From the Trenches to the Boardroom: Making QE Matter to the C-Suite with Parasar Saha

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Today we'll be talking with Parasha all about from the trenches to the boardroom, making QE matter to the C suite. Parasha is the founder and CEO of GG4, Canada's leading AI powered enterprise platform with quality engineering and real time reporting. He has over 23 years of experience in QE leadership across airlines, finance and telecom and he previously led a major IT transformation initiatives in the Canadian airline industry. Really knows his stuff. It's his third time on the show. You don't want to miss it. Check it out. Hey Parasan, welcome back to the Kilt.

Thank you Joe for having me.

Great to have you. It's been a while. So what have you been up to since last time we talked? I know you, you launched a company and you were doing really well. I don't know if you've expanded to anything else, what you've all been up to.

Great question. This is my first of all, this is my third time in your podcast, so I really enjoy this and how I see it is like, you know, what I started with like, you know, in 2020 about Universal Cloud. Then I talked about how I was starting my journey in the industry as an entrepreneur and then now I'm here. So I love to talk about like, you know, recently, like, you know, as an entrepreneur, like, you know, we scaled the company up. We are like, you know, working with several like, you know, global enterprises. One of the thing I do is coming from the industry is to understand what the problem is right in the what is the challenge that the leaders, the practitioners, the mid management is facing and then taking back to that problem statement to my team and see how we can help them. Like, you know, I just have some numbers, you know, like, you know, some people are kind of really surprised by this. Like last 30 days, like, you know, I've been in like, you know, six countries, 11 cities and 21 flights. Right. You know, the reason I did it is to kind of meet people in person, understand what their real challenges in quality and different segment, different domain and then taking it back to my team and taking the conversation forward for a solution here.

Love it. All right, so what's the main theme you've been hearing? Does it depend on the vertical? Is it all the same?

So we do find a pattern and we are Very focused in what we do. Right. You know, we are focused in data visibility, AI. Right. You know, and our AI is a very specific area of the AI. AI has a lot of things and we can deep dive into that. We call it insight AI. So we are very specific in what we do. And the conversations that I had around the globe with different domains is that try to understand the area that we work into, like how that is impacting quality, like you know, which is reporting analytics, why it is important and how this can elevate QE to become just not a kind of a group in the organization, but a place in the boardroom where they can actually make the boardroom and the C people understand the value of quality and the work that we do. Okay, how do we elevate that conversation? So there is a pattern in that a lot of people are struggling in that same space and that's what we kind of like, you know, my focus is right now.

I'm glad you're on the call text. I just had a recent conversation. I don't know if this is normal. I think it is where he has a mandate that they need to use AI, start using, implementing AI. And so this is from the executive team down to their team. They need to start using AI. And so he was looking for ways to write tests quicker and things like, things like that. And we were saying, well if. And we said how about your requirements? He's like, oh, we just based on the code we write test. And we said, you know, you probably aren't going to get really value from it unless you have really good requirements. He's like, ah, management isn't going to be into that because they're going to have to be involved. So is that a common thing you see where businesses don't. Top management doesn't necessarily care other than just write more tests. Like how do you build the knowledge up to let them know it's not just building more tests but building the right test and why it needs to be involved from the beginning and there needs to be some startup investment.

What you said is something that we see in the industry very often because from like a senior management perspective, like you know, if you look at the different Persona, right? QA usually goes like in an organization mostly you will see the highest level is director. VPs are very uncommon. Like you know, there are a few corporations with VPs. SVP's are very uncommon in the quality world. Right. So the people who are giving this direction are beyond qa. Okay, they are beyond qa. But the QA rolls up to them. So in one way their thought process is like, you know, if I get more test, then yes, it's better for us. I'm going to have more coverage. That's not the truth. And with AI coming in, the common understanding in that space, like who are beyond qa, the volume will bring them the quality. It's not that AI can create a volume, but what is more important is that the impactful test. How do you figure out when you're creating tests, which tests are more important? And when you're running those tests, which tests are more valuable for you? Which is finding you more defects or which is going to be like, you know, being the guardrails? That's most important because if you look at it like with AI coming in, you're going to be scaling up from a, let's say 500 test suit organization to a 5,000 test suit organization because you can create n number of with the AI. But again, if you keep running those thing, you're going to end up having at least 10 to 20% failure. So your failure rate has become 500 failure. So what happens is end of the day is like you end up in a kind of a spiral of following up some things and you're not actually making quality faster. The way you can make quality faster is to find what is your impactful test, what test you should be focusing on and what test you'll be running when a change happen in the ecosystem. That impact analysis is a very big part of the thing. And that information you can mostly gather from your data and reporting and the thing you can measure and you can manage things well.

How does someone in the C suite receive that message though? Especially like when you hear that you're like, oh, I'm not a regulated environment, I'm not a healthcare, I'm not an insurance company. Big deal. Is that, do they say big deal or like, how do you like what are the pain points? Is it brand? Your brand could be impacted if this doesn't launch. Right. Or you can lose money. Like, how do you approach the C Suite with like why? It's why they need to be really heavily invested into this.

Yeah. So I'll talk about two things here. Like, you know, when, see I had several conversations with people who are at the ground level doing the thing, which may be practitioner, mid level senior managers or a director. Their talks are about operational data points. Right. Which is how many tests are automated, how many defects you have found. Right. So they are talking about that thing. But when you are A like C level guy, like you know, even me, like you know, when I am actually like you know, running a company of myself, what do I care about? That's what I also think about. Like what do I care about? What keeps me awake in the night? What keeps me awake in the night is not the lower environment. What keeps me awake in the night is my production. What is my customer seeing? Right? So what I really care about is how much disruption I'm having in the production. Right. Am I having this many bugs? How does it impact my customer? Customer retention, customer experience, sentiment analysis, those things I mostly care about right. When I'm at sisu. Because that's very much tied to my revenue and customer. Okay. An industry is driven by customer retention revenue because investors are going to look at that money now as the leaders of quality. If we can like yes, this data is good, like how much we have automated and the stuff. But if you can funnel this data, these KPIs that you are building with your organization and roll it up to okrs, right? Okay. What is helping the business? How you are helping the business, then your data become a gold mine for ASI people because they can see like you know, hey, I did this work and I invested here and this helped me to keep my production safe. This helped me to build my brand value that my customer is not coming to my critical things and failing to operate this, my one business transaction failed. The customer is going away. So if you can show that data and can tell that story, that's a way powerful story for a C people. Rather than just trying to figure out like you know, how many automations and stuff that you, you have to tie those things together. Right. And I can tell you like you know, talking to the C level where these also get challenged sometime is that the data they are getting 56% of the C people fills, right. You know that the data they give receive is out of date data. They can't do anything with the. Because there's so much volume by the time that you actually present the data to a C people that data has become still. So you have to give them real time data, meaningful data and that can make more revenue, more customer retention for them. You have to tell that story.

So how does a test to tell that story?

Yes.

Without Ken, like I just like writing tests. I have thousands of tests and you know, maybe they're not doing the right thing, maybe they think they're doing the right thing, but they're not. Like you said, they're not testing the right things. They have thousands of tests, but not the right test. How do they tie it together, knowing what the KPIs that matter to QE and make sure that tests are tied that way and that they're reporting it up in a timely manner.

It's a very complex thing. Okay. And tell you why 90% of the organizations in the world has gone through digital transformation. Okay. What digital transformation has brought the shift is people move from single digit number of softwares in their software engineering cycle to double digit. Okay? So there are too much of tools and these tools don't talk to each other seamlessly. So what happens is right now you know how much data we have as of 2025, like generating it is 180 gigabyte of data. One gigabyte is like one trillion gigabyte of data. And that was a 50% increase from 2023. Only imagine the volume of data that you are seeing and the sources it is coming from. So when you talk about reporting, it's a very complex thing because you're on site doing this data extraction which is a complex thing, then you have to do this data modeling and the data visualization. You are sitting into a stack of very detailed technical stuff. A lot of times there are challenges to do this right. You know what I would suggest is like, you know, lean into your larger organization reporting platform like how you can use them. If you see there is an opportunity to work with a buy solution that you can do that because it's a very full scale solution. So get to a solution which can get you real time reporting. Okay. And have a conversation with the set of people who are not only in qa. Your main customer is your people who are outside of QA engineering business. What do they want to see? And if you can show them that and how you're helping that are moving the needle. Don't just show them like you know how much automation you ran, how many tests you ran. That doesn't give you anything. That gives you, yes, that gives you power to save. But what they want to see is that how you're saving them from a sorry face to the customer or the investor.

So that's a good point. So I guess you know, with AI it's becoming even more like test, easy to write. And if you don't show this other value that you're talking about, you could be let go. Not because you're not needed, but because you didn't tell the story or bubble up to more, more important things that the C suite really wants.

Yeah, that's exactly. And you know, like, you know, I have had discussions with people that you know they are buying tool. The tools are not like, you know, cheap these days. Like you know, when you go to this AI tool then investing millions of dollars and the organization is holding people accountable, how you are actually using that million dollar that we have spent. Right. Okay. Unless you can prove that you can't go for the next million that you want that. Right. Okay. So they're facing that struggle how to get the funding also. Right. And it's. And the data is the only way you can actually tell that story to the management. What the, like you know, writing the checks for you. That. Okay. How you're getting that. Right. So it is something like, you know, if you can tell that story that helps you to get more funding, more visibility and you can be sitting in the boardroom actually like you know, giving quality and giving your team the visibility and the right, the kind of like, you know, the significance that they deserve as a leader. You have to make it visible.

Power SLOT So in the age, there's no shortage right now of AI tools with the modern pipelines and everything. So why are people still struggling with data? Isn't this what machine learning was created for, for being able to analyze a lot of data? So why wasn't this the first thing that was solved? Why is it still an issue?

Yeah, so machine learning is there. So machine learning can process a huge volume of data, but getting everything integrated and if you think about it like in a large organization, let's say a bank or a telco, right. Okay. Now they have more than 20, 25 pod teams like which are agile teams. They use like you know, more than 10, 15 tools in their ecosystem. Even like for same purpose, they may be using multiple tools. So this comes from fragmentation, like the fragmentation that you have in terms of tool. So your data is coming from multiple sources. Now if you show, hey my Cypress data is this Selenium data is this API data is that's not what the people want to see. What they want to see how much of the coverage is done, how much of your like things are protected defects you're finding, how many defects you're finding from your testing, those are the things, right? That fragmentation, you have to keep it behind the scene. So that part has to be engineered in and that engineering, that integration is not done in the industry that much. And most of the time organizations are falling back to their in house capabilities to build that engineering which is taking them time. It's not scalable and not working. So that's one of the reason that fragmentation and then again because you are living in an agile world, every product team have their own ways of doing the stuff. Many organization, they don't kind of dictate the terms that you have to do this, they just let the teams to do that. So what happens is even in the same way they kind of their data is in different format. So but you need a system who can actually build it together and feed the thing and then you can use AI to do pattern matching and see what the problem is and give it to your management to see where it is.

All right, so how do they do that? I mean, you mentioned inside AI. Is this something that could help with this type of fragmentation? Is that where we're heading inside AI?

Like you know, so the first part, how do I do it? Like, you know, had a platform like you know, over the years, like you know, over five years. And like, you know, talking to industry, we built out something called. One of our flagship product is Digi Dashboard. Right? Digi Dashboard. What we do is we mimic like, you know, if you remember my, like the conversation, I always say that, you know, look at the broader ecosystem, not only qa. Right. You know, in the broader ecosystem there's a concept called integration hub. Like the salesforce, everybody has this integration hub where you integrate everything. So we build that integration hub no matter what tool we can integrate into one. Then a universal data pipeline which can talk to different type of tool irrespective of what type it is the thing. Then you kind of consolidate the data and then kind of like, you know, feed it to the AI piece right now. So that's the thing. Like, you know, we build a whole platform which can help towards that piece. And again there are other ways also to do it. You can do it like, you know, thing, but it will take you a lot of time if you do with power bi. So that's one of the things. And using this data to tell the story is the most important part. You have to know what you are because we find this gap lot of time even the C people doesn't know what to kind of like, you know, ask for. Now this is where the community factor is also going to be important. Like if you look at the DevOps, they have something called DORA metrics. But in QA we didn't set the path what are the proper metrics that we should be doing. So every organization is trying to build their own metrics. So it's a time when we should be coming together and talking about what is the measurement that we can give from qa and that will set the golden standards that will help lot of conversations to make easier and expectations will be much clearer in the path.

So what are they? Is that something that's built in the tool? It gives you a common set of key metrics you've seen have been helpful over the five years or so you've worked with all these different companies?

Yes, we did that with the companies we worked with. Right. You know, we see a pattern. We see that they are looking for like, you know, escape metrics. They're looking for like, you know, release, like, you know, the release trends. Right. Okay. We see that and they look at rework metrics. So there is a set of metrics they usually look for. So we worked it out like, you know, because we are heavily focused in this area, we try to figure out what the industry work, what is the pattern that we are seeing and we are taking this conversation, but this conversation has to grow as a community conversation as well. And that's why I also have this community forum called the cqta, you know, the Canadian Quality and Testing Association. Right. So as a whole community, we have to agree that how you measure qa, once you set that standard, it's much easier for leaders, practitioners and everybody is driving towards one goal. End of the day, organizations goals are pretty similar and quality goals are similar. So we have to align and figure out what is that dora matrix for us for QA that we can champion that thing.

Love it. Let's say someone had the key metrics that they're in agreement with that they're going to use. They have a hub in place to correlate all this data you mentioned one of the issues is having the data fresh and on time. How do you not. Are there any blind spots to creating a dashboard or alerts that make sure that your management is getting alerted on the right data in a timely fashion and not getting false data? I don't know if that makes sense. A lot of times it might be over inundated. How do you know, like what's the right level of information to give them and what the time the most timely manner is?

Yeah, and this is something we. It's a very good question because through our journey we have seen this. That data can be sometimes overwhelming because you're getting too much data, too many metrics. So the way to build it is Persona based metrics. Right. Okay. So every Persona has a specific need. If you talk about like, you know, escape metrics to like you know, or revenue metrics to an engineer. That may not be that meaningful for them for they are looking for some engineering metrics. Right. But for different people it's different. So how do you stack it up is for an engineering metrics you have something for a mid management or a project management level you have a set of metrics and then another set of metrics for the executive. So your dashboard should grow with you and whichever level you are, you should have a separate set of things which talks to their need.

Absolutely. How do you get the setup? Like how do you get a seat at the table though with your CFOs and CEOs like as part of a process, you're not just an afterthought, you're testing software. They don't care. How do you become part of the whole process where they know about you and you're reporting to them like quarterly or weekly or whatever. How does, how do you see that work?

Before I transition into entrepreneur, I have worked with a lot of salespeople. Try to understand because I mean when you're building a business, sales is a very important part of it. I always observed how they are actually influencing the key business people like or the leaders of the organization to achieve what they want to achieve. Right. And that is by real time data, right? Okay. If you see they give a very real time data with Salesforce and thing, right? Okay. On one side it is pulling you like you know your sales data, one side it pulls you your revenue, your things. It's a very connected ecosystem. And imagine you are sitting into like, you know, your SVP and who is also facing a sales guy and he opens up like you know, a fancy Salesforce dashboard and you open up like an Excel, right? You know, where do you stand, right? You know, you have your data like you got it probably three weeks back or that guy got the data like you know, real time and feeding into from every system, their GL ledgers and everything. So that's where I think it's very important to kind of get that every system connected and you have to make sure that data is coming from the ground. But if your data is kind of like, you know, some handcrafted data, then what happens is the trust on that data Is low and 45% of the C people don't trust the data if it's manually kind of like curated and it goes into a different. So it has to be ground data, it has to be real time data and it is need to be a simplified matrix that talks to the need of what the people want on the table, right? And that's where it has to talk to your revenue. It has to talk to customer and how you are helping retain revenue or gain revenue. If you cannot take tell that story, that boardroom is not going to listen to you.

Love it. So if someone's listening to this and they're a QE or QE or whatever or a tester, how do they know where to start? Because you said you know, how do you know what you don't know within your organization? So to get make sure you are collecting the right, right data, especially the monetary data to make it like the big picture to be seen. You're not missing segments here or there and slices that add up once you have them all in the proper place.

The process is simple, right? You got to talk to people outside of QA as well. Ask them how can I help? When you ask that question, the person will tell, okay, this is how you can help. What, how do you measure that? Okay, what I'm giving you is helpful for you. If you have that conversation, it will be helpful. If you only talk to insight qa you won't ever get the solution. That's what I always think about like you know, if you see my travel patterns, like even my travels, right. I'm going to like, you know 30% of my travels into QA conferences and I going rest of the conferences are non QA conferences. I'm trying to understand how they are doing, what the value qa. I was in a converse like you know a conference yesterday. It's a CX conference which is like you know, Contact center conference of AWS and people were talking about so much changes happening in Contact center in with AI coming in and how QA can help. When you kind of sit into this conversation then you understand what are they looking for. You can bake into that lot of time that is what we are missing. We are very inward looking organizations. We are beginning now how we open up our shell and we open to people what they are looking for, right? And even I would want to see in our QA conferences how do we bring in C level guys and tell them what are they looking for? Not just kind of like yeah this is my AI use cases but what are they looking for? The use cases that can help. So that conversation, that bridge will help us to reach a wider audience and make QA valuable to organizations.

Great advice. Okay Parasar, before we go, is there one piece of actual advice you can give to someone to help them with their QE automation efforts and what's the best way to find or contact you?

Actually you know what like I will quote okay what what I heard yesterday from one of the leaders and it actually sticking to me even now. Right okay. Measure to manage. You should be able to measure yourself. If you cannot measure you cannot manage yourself. And this is the exact word another leader said in yesterday's conference. I was so I want to kind of like you know like mention that and if you want to reach me I'm like you just google me. You will find me. I'm on LinkedIn. Like you know, feel free to send me a DM. I will be responding to you and my email parasha Shahaddg4.com and we'll have.

Links to all this in the comments down below. Thanks again for your automation awesomeness. For links to everything of value we covered in this episode, head on over to test guild.com a553 and if the show has helped you in any way, why not rate it and review it in itunes? Reviews really help in the rankings of the show and I read each and every one of them. So that's it for this episode of the Test Guild Automation Podcast, Joe and my mission is to help you succeed with creating end to end full stack automation awesomeness. As always, test everything and keep the good. Cheers. Hey, thank you for tuning in. It's incredible to connect with close to 400,000 followers across the across all our platforms and over 40,000 email subscribers who are at the forefront of automation testing and DevOps. If you haven't yet, join our vibrant community@test guild.com where you become part of our elite circle driving innovation, software testing and automation. And if you're a tool provider or have a service looking to empower our guild with solutions that elevate skills and tackle real world challenges. We're excited to Collaborate visit test guild.info to explore how we can create transformative experiences together. Let's push the boundaries of what we can achieve.

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