-20 minutes which also allows us to do multiple runs until we find the best solution. It’s amazing and quite incredible.

Does it actually find the BEST match for everyone?

Yes, absolutely. Our purpose was not to find just the OK matches for some and good matches for others, but to find great matches for the entire population.

Another thing to remember is that for many of our clients, it is not good enough to just match them in only one area, the area of expertise, for example. There are many other different factors. We recently did a match for a client where they wanted to cross-pollinate members from two previous organizations that merged together to create their current organization. They wanted to make sure to cross-pollinate across regions and also across business units. There were many different factors to consider when we set out to make these matches. It is nearly impossible to do it by hand. It would take you several weeks to try to think about all of those different variables. We were able to do this for the client, and find the best possible matches very quickly.

What types of clients can use this Optirithm? Is it only for large global organizations? Or can we use it with smaller, local organizations?

Any organization that is doing a mentoring program can benefit tremendously from these algorithms. Large organizations are obvious candidates because of the number of the possible combinations that exist, as it could get into millions of possible combinations. Smaller organizations matching 20, 30, or 50 pairs still present quite a number of possible combinations. It will significantly facilitate the process. The more important issue is getting the right mentor with the right mentee and vice versa. This process ensures that all of them get the best possible match. So literally any organization that is doing a mentoring program could benefit from this algorithm for sure.

Are there any other benefits to using the Pollinate Optirithm?

That’s an excellent question, because that brings me back to how I started with my original research. One of the key things that human beings have been doing and practicing for thousands of years is mentoring. This is how we have learned. We learn from our parents and we learn from our siblings. We learn from teachers and we learn from people that mentor us formally and informally. This is not what we are doing, we are not just helping organizations implement a mentoring program. If you reflect on your own experiences, the best lessons that you have learned are from somebody that you trusted, somebody that you care for, and somebody that actually was able to share that knowledge with you. Again, whether it’s a sibling, a parent or a teacher, we all have experienced those types of people in our lives. For us, being able to set up a mentoring program and help them, but also keeping the perspective that matching really matters. You cannot just have someone who knows about sales matched with someone who wants to learn about sales, and assume it’s a good match. There are many different factors that affect the quality of that match, and depending on what the goals are of the organization, there are different factors that can influence the matching. For example, in some organizations, improving workplace culture is very important, so the way in which they wish to approach mentoring and knowledge transfer is very specific. We therefore don’t limit them to just one parameter like a topic or goal. Organizations rely on many different parameters, and with some of our clients, we work with 10 or more different variables. Whether it’s job position or job level, whether it’s language, location, experience, or business unit, all kinds of variables can influence a good match. When we optimize those variables and parameters, that’s when we get the best match. The best match means you have the greatest chance of knowledge getting transferred from one person to the other. That the people involved in this mentoring process benefit from learning together. This is particularly important for the mentee, who gets to learn and gets to have a good mentoring experience, that’s the key. It is not just about putting two people together, it’s about putting the RIGHT two people together, and that’s what we are doing.

Do you have any concluding and/or summarizing wisdom to share?

Many organizations are starting to put a lot of more emphasis on knowledge sharing and mentoring. What we need to start reflecting on is that the more we share, the more we learn together, and the more we learn together, the better we are going to be. I was just reading a report from the Bank of Canada. The Governor of the Bank of Canada speaks about the lack of productivity in the Canadian workforce. How are we going to overcome that? We are falling behind many countries on our productivity measures. The question becomes, “How do we get our employees, or our workforce to be more productive?”. One of the key things is obviously for them to be better prepared. And how do we get to be better prepared? You do it through training, but more importantly, you can do it through mentoring! You have people in these organizations that have a great amount of knowledge. How do these organizations then set up programs to help, especially for the newer workers, to become more productive? You do that through a good mentoring program. I believe this is now more important than ever. We need to put more emphasis on knowledge sharing and on programs like mentoring to help. Think about the difficulties of the young workers coming into a workforce. Sometimes they get to go to the office maybe once or twice a week. Sometimes they have to work remotely all the time. So how are we going to help them? That’s a key element. When I started my career, I had to go to the office all the time. Of course commuting is not an easy thing, but when you’re in the office, you get to learn from others. You get to learn from your boss, you get to learn from your colleagues. Some of the new workers do not have those options these days, because we are in a hybrid environment. So it’s very important for organizations to set up these types of programs to help them acquire the knowledge. This is not just the knowledge about a specific topic, but the knowledge about the organization, how the organization works, the culture of the organization, how to network within the organization. All of those things are very important, and all of those things can be done through a good mentoring program.

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This article is a transcript summary from an interview with Pollinate Networks Inc. Co-founder and CTO, Giovanni Salas in June of 2024. In this interview, Giovanni describes the Pollinate Optirithm, what it is, and how his passion for researching the topic of knowledge sharing led him to the desire to develop this type of tool.

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