Nicole Radziwill  0:00 
Well, you know, it's interesting even among the companies that do invest and things like building data catalogs and having data dictionaries, 99 times out of 100, you open them up, and they make no sense. Unless you're embedded in the data, it's impossible to decipher what those things mean. So one of the things that some of the teams that we've run have tried to educate clients on is

Harsh Thakkar  0:25 
[Podcast intro message] All right, we're live. Thank you, Nicole, for joining the show. Really appreciate it.

Nicole Radziwill  0:46 
Yeah, thank you too. Good to be here.

Harsh Thakkar  0:48 
So I have just been digging up the stuff that you've been up to, you are a senior vice president and a data scientist at Ultranauts. It's a professional services firm helping out with data quality, data engineering, quality, engineering and accessibility. In the past, you've been involved in the ASQ American Society of quality community as editor in chief and also doing work in their software quality, professional division. You've been a professor in data science, you are an author, you've written a book about digital transformation and quality 4.0. And I just have to ask you to start this off the right way. Why? Why are you doing all of these things? What's what's the underlying motivation behind it?

Nicole Radziwill  1:34 
So one of the things that was really important to me even early in my career, and I think this had to do with one of my first jobs out of college, my office mate was about 10 years older than me, and he was a member of ASQ. And so he he was was embedding quality and principles and practices into everything he did. And I really looked up to him. And one of the things that was important to him was capturing the records or the evidence, things that you did things that you learned so that you'd be able to use them later. Right? The one of the principles was, it's great to experience something, it's great to learn something, it's even greater to leave some breadcrumbs behind. For the next person who has the same issue. And whether you're early in your career, or whether you've been doing your job for 20 30 years, it doesn't matter. Everybody has something to contribute every everybody has something to reveal to people who are learning the thing that you just learned. So I got in the habit of leaving breadcrumbs behind. So you know, you go through a project, you learn some new lessons. And so you record it and leave it somewhere where other people can find it. As I'm getting older, I'm finding that I forget a lot more stuff. There's just there's there's so much stuff in your head, the older that you get, you just you forget things. So I'm so thankful to my past self, for getting into this habit of leaving breadcrumbs behind, because now I use my own breadcrumbs. And if I have a task to do, I say, I think I've done that before. And I go look up, you know, my past self telling other people how to do it. And it turns out, I'm now the other people. So it looks like a lot of effort, a lot of activity, a lot of engagement. But really, it's just a habit, a practice that I developed very early on, that started out as an intention, and has become helpful to other people and me. So

Harsh Thakkar  3:30 
Yeah, talking about being helpful, I have just learned so much. Going through your book, reading your blog, trying to just understand your perspective on quality management and data driven quality management. So I'm, I'm really thankful, please do not stop the habit of sharing, because I'm learning a lot. And talking about the stuff that you've written. One of the articles that I was reading on your blog, the title was where as quality management headed, where you talked about the emergence of enabling technologies like AI, machine learning, virtual reality and augmented reality, and also the role of quality managers and leaders to sort of understand these technologies and implement them within the organization. So how can somebody who's a quality leader or working in the quality management space, what can they do to define where quality management should be going in their companies?

Nicole Radziwill  4:27 
So what I would encourage everybody to do is essentially the main message of the book I wrote in 2020. And that's to focus on three things. Number one is increasing connectedness. That's connectedness, not just between systems, but between people and systems and between people and objects and between people and in data sources. It's just increasing the connectedness between all of those entities that make up your organization. The second thing that I encourage people to focus on is adding intelligence. You know, right now Oh, there's so much interest in AI and machine learning that people jump to, oh, we need AI? And the answer is you might not it just look critically on how you can capture and reuse the information in people's heads, create good systems of documentation, create good systems of knowledge management through your organization. Those are also ways of adding intelligence, add some decision support steps to your processes, there's so many different ways to increase the intelligence of your organization using those connections. The third thing that I encourage organizations to do is consider automation. So one of the things that I've noticed, particularly over the past 10 years, is an increase in interest in digitization. So 10 years ago, it was still pretty common to see a default among quality managers of oh, well, we have our we have our warehouse floor, let's just have clipboards and pencils and gather our data that way. And so you have to plan for for the data collection, and storage step. Now I'm seeing organizations default more to digital first. So you're seeing them say, let's invest in tablets, let's just get things digitized immediately. So that's a very critical step in automation. Because as you're automating processes, you want to make sure that you have the information available so that people can follow what's going on as the steps of the process are happening. So those would be the three things that I would recommend to quality managers. Think about the connectedness, think about adding the intelligence, think about ways to automate. And when you do those all together, you're really going to start to see synergistic effects. And synergy is kind of an overused buzzword. But you know, those three things build off of each other.

Harsh Thakkar  6:48 
Great. And from from the work that you've been doing, whether it's researching to write the book or working with any clients, do you have any use cases or case studies where you can talk about a company like the before and after before the old ways of quality management, the after is like you explained, connectedness, intelligence on automation? And can you give us a use case or a case study that you've come across?

Nicole Radziwill  7:13 
Yeah, sure. So there's a few different areas that you can apply connectedness and intelligence and automation to one of them is in reinventing your business model, or creating smart products and services. So one of the best and earliest examples that I've seen, you know, it's 10-12 years later. So we can definitely consider this a success story is from a company called Diversey. So the company used to be Sealed Air. Now, their Diversey, I think there was a merger and acquisition anyway, that the business of this company was selling chemicals to clean floors, so their customers or warehouses, warehouses need clean floors, and they need to stay on the schedule to because there's there's regulations for a lot of industries, like think hospitals, they need clean floors. So this company, wanted to find ways to sell more of their cleaning chemicals. But they revisited the idea of their core business model. And they said, now that we have systems for data management, now that we have systems to connect the people to the data to our objects out in the field, there are fleets of industrial robots that do the cleaning, maybe there's a different way, maybe there's a way to apply connectedness and intelligence and automation to reinvent our business model. So what they did was really interesting, they said, maybe we're not just a chemical company, maybe we're a data company, maybe what the clients are most interested in, is making sure that they reorder the chemicals at the right times, maybe what the company is most interested in is making sure that they have records of when the cleaning was done, or, you know, assessments of how well it worked. And so they shifted their entire business model to be the data management from the fleets of industrial robots, they started renting out those fleets. They were the data management service. And it turns out that as a result of reinventing the business model using these quality 4.0 techniques, they sold more of their chemicals, which was kind of what they intended to do, because now the customers aren't forgetting to reorder. It's just all done automatically. The feedback loops are tightened, and everyone benefits innovator benefits, customers benefit, the adhering to regulations benefit, the auditors are going to have a nicer job because they just look at the records now. So I think that's a fantastic case study. And like I said, it's old enough now where it can really be considered a success story.

Harsh Thakkar  9:45 
Yeah. And I agree with you because any company that's operating today, whether it's in a regulated or non regulated space, I can't imagine them not using any sort of cloud systems or technologies. So there's a small component of every company that is a data company, whether you know, they put themselves out there as that or not every company, yeah, every company is relying on data. And obviously, I've seen a lot of terms being out in the industry like data driven quality management, or data driven decision making. So I want to ask you, as an expert in this space, what is your definition of data driven quality management or just being data driven?

Nicole Radziwill  10:29 
Yeah, this is a fun one. So I get asked the question a lot, how do we become a data driven enterprise? And so the answer is always the same. It's evidence, it's shifting your value system, to teach everyone what evidence looks like, where to find it, how to evaluate the efficacy of it, how to interpret it, it's all about the evidence. One of the things that you notice when you go into organizations is that sometimes they believe in the data, but they don't hold it to the standards of evidence. And so all you have to do is well, I say, all you have to do, it's complex, it's people learning, it's developing competencies. But if if you if you reinforce that value of hey, let's go look at the evidence. Where do we store the evidence? Do we believe the evidence what that allows you to do over time is balanced the really tricky dynamic of of trusting your colleagues, which you know, you need to have have healthy functioning organizations, and holding everyone accountable to standards? So that that'd be my recommendation? What's my definition of data driven quality management? It's it's highly valuing the evidence, making sure that everyone has the literacy that's required, and the the concern about making sure that all decisions are driven by that evidence, and that they dig down to find out why are we using this information to make this decision? So evidence? That's number one.

Harsh Thakkar  12:02 
That's interesting, because as you're saying that the thing that came to my mind was, as a quality professional, is metrics, right? And I know that you've shared this on your blog, or in your book somewhere that a lot of times quality professionals are immune to bad metrics, or they're biased to the data that they're they're reviewing, and they end up making decisions without the full picture. So any word of advice of how to break that habit? Or how to how to look at good data versus bad data or good metric versus bad metric?

Nicole Radziwill  12:38 
Yeah, ultimately, I think metrics are emotional. Yeah, there's there's emotions and how they're set up, there's emotions and how they're used. And we need to apply the same critical thinking that we do to things like root cause analysis, as we're looking at our metrics. Here's, here's one example, from an organization I worked at a while back. Customer satisfaction is kind of important, right? I think you agree that most companies want to be able to evaluate, hey, are our customer satisfied? But what I was noticing after a while, was that different groups were acting differently, right? So we dug into the metrics, like how are we measuring customer satisfaction? What ended up being revealed was that there were six different ways to measure it. They were all using different data. Some we're not actually using data, but we're using opinions from the wrong stakeholders, from people who signed contracts, not people who were actually involved with the product or service. And once the light once once there was a light to shine on, you know, what's, how did how did all these metrics come to be? Who initiated them? Where did they come from? It's just you know, that that whole, that whole experience of exploring how did this metric come to be? You know, who wanted us to use it? Why did they want us to use it, that exposing those topics? It made it really, really easy for people to say, hold it, we really need to know where all of our metrics are coming, because we've been making really key decisions, you know, top level decisions that drive revenue. And we've been making mistakes, because we didn't look at this. This relates to the question of evidence, right? We didn't look down to why are we measuring this thing? Do we know how we're measuring it? Does it make sense to us? Is it backed up? A second example, and this is one that you see a lot in performance reviews, right? There's all kinds of opinions good bad about performance reviews, but one of the things that we don't tend to do is it triangulate around performance as some companies are introducing 360 degree if evaluations, but every single observation, every single assessment about a person's performance is context based. It's situational. And in order to know whether that's true or false, to what degree, you have to look at that same thing from multiple perspectives, multiple people, multiple eyes, multiple ways of knowing. And so we need to encourage each other to look for those multiple methods. So that would be my advice for for how to break the habit of accepting bad metrics, just, you know, be more critical, ask more questions, hold your metrics to the same standards that you want your your business held to.

Harsh Thakkar  15:41 
Yeah, and as you're talking about this, it reminded me of another quote that I had read in your book about bad data, and it said, the quote was bad data is like cancer, it can weaken and kill your organization. And that was a really hard hitting one for me just do, I had to stop reading that and just reflect on it because it's, I started thinking about all the times where I had come across something, a dataset that wasn't completely accurate, or made a decision, not knowing exactly what I was looking at. So it's really, really nice to hear from you of how companies and quality professionals can, you know, really start to look at their data and, and understand where it's coming from. And also understand if it's, it's meeting their end goal of delivering a process or whatever they're trying to do.

Nicole Radziwill  16:37 
Yeah, that particular quote is from Rupa Mahanti. She, she wrote a fantastic book on data quality, it's it's five or 600 pages, it's like a textbook, it's it's the Bible of data quality. And although that goes into a lot of technical details, she really does underscore the fact that that you can't just let your data be when you think about the causes and consequences of bad data. Number one is entropy, right? Data left alone is going to degrade over time, even if you don't do anything to it, it's going to degrade because its relevance is going to change, it's going to degrade because people who know it and understand it are going to move to different roles, or maybe out of the company. So entropy is always a consideration. If we do nothing, we're actually doing something bad.

Harsh Thakkar  17:31 
Right? Because if the person or whoever has knowledge about the data, even if it's if the data is in that person's brain, or in their email, or wherever it is, and the person's gone, or just the expertise of looking at a data and analyzing could also be a skill that a person has. And if that person leaves the company, now you don't have that skill. So it's, it's, for me, that has been something that I've heard from a lot of other people in the industry. And it's one of the main reasons why I always thought about working in quality management, to, to come up with some sort of using these technologies that you talked about artificial intelligence machine learning, to have like a data lake or a data dictionary of all the key objects that are important to that company so that if a person leaves or the company gets acquired, or whatever happens, you can just look at those data elements and understand what are the key processes in that company and how that company works? I have yet to see that within Life Sciences. But I'm optimistic that that's going to happen in the coming five or ten years.

Nicole Radziwill  18:46 
Well, you know, it's interesting, even among the companies that do invest and things like building data catalogs and having data dictionaries, 99 times out of 100, you open them up, and they make no sense. Unless you're embedded in the data, it's impossible to decipher what those things mean. So one of the things that some of the teams that we've run have tried to educate clients on is making those data dictionaries and data catalogs interpretable. You know, again, I use the future self test, right? If you imagine, you know, you know what this data is today. But imagine that you go to another job, and then you come back to this job five years later, you're not gonna remember anything about the details of the data. So have you left and artifact where the time between when you start looking at it? And when you understand what's going on? Like, is that time short? Or is it really long, but those are questions that that we need to ask of each other. I think in this this goes back to causes and consequences of bad data. There's two diseases, if you will, in most companies that that have been exposed to one of them is the addiction to speed because yeah, We need to get features out there, yeah, we need to acquire new customers. But we also need to balance that need for speed with the need to make sure the foundation isn't completely crumbly as we get there. The other thing is the illusion of progress. So this kind of ties in the metrics issue as well, it's people want to demonstrate that they're adding value, that they're doing good things, that they're helping the organization move forward, unfortunately, because of that deep desire, and that can that can cause you to show metrics that maybe show you're putting the effort in, but you're not really getting to where you need to get. And so unfortunately, those are those are organizational diseases, there's a constant, they're a consequence of what's rewarded from organization to organization and industry to industry. And, you know, that's another thing that we have to become aware of, and shine a light on so that we can get beyond it. That's a tough one.

Harsh Thakkar  21:00 
The next question I was going to ask you, I'm not, I'm not going to ask, but I was going to ask you about, you know, technical documentation being boring and just very hard to read. I've seen examples of configuration specs and documents from software vendors. So I wanted to ask you if you have any ideas of how to make them better with using AI or something. So,

Nicole Radziwill  21:22 
You know, I think we can use our own human intelligence, right, just apply the test what's going to hold my interest if I had to read this, look at it. That one of the things that I think I wrote about on my blog, I call it the Discovery Channel test. So there's a little bit of a story to this. So I've got a I've got a backtrack and tell you the story. They used to be that maybe it's still there, there used to be this show on one of the cable channels called City confidential and city confidential was an hour long, fascinating show was always about a murder mystery. But the first half hour of the show they spent getting you into the town, you know, here's this great town. And you know, here's the history, the people who settled there, and these great dramas that happened in the town, and about the half hour mark, the the there would always be, it's the last place you'd ever expect a murder. And I'll tell you what, every single time I watched that show, it was just so enrapturing hearing the history of these really small, on the surface boring towns, right? You just got so into it, you forgot you were there to watch a murder mystery. And it was it was great. And so I thought to myself, I want anything that I produce, or you know, actually anything that I have to engage with, I would love to be entertained at the same way I'm entertained by that, like they really take that particular show really takes care in making something that might be boring, actually interesting. And you lose sense of time. And you just get into it and you learn new things. So I ask a lot of our people, I say, you know, apply the Discovery Channel test. If you're writing this description of your solution or your roadmap, make it interesting, like is this going to be something that like people are going to be in meetings, listening to you talk about this? Are they going to fall asleep? Don't you care enough about them to maybe help them not fall asleep? It's really that simple. There was this great book written about 20 years ago, I think it was written by Scott Ambler. It's called the Agile modeling. One of the great things that they reinforced in that book is documentation can be agile to the purpose of documentation is that you communicate a message and that the receiver can get it in as quick time as possible, because communication is costly, even easy communication. It costs right. So the what he recommends in that book is, and this was it was written during the days of Visio. Do you remember Visio? Yep. So one of the things I remember about working in offices, when Visio was really popular, is that you'd have armies of business analysts coming up with a diagram, you go to the you go to the whiteboard, you come up with the diagram, and then they'd spend like a week or two weeks making it perfect and beautiful, in Visio. And I remember thinking, Gosh, what a waste of time, right? I mean, I know you want it to look perfect, but the Scott Ambler in the Agile modeling book says, Once you figure out what you're trying to communicate, just draw it nice on the whiteboard. You know, everybody carries around these fantastic devices now just take a picture and send the picture the people on your team and you've just saved that week or two weeks of Visio time. I know people don't use Visio anymore, but you know, it's the it's the same concept. Yeah, just you know, make it the what what do you want the person who's reading this or needing this? What do you want them to get out of it? Make it the shortest path possible to let them get what they need out of it? Because you care about their  time and you don't want to overburden them with the cost of having to understand you. So that's, that's my, that's my position statement and what I value and I will stick to it.

Harsh Thakkar  25:11 
Yeah, I can relate to that. Because I was a business analyst in early my career. And I worked couple two years as a consultant in business analysis. And I've drawn a lot of Visio diagrams back then, I came across this concept, when I was reading your book about you started, as you started to create like a value proposition for quality 4.0, you walked through different examples of how quality has evolved starting from quality by inspection than quality by design? And what you're calling like, the current era or the coming few years in the quality space? Is quality by discovery. Can you elaborate a little bit on that concept for our listeners?

Nicole Radziwill  25:56 
Absolutely. So if you think about how quality has evolved, right quality by inspection was, we know what bad looks like, and we're going to apply some tests and throw the bad stuff out, put them in the scrap pile or rework them, then we moved into quality by design. So it was like, hmm, you know, maybe if our process is producing a ton of bad stuff, maybe there's something we can do to our process to produce less bad stuff. So that was great for a while, then, you know, by the time that the 80s rolled around, the the thought was, well, it's great if we design quality in the process, but if people aren't doing what they need to do to support the process, we all lose. So that's when you had the TQM revolution, you know, people, process, technology, let's make sure that they these things can all work together effectively to achieve the outcome. So what's changed since then, since the 90s, is our ability to access information, right? Like now the internet is like electricity. We can get it. I think it's funny, because do you remember, like when you were a kid, and you'd be sitting around people like, Hmm, I wonder how tall a camel is? And you'd be like, gosh, yeah, I guess we'll never know. You remember that feeling? Yeah. Well, now we don't have to have that feeling. Right. Yep. So now we have access to all this information at any point in time. And we can go discover what good means by crowdsourcing from Google. Right? We're also seeing more AI tools, right? I mean, you've probably you've probably played with ChatGPT, which, you know, despite us accuracy issues, it's great for pulling some things together, that you can then process and evaluate and turn into something that is accurate. So what my contention is, is that the availability of information, and the availability of new tools like this will help us discover what good means more quickly, and more tailored to our specific organizations. We don't know how to do that yet. But think about all the time that you spend when you're developing quality management systems, figuring out what really is good. And how do we know if this is good? And how do we make sure that each step along the way we're doing the thing that will get us to good? That's difficult, that's hard. So what if you could leverage collective intelligence and tools to get there more quickly? I think that's where quality is going. That's that's going to be the next next era.

Harsh Thakkar  28:27 
Yeah, I agree with that. And I think it's the reason why it's so important to get there is because from the way I see it, the data is already there, right? So if you look at Google, Google ranks, different articles, different sites, based on how they're indexed based on the content, you put in a query, it says, Hey, you're looking for how to do x, here's five articles on how to do X, right? So the data is already there. Google has the data because people are creating tons of content ChatGPT, or something like that will get the data, because it's already fed in and trained that model to do that. So we look at a company and their quality data, the data is already there. It just needs to be fed or put into this model. So that end user can come in and type a query or a prompt to say, show me metrics in training or show me this here. And the capability of technology is already there to quickly analyze that data and say, here's what you need to know. Here's your answer, rather than Yeah. Yeah. So I'm excited.

Nicole Radziwill  29:39 
It's still up to you to make sure it's the right thing and to make sure it applies. But like I tell people, it is a heck of a lot easier to edit than it is to write from scratch. So a heck of a lot easier to crowdsource the collective ideas of history to figure out how you determine what good means so that you can build your QMS around it a heck of a lot easier. We're to look at past examples than it is to invent them from scratch.

Harsh Thakkar  30:04 
Agreed, agreed. At what are you working on? Now? Any interesting projects or stuff that you got in 2023?

Nicole Radziwill  30:11 
Yeah, absolutely. So the first interesting thing that I'm working on is that over the past couple of years, more than more than 12 people at this point have said, they've asked, Do I have a course an online course available? Because they start, they start reading my 2020 book, and they're like, this is great, but I wish that it could be delivered to me in an hour. So I don't have to read the whole thing. So the two questions I get are, do you have a course that will help me internalize this more quickly? Or do you have an audio book, unfortunately, ASQ hasn't published an audio books, and I don't have the rights to that. But I can create a course. So I've got one that should be coming out at the end of March. That's really exciting, because I think it's gonna help people grasp the messages and be able to apply them in their own environments a lot more quickly. Second exciting project that I have going on this is this is part of the work at Ultranauts, the professional services firm, that I'm part of that you mentioned earlier. And I'm gonna start this one out by asking you a question. So do you think it's good to have an inclusive work environment?

Harsh Thakkar  31:17 
Absolutely. Yeah.

Nicole Radziwill  31:18 
How easy is it to get one of those?

Harsh Thakkar  31:21 
Very difficult.

Nicole Radziwill  31:22 
Very very difficult. And one of the reasons why is that a lot of the advice in the space are things like we're gonna make sure that everyone has a voice. We're going to make sure everyone's heard that's, that's great, right? Like, we do need to do that. But all of those intentions stop short at will exactly. How are we going to do that? How do we know if we're doing it? And how do we adjust if we're not doing it in ways that support performance? Right. So it's the Plan, Do Check Act cycle around creating inclusive environments and continually improving, it's missing, right, because we just don't have enough actionable guidance. So Ultranauts has been thinking about this and working on inclusive environments for 10 years. And when I joined three years ago, we started to ask the question, are there ways that we can take all these lessons learned and make inclusion more actionable? And so we, we've done a pretty big research activity, there have been lots of focus groups that have been gathering, you know, Voice of the Customer, from people in marginalized groups. Long story short, we are going to be helping other organizations become inclusive enterprises, you know, we've developed a data collection and analysis system that will make it really easy for individuals to say, How am I contributing to creating an inclusive environment, and for teams to set up that Plan, Do Check Act cycle. So I'm really excited about that. Because you know, it like, like, we were saying, inclusion is hard. And if we can make it easier for each other, then we might be able to benefit from the collective intelligence of people a little more effectively. And also, you know, lead to happier employees, which, you know, there's there's lots of business outcomes that are associated with them. The last thing is, I'm still working on more books, the next book that's going to be coming out, either at the end of this year, or maybe the beginning of next year, is all about data management, and the intersection between quality management and data management, from a people perspective. So that's exciting to work on to. Those are my three things.

Harsh Thakkar  33:34 
Yeah, that's great. It's interesting when you mentioned about your new book, data management from a people perspective, because there's two things that you really need, if you're going to quality four 4.0 or trying to go towards digital transformation, you need to have a good engaged culture of professionals in your company, who are engaged with this quality 4.0 mindset. So data is already there, you need to clean that data, you need to present it to a model in such a way that you can make decisions. But this this mindset shift that people need to have to look at every process and say, How can I make this more connected? How can I make this manual step automated, just having that thinking is the first step. It's not like everyone's looking for, hey, how to like you can read this book. But from from what I understand is your book here is not it's not a prescription. It's it's that initial mindset of thinking, looking at your own processes, because this book can be applied to any industry, you know, life sciences, food, what have you. So for me, that part is really important for people to understand. Well, how do I change thinking before I go, Yeah, exactly.

Nicole Radziwill  34:55 
Because you know, where, where do initiatives fail? Where did digital transformations fail? It's because you aren't laser focused around the quality and performance goals you're trying to achieve, or that you haven't really dug down into the underpinnings of connectedness, the the connectedness aspect. You know, when I, when I was doing the research for that book, I knew it was important, but as time has gone on, it is the most important aspect. And these are challenges that we've had for a long time. You know, one of my favorite papers is by a researcher named Lynn Marcus, and it was written in 1983. And the question was, why is it that when we build software for people, it's actually software that will make their lives easier? It takes them so long to grab onto it? And sometimes they reject it, you know, why aren't they eating their vegetables? And the answer boils down to change is difficult. Interacting with people is difficult change management is super important. And so even though we're entering a really highly technological state of our profession, and quality management, gosh, those those foundational first principles are even more important than they've been up to date. I mean, it's it's exciting in the sense that, you know, all the things that we've learned still apply. It's a little daunting thinking just how much more those basic concepts, those foundational principles are going to impact success.

Harsh Thakkar  36:22 
Yeah. Listen, thank you so much. I've just been learning so much from your blogs, your books, your content, just talking to you having this conversation. And it's like, I'm just getting ideas, I'm trying to think of how I can be a better consultant, you know, at Qualtivate and work with clients, and start thinking, you know, forward thinking as to how quality management is going to evolve. So I really appreciate you sharing everything in all formats that you're doing for the community and helping us learn how to how quality management is going to evolve. So thank you for that.

Nicole Radziwill  36:59 
Yeah, absolutely.

Harsh Thakkar  37:00 
Where can where can listeners find you or connect with you, after the show?

Nicole Radziwill  37:05 
So the best place to find me is on LinkedIn. So if you're part of my professional community, and if you're, you're listening to this Podcast, or if you're watching any of the recordings, then you are a part of my professional community. And I've met so many wonderful people and done so much fantastic information sharing, please friend me on LinkedIn, because I really like that and you know, anything new that comes out, you know, posts or things from from me and my colleagues that I'm supporting, that's, that's the place to get them, it's also going to be the first place that I announce the Digital Transformation Course, when it comes out at the end of March, and you know, any of the other exciting stuff like, you know, how can how can your organization become an inclusive enterprise, because you know, all of you if that's something that you want, that's something that, you know, we now have a path for you to get there and for you to engage, you know, whoever are the leaders who want it to, we think it's really going to be something that helps millions of people.

Harsh Thakkar  38:04 
Alright, well, we'll make sure to put that you know, in the in the show notes so people can connect with you. Any final thoughts you want to add? Were you thinking of any questions that you thought I would ask you, but I didn't.

Nicole Radziwill  38:16 
I just wanted to say thank you so much for having me on here. I really appreciate your mission of bringing this resource to people in life sciences and beyond. You know, I know that that's the the main niche that you can bring a lot of the the new concepts and innovations and help people practice in ways that are going to make them more successful. And I'm just really excited that you're serving that purpose, and that you're also going to help us understand more about the quality and life sciences. So I just wanted to say thank you.

Harsh Thakkar  38:49 
Yeah, thank you and wish you all the best with all the projects and everything you're doing in 2023.

Nicole Radziwill  38:55 
Thank you.

Harsh Thakkar  38:56  
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