Tariq Munir - Beyond the Buzz: Simple Strategies for Adopting AI & Machine Learning

Tariq Munir - Beyond the Buzz: Simple Strategies for Adopting AI & Machine Learning

Episode 23: Where we unlock the secrets of staying relevant in the ever-changing landscape of technology and finance with our guest Tariq Munir (Head of Finance - PepsiCo), a finance thought leader who's passionate about simplification and easier solutions. In this captivating conversation, we dive into how technology has redefined finance and discuss Tariq's experience with upskilling, particularly in data science. Discover how Tariq's mastery of machine learning algorithms has allowed him to evaluate technology based on its merits, rather than just the hype surrounding it.

Join us as we explore the importance of having a future-focused vision for metrics and data in digital transformation and the dangers of jumping into digital transformation too quickly. Learn how continuous upskilling can give you a competitive edge and why it's crucial to invest in yourself to stay ahead of the curve. We also discuss the role of culture and upskilling in the success of digital transformation initiatives, as well as how to avoid the pitfalls of the 'shiny tool syndrome.’

In the final segment of our conversation, Tariq shares valuable insights on implementing digital transformation projects holistically, emphasizing the need for shared responsibility and adaptability. Learn how to approach upskilling and technology with a tailored approach that ensures successful outcomes, and how to be tool agnostic while committing to a plan to upskill. Don't miss this episode filled with expert advice and strategies for finance professionals navigating the world of technology and digital transformation.

Audio Podcast Links

Show Notes

Where to find Tariq

Tech Mentioned

- IBM - Tariq mentioned completing a data science certification from IBM. [https://www.ibm.com/ ]

- Python - Programming language used by Tariq in his data science projects. [https://www.python.org/ ]

- Power BI - Data visualization tool discussed in the podcast. [https://powerbi.microsoft.com/ ]

- QlikSense - Data visualization tool mentioned in the podcast. [https://www.qlik.com/us/products/qlik-sense ]

- Notion - Productivity app mentioned by Tariq as his most-used app. [https://www.notion.so/ ]

Other mentions

- Institute of Chartered Accountants in Pakistan - Tariq obtained his ACA from this organization. [https://www.icap.org.pk/ ]

- Chartered Accountants of Australia and New Zealand - Tariq is a chartered accountant with this organization. [https://www.charteredaccountantsanz.com/ ]

- Code Camp - Tariq mentioned using Code Camp as a resource for learning. [https://www.freecodecamp.org /]

Books Mentioned

- Leading Digital by George Westerman, Didier Bonnet, and Andrew McAfee [https://www.amazon.com/Leading-Digital-Technology-Business-Transformation/dp/1625272472 ]


(0:00:00) - Upskilling for Finance Professionals

(0:07:24) - Upskilling in Technology for Finance

(0:22:35) - Upskilling & Data Visualization

(0:29:51) - Holistic View in Digital Transformation

(0:35:41) - Agility and Strategy in Digital Transformation

(0:43:31) - Digital Transformation and Shared Responsibility

(0:54:31) - Approaching Upskilling and Technology

Chapter Summaries:

(0:00:00) - Upskilling for Finance Professionals (7 Minutes)

I chat with Tariq Munir, a finance thought leader passionate about simplification and finding easier solutions for difficult problems. We discuss how technology is redefining finance, and how it is the central figure in all of the change happening today. We also explore Tariq's experience with data science and his recent qualifications in the field. Finally, we reflect on the importance of data as the most valuable resource today, and Tariq's hobbies outside of work, such as oil painting and his passion for classical music.

(0:07:24) - Upskilling in Technology for Finance (15 Minutes)

Tariq Munir shares his experience of upskilling himself and how he used machine learning algorithms in finance to detect anomalous transactions. We discuss the power of machine learning algorithms and how they can save time and money for finance functions. Finally, we look at how Python was used to build the algorithm and create test data.

(0:22:35) - Upskilling & Data Visualization (7 Minutes)

Tariq Munir and I discussed the importance of upskilling and how it has helped him develop his career in finance. We discussed how demonstrating one's passion and desire to learn has helped him get ahead and how he used machine learning algorithms in finance to detect anomalous transactions. We also explored how data visualization can be used to uncover interesting trends and insights that may have otherwise gone unnoticed.

(0:29:51) - Holistic View in Digital Transformation (6 Minutes)

Tariq Munir and I explore the importance of having a future-focused vision for metrics and data when it comes to digital transformation. We discuss how data from multiple sources must be combined to create a robust predictive analytics forecast and how finance often takes the lead in this process. We also touch on the dangers of jumping into digital transformation too quickly and why it is important to upskill yourself to stay competitive.

(0:35:41) - Agility and Strategy in Digital Transformation (8 Minutes)

Tariq Munir and I discuss the dangers of the 'shiny tool syndrome' when it comes to digital transformation. We explain how this can lead to tailoring a solution to fit an existing process, resulting in complexity and a 'legacy spaghetti' of tools. We emphasize the importance of having an overall strategy for digital transformation, and going for small pilot projects to demonstrate the value of the technology. We talk about the importance of taking the time to think through a problem before solving it, and how culture and upskilling can play a big part in the success of digital transformation initiatives.

(0:43:31) - Digital Transformation and Shared Responsibility (11 Minutes)

I chat with Tariq Munir about the challenges of implementing digital transformation projects in a holistic way, and how shared responsibility is key. We explore examples of successful digital transformation projects and how to adapt them to different business models. We also discuss the importance of upskilling, having a future-focused vision for metrics and data, and avoiding the 'shiny tool syndrome' when it comes to digital transformation.

(0:54:31) - Approaching Upskilling and Technology (14 Minutes)

Tariq Munir and I discuss how to approach digital transformation projects in a way that ensures successful outcomes. We emphasize the importance of being aware of the limitations of technology, and how to use it to solve complex problems. We also stress the need to learn the fundamentals of data visualization and the importance of investing in yourself to stay relevant. Finally, we share advice on how to be tool agnostic and commit to a plan to upskill, as well as the need to tell the story behind the data.


Transcript generated by Podium.page

0:00:00 - Tariq

The intent or the purpose of doing that was to demonstrate the power of machine learning, power of AI, and how it will reshape our future of finance. So imagine a situation that finance wants to build up predictive analytics right, that's a big buzzword right Now. Imagine I build that based on the finance data that I have, and by building that I ignore all of the marketing innovation data, all of the category inside state. All of them are marked up with the sales data. I will always have a half-baked focus. The earlier phases of any digital transformation journey is to simplify your processes, remove the complexity and automate your workflows. Only then you should go for machine learning. If you haven't done those, do not go for machine learning.

0:00:52 - Adam

Hello and welcome to Tech for Finance, where we help finance professionals leverage technology to level up their lives. I'm your host, Adam Shilton, and in this episode we're going to be chatting with Tariq Muneir. Tariq is finance thought leader with passion for simplification and finding easier solutions for difficult problems. His breadth of experience spans supply chain finance, FPNA, integrated business planning, advanced analytics, financial reporting, commercial decision support and finance transformation. Well, Tariq is currently the head of finance for supply chain at PepsiCo in Sydney, Australia, where he leads a multi-geography team and serves as a strategic business partner. Before joining PepsiCo, Tariq worked at XO Noble as head of finance in a PWC in various audit and advisory roles.

Originally from Pakistan, Tariq obtained his aca from the Institute of Chartered Accountants in Pakistan and then later became a chartered accountant with the Chartered Accountants of Australia and New Zealand. Outside of work, Tariq loves to do oil paintings. We've not had that one before. He's a great artist and a great leader. He's a great leader in music and cook. But before we start, if you like what you hear today, please make sure to subscribe to Tech for Finance on your favorite podcast platform and on YouTube.

0:02:06 - Tariq

But really, appreciate you joining me today. Tariq, It's really good to have you on. Oh, thank you so much, Edamon, And the players are mine, And thank you for such a detailed and overwhelming I would say introduction. Yeah, thank you so much.

0:02:21 - Adam

I'm humbled to be on your own. your podcast I mean, the oil painting is a good one. Is that something you've been doing for a while?

0:02:30 - Tariq

Yeah, actually last couple of years I would say four or five years I have just found out that it's kind of a hobby I have developed. It wasn't something I have been doing for a very long time, but, yeah, i developed something very recently. In fact, these paintings just at the back, these are all painted by myself.

0:02:49 - Adam

You know, amazing. So people watching our video will be able to see. But yeah, take some pictures after the session. I'll put them in the show. I think people have lost that. And then the music you play.

0:03:03 - Tariq

So I do a bit of a classical music like the Indo-Pakistan classical music. So, yeah, still, still, i would say not something I would do on a podcast, maybe, maybe, maybe later part of some, just give it a little bit more time, not in this podcast, for sure.

0:03:25 - Adam

Yeah, i'm interested because I've got a bit of a muted background myself, but more sort of rock and heavy metal is my scene, i guess. But a bit of a side-trick anyway. But over to you. So I keep up with a lot of the stuff that you're doing, tarik, now because obviously you've become more active on LinkedIn recently. I think you've been a PepsiCo coming up to what Is it? 11 years now, something like that.

0:03:50 - Tariq

Yes, I'm a very own dachio.

0:03:52 - Adam

I like that. So I mean, obviously our listeners love the tech side of things. You know. You know a load of finance professionals that are really interested in. You know how do I build my skills? How do I start thinking about technology? So, looking at what you've been doing recently and we'll jump straight into it. I hope you don't mind, but you've taken a really interest in the data science side of things. So, looking at some of your recent qualifications looking back to, i think, 2020, which was the statistics of Python, machine learning, data visualization and so on And then I know you've got a more recent qualification as well on digital transformation, but that sort of stuff back in 2020, that was kind of before we've seen this recent AI explosion. So what kind of led to your interest in upskilling there? you know, specifically in those areas?

0:04:50 - Tariq

So, anand, a great question. Adam. So the art best man? he writes in one of his books, leading Digital, that technology is the biggest story in business today. Plain and simple, right, it is redefining the way we interact with our customers, how they are consuming our products. How do we redesign our processes around technology And in terms and hence we are reinventing the business models. Right Now, this impact of technology is same, similar, so finance is no different, right, when it comes to the impact of the technology. In fact, if I firmly believe that finance is the central figure in all of these things, we are at the core of it, we are at the heart of this change.

Finance is presumed, is a presumed data stewards. How many time it happened, would have happened to you, that any data, in any part of the organization, something goes wrong? or finance, let's talk to finance. They can fix it. Right, so we are at the helm of it. Right, so we are at the center of it, and we know today that data is the most valuable resource. It's the new right.

So, through this upskilling or through the certification, what prompted me to do that was actually one of I wanted to answer the biggest question I had in mind. How could I use the data that we have available to drive a data-driven decision making and become a better strategic partner to the organization? right, it all revolves around that, and when I moved into that space, my initial reason was not to get certified, start writing codes or become a full-blown data scientist. Right, it was never the intention and that is still not written. I just wanted to be comfortable enough to speak the same language as more technically people, technically savvy people in the organization have that seat on the table when the big decisions are being made around the digital transformation. How do we transform and how do we digitize or automate our ways of working? So I believe this is this helps you get that seat at the table, right Then?

secondly, i wanted to be able to evaluate the technology. Look at a solution not just based on its hype or buzz. I wanted to evaluate based on its merits. Right, i wanted to know what are the things out there that are available. Yes, i can code per se. I did write two codes as well as part of my capstone projects and then later on, just as part of my own personal address.

But again, that was not the intention. To write code these days, you can actually, you know, without even using a single line of code, you can create a whole app right. However, getting back to the basics, understanding how things are working, how technology is really shaping our workforce, how it's impacting the business That is what actually prompted me to go down that road upskilling myself And, to be honest, upskilling. I'm a big advocate of upskilling. You might have seen some of my posts on LinkedIn also. I believe in continuous upskilling, whether it's technical, it's software scale. I firmly believe that that is the only way finance can stay relevant in this ever-changing landscape of technology. So hope that answers your question.

0:08:42 - Adam

Of course, Of course, And I think that's such a good way of looking at things. Just going back to your point there about I want to be able to evaluate technology based on its merits, not on the hype, And I think that is something that people need to take away straight away, because there is a lot of hype now. Yeah, there's a huge amount of hype. There's so many platforms coming out And, as you say, people being able to develop applications without needing to write a line of code. Right And curious though you said, obviously you're not a full-blown developer. You know, and doing these qualifications wasn't ever with the intention of writing applications or doing any of that sort of stuff. But the stuff that you did write, what sort of stuff was it? I'm just curious. No-transcript.

0:09:33 - Tariq

So you know, when I did the data science certification, that was like around a six to eight month certification from IBM. It was pretty intense, right. I mean you actually learn a lot of machine learning and a lot of advanced coding kind of stuff, iphone and stuff. So I was actually thinking and that's where those those curiosity comes into play that what is it that I can? I can use these different machine learning algorithms as one of the practical use cases In finance. My initial capital was more around the real estate landscape of Sydney, so that was not really finance related. So I was really working on, on, on, on on the back of my mind that how do I actually bring this into practice in the real world finance? So I used certain algorithms, i did some of the research and then the good thing about Internet today is that all you need to do is then actually an internet connection And you can learn everything pretty much right. So there are a lot of forums available out there where I could. I could see some of the chords and some of the algorithms which when I, when I looked at those, it struck me that I can use those to to help, to help finance functions.

Do the enormous, do the detection of anomalous transactions right. So, how you know, we in our audit, we do a conventional approach where we pick out a sample and then hope that you know, in that sample Somehow we will be able to pick one of the transactions which are an anomaly. However, this algorithm, what it does, what it did it I created a test data. Based on that, on that test data, which actually It actually identified all those transactions which were anomalous, right, and which were not known, not a routine transactions, so it flagged. So they were around I'm on top of my mind, i don't remember, but we be around 10,000, 20,000 transactions that I put into the algorithm and it gave me, say, 20 or 30 transactions which looked big, all right.

Now, what a finance can do is, or, or those internal control managers, or those finance leaders. What they can do is they can then just look at those 20 transactions and see how many of those are actually false, positive, or how many those are actual, real anomalies. This cut down the time, this cut out the efficiency. This removes that sampling bias, that population bias, if you scale these kind of algorithm. So this was just an example to demonstrate the power of power of of algorithm that power is actually learning. If you scale it across the entire organization, that is, you know, creating transaction. That is, creating gigabytes of transactions per per day, you can imagine how much value it will generate. 100% right.

0:12:33 - Adam

Yeah, i'm 100% and it's an interesting project and you probably had the curve because there's some tools now that are trying to build that in some, some better than others, right. Yeah but no, no, i mean the use case, obviously definitely there. You know why go waiting through transactions when a machine learning algorithm can tell you what to watch out for? you know?

so, from a, from a time exactly use is a major, and was that just a bit of Python connected to the algorithm with some, some rules? I mean, i'm not simplifying it right, but was that that basically the foundation of how that was built?

0:13:08 - Tariq

Exactly so. It was all written in Python, right. And then there was a couple of algorithms that I used the mean mean one being the genius neighbor And and then I use those to. Then I created a bit of a test data to a population, in fact, a popular. I created a bit of a population to to test my algorithm, to make sure that you know I'm getting the results that I'm trying to achieve And actually the algorithm is working. So it was more of a, more of a test case for me. It's actually the link is out there on my LinkedIn profile as well, on the feature section.

If you scroll towards the right, you will find out the, the, the details of that algorithm that I, that I. That was quite a few years ago, but the. the intent or the purpose of doing that was to demonstrate the power of machine learning, power of AI and how it will reshape our future of finance. Imagine this kind of an algorithm you have that algorithm available or sitting on your system all the time which is flagging you all the time transactions which you need, which are not normal, which are non repeating transactions. Imagine how much time the reporting teams or the audit teams will save because they have that information available in real time, they don't need to do a quarterly testing, a yearly testing, and then you know, get, get all those kind of works done. So it's, it's, it's really really, really a powerful, powerful tool, i must say.

0:14:51 - Adam

And I'll link in the show notes and took to that resource if you're, if you're happy for me to do so, so know that that'd be great. So not absolutely. That's a slightly different question, then, and this difference in the list of topics.

So we're going to go in a bit off track here, but it's it's related so that's okay, since, since you went through that exercise a few years ago, obviously that was pre chat, tpt, large language models. So the question is if you were to do that again now, do you think you'd be able to do it quicker? Is there anything that you do differently if you went through that exercise again with the tools that we now have available to think?

0:15:34 - Tariq

Oh yeah, absolutely. I think I would have just put in the chat tpt what's the code that I need. And again, there are there. There can be two ways of looking at it. One was when I did my research on that. I mean, it took me quite a while. It took me couple of weeks to actually come up with an algorithm.

Considering the fact that I'm not a coding expert, right, i mean, i did learn the coding, but it wasn't my never mind tension or that level of interest, but the the the other way. So that actually help me build a lot of expertise in that right. So I gain a lot of knowledge through research and came to know about different, different ways. Actually, it gave me idea as well that different algorithms can be used in different situations in finance, right. So which was the intention, which was the actual reason for doing these kind of certification and these kind of upscaling? that you know what are the algorithms there, what are the tools there that you can then apply to the real world scenarios. The other way of looking at the technology is that, of course, yes, you know, i would have put that in chat tpt and it would have given me me that poor. Having said that it would have still saved me the time to write the code. But how do I contextualize it in today's and to my use case, to my scenario? that's where my role comes into play, right, yeah, gpd is, of course, a great way to get get your base work done, save some of the time on the research part of it. But then you contextualize, you bring that human aspect, humanistic aspect around. What is the reason you are doing it? what are the? what are the underlying parameters? that needs to be evaluated, how do you actually evaluate the result? right, so an algorithm can give you thinking, tell you there are five different, five transactions which are not correct, but then you need to evaluate that, whether, yes, there might be context to some of the transactions. So, yes, algorithm identified it correctly, which is a good thing. But then, yes, this is not something we will classify as a, as a as an anonymous transaction or as a as a non routine transaction.

So, yes, with chat, tpt, i would have definitely cut down my research time, but, having said that, i'm so I do a lot of research on on this topic and recently I since we're talking about the topic, so I did. I haven't had a very, i would say, enlightening experience from a charge, from using charge ept. So I did ask about so. For example, there were a number of studies on digital transformation, number of people that have been published on digital transformation, and when I'm going in and trying to get some of the references from chat ept, generally those, generally those references, do not work.

So either the data is without data at the moment and it's, or the links are updated and they do say that it's only valid 2021. Mostly not a lot of data 2021 is there, but still I I can. I found that bit of a bit of a disconnect in that aspect. So, yeah, over the lines should never be there. We should rely on those technologies that keep. It is a great technology. But then, yes, definitely cut down can cut down a lot of time. But just a question proceed with question.

0:19:04 - Adam

Yeah, yeah, and I think one of the points that you mentioned that this that's really valid is the certification that you got and the training. The qualification that you got gave you that grounding and that base knowledge of machine learning and the topics that you you covered, and I think that's so important because a lot of people think you know and maybe there are some cases where you can actually do this without any foundational knowledge at all. You know because there's a theory to say chat, ept, just give me the foundational knowledge.

But but that aside, i think there's a lot to be said for at least getting grounded in the subject, Before you then try and use chat ept to speed it up, because and I have tried this right so I have tried and getting to keep it to code and so on and so forth, but it's always been limited success because I don't understand the base language or the base concepts of that coding language. You know, and you can tell Jack chat ept to validate the code as many times as you like, but again you know it doesn't totally understand the context or the end goal of what you're trying to achieve some time down, so you can get into a real rabbit hole. You know of frustration and endless time consumed to it because you think that this top knowledge is going to be the answer to everything. But in fact sometimes you need to take a step back and say, right, well, what base knowledge do I need to get the most out the tool, and then how do I speed things up after that?

0:20:37 - Tariq

Absolutely. You're your sport and Adam, and we need to treat all of this technology and embrace all of the technology. So I'm big on technology, right, i mean that's why we're having this conversation, yes, but I'm, i do. I do say that to myself that, yes, i'm a big fan of the adge and all these open a large language model. These are. These are great, large and model the. It's a great, great technology, right, but whenever we use it, we need to make sure that we are as you, as you rightly said, that we are grounded in in our basics. Right, if you want to and there is nothing wrong in learning a new skill, right, there's nothing wrong in if you want to really absolutely go for coding, go for it, but then get some basic knowledge, get some basic understanding. And how do you actually apply that code in the, in the, in the world? that's, having that knowledge is very important. Having that context is very Yeah.

0:21:35 - Adam

Yeah. So I suppose that that's the key takeaway, isn't it? you know, don't don't go head long into something you've not got expertise, and just because you think there's a short cut to doing something, you know, do, do, have a bit of a bit of a think about it. First, right, so, but but moving on a little bit, i guess. So I noticed that, when you've done some of the certifications in data visualization and that sort of stuff, that one of your next projects at Pepsi co was related to power behind visualization, visualization with power behind. Now, of course, don't you don't need to go into the specifics. Obviously it's sensitive information, right, but I'm curious to know, you know, was that a direct result of the stuff that you did in 2020? did that open your eyes to say hang on, there's technologies that we need to be using? and then the second part of that question is you know, what sort of value did that project bring about for your team and and the business? I guess?

0:22:32 - Tariq

Oh, great, great question. And so, yeah, i would. I would say, when I did my certification and you know, i presented my capital project and discuss my capital project with my, with my manager, with the CFO, the CIOs, etc. Just to give them that and that, just to demonstrate my passion right that, how much I am passionate about these kind of things and how much I'm upskilling myself, so that definitely helped me in, in, in getting in, in becoming a lead for for a period of time, for that project right. And and again, you know, i was definitely considered because I had demonstrated that desire to learn and that desire to upscale and my passion for, for, for data science and and analytics From a from a value addition point of view.

again, you know, this one quite a big project. it was not just the, just the power, power beading, but again, you know that really helped me put out in front of the of the broader organization how different, how different ways data is flowing through the organization and how we can actually streamline and create dashboards and do some of the work related to, related to advanced, advanced analytics. So so, again, when, when you upscale yourself and when you keep demonstrating how, how you want to take your career further and how, what are those areas which are really excites you? Definitely, the opportunities come into play and you, sometimes you get surprised that how opportunities come through your way, through, through, through, through the avenues which you, which you would least expect The definitely.

this, this certification, definitely helped me, and not just from from from the, from the, from the organizations point of view that they see you as someone who has the right skill set, who is upscale with, always ready to learn. It also helps you to apply your knowledge right in those, in those, in those projects, and really develop you in, in, into a much more of a futuristic kind of a finance professional. So that's why I always I'm a big advocate of, as I mentioned earlier, big advocate of upskilling, showcasing your, your, your technical skill set and your, of course, your, your software skillset also.

0:25:18 - Adam

So that's, that's I would say It's a slightly different question then. So and again I'm focusing more now on the, the visualization and the analysis, i guess and whether it's power, be I, your, what, whatever other tool, that's the end, the end game or the end results the same right want to be able to visualize. We will want to be able to make more informed decisions. I'm always curious because I did, you know, i've run data projects in the past and, and I've seen firsthand how, in visualizing data in different ways, you spot stuff that you didn't necessarily notice before. So, as a result of that project or any other project that you've run, if there've been any interesting trends or or any interesting stats, kpis and anything that you didn't necessarily expect to see from being able to visualize data better at the end of the project compared to working spreadsheets before, for example.

0:26:10 - Tariq

Yeah, absolutely. And number of number of instances, not just this product, but throughout my career. I have the number of different automation project. So we're, we're where we did eventually noticed a number of insights, i would say, which were very different to what our expectation was or what our perception was, things like profitability of products, or, or, or even there was one, there was one project that I looked after which was around the consolidation of the financials. So I, when, when we did the consolidation, when we moved our consolidation to the cloud, i did realize that, you know, there are number of different insights that I was not looking at. There were number of different information that I was not, i was probably missing out because things were in Excel and you know they are they.

You, you put something in Excel. In next year when you open the Excel file, you have forgotten all about it and some or someone just goes in and change something in everything else changes. So that must a number of different insights into those financials which are technology help me unlock. So again, you know I mean technology and lock so much for you from a data visualization perspective. And it's again, it's all about enabling the data driven decision making Right. It's making sure that we have the right data structures in place, right tools in place, right technologies in place in order to drive that right, that decision.

0:27:56 - Adam

And it's interesting, isn't it? and you gave a couple examples there. So you know product profitability, right, and and that's. You know, in some instances where we end up, we end up with a gut feeling some instances, don't we? so we work in a certain way. You know, we see the numbers coming through And we don't necessarily see that the whole picture, but because we've got grinding in the business, we know how things work. We kind of build up this internal dialogue and this, this communication that says, oh well, we, we assume this to be the case, but it's only when you then get the data and the ability to pull it into context and visualize it that sometimes that gut feel that we've always had, you have to challenge it, and sometimes it's a bit scary, isn't it where you think, hang on a second. You know we begin not necessarily doing things wrong, but have we really missed that for the past, however many years? you know, and it's interesting to see.

And then the second piece that you mentioned was I mean, you mentioned consolidating, consolidations. Obviously you're taking data from multiple areas and you're rolling into a single view, right, and I'm trying, trying to think of an allergy. I mean that the Lego bricks analogy has been used a million times, right, yeah, lego bricks scattered over a table doesn't really say anything, whereas if you build them to build them into a house, obviously you know it paints a much better picture. But you know whether it's. You know, if we imagine what one one data areas bricks, for example, and then another data area could be, you know, cement, and another area could be tiles right, you know, if we're looking at those areas separately, we only get an impression of the dimensions of the bricks, the materials of the bricks or what. But but then, putting them all together, you can then really start building.

You know what, what, what does it look like when all of these things together? and I think it sounds really simple, but it's not because a, you've got to get the data in the right format and in the first place. Yeah, be you've. You've kind of got to look at that, and maybe you can speak to this a little bit as well.

You know, sometimes people are guilty of just looking at data in the areas that they know about and they don't necessarily think about the data that they don't have. So we generally have conversations, as I say, this is the Royal, we, i previous podcast guests that say you need to have that future vision of the metrics that you want to surface and the decisions that you want to be able to make and then work back from that. But often people get stuck in there. Well, i'm only going to work with the data that I have access to right now. So, you know, if you found that that having maybe more of a view of that end goal and what you need to work to is better than just trying to, you know, take the data, got the moment and throw it into into a slightly different format.

0:30:32 - Tariq

That's a really, really good point, adam, and I would definitely I can. I can zoom in a little bit on that one, that is. That is, i believe, where the holistic view of a digital transformation strategy or digital strategy comes into play, right, what happens generally, generally, is that and you rightly pointed out we we tend to be comfortable working in the data, with the data that we have. So imagine a situation that finance wants to build a predictive analytics right, that's a big buzzword, right, predictive analytics that's coming. Everyone is going to go for predictive analytics. Now, imagine I build that based on the finance data that I have, right, and I ignore. And by building that, i ignore all of the marketing innovation data, all of the category inside state, all of the all of the market up, lift the sales data, right, if I ignore that, i am, i will. I will still create a predictive analytics based on an algorithm that will, right, because algorithm would not know, right, what is what is missing? data per se, right, what's that? what's the context of the data that is missing? I will always have a half big forecast, right, which will, which will again be the so, till the time I overlaid the data which is outside of finance, for example. Only then I'll be able to create a robust predictive analytics forecast, right. It will be of no use without that.

You need to overlay how the category is performed. You need to overlay what's the innovation plan. You need to overlay what. What are other factors that might impact your, your market up lift, right. You might even have to input some of the data. From the supply chain point of view, you might have had a need to input data from things like macroeconomic indicators.

So, if you very rightly pointed out that working in that one silo of data is, is, is not never, never helps with the, with the, with the, with the, with ensuring that the technology, we are unlocking the real benefit of the technology and that's where a bit of a concept around data leaks coming to play right. Their organizations are now moving from those typical reject data warehouse structures to a much more flexible data lake houses, which has data flowing in from ERP systems, from marketing, marketing systems, from from what? the sheer data and and a lot of other data, like even unstructured data, coming in from from things like social media, facebook, instagram. So a lot of data comes into both data lake houses and then from there you apply a layer of your AI and machine learning algorithms where we lack generally, or what we sometimes do organizations do, is that they just do it based on the data that they have, right, that that you that you just mentioned.

So finance generally, you know. Look at their data, like, okay, i have a 20 years of information available, so maybe I can put some algorithm on top of it, and then what, what, pretty much, that algorithm will do. We only do it of a time series analysis, right, and based on that it will say, okay, this was the trend last year, this is the trend, this was the trend over last 10 years, this is the trend which is going to be next for next three, four years. So we do not add a lot of value in my view.

0:34:07 - Adam

No, it's really. It's really good. Again, come back to you know part of your intro, which was your work as as a business partner and not it may be like, because I looked when you're linked in post, it's what you just said. You know, digital transformation and functional side, those are sworn enemies of each other. You know it's. It's the right.

So, as soon as you start getting into that strategic partnering side of things is, as you mentioned quite rightly, it's you know, we're not just talking about finance data anymore. You know it's non finance data. We start bringing into these models and I'm pretty certain, you know, with the advent of, you know, sustainability, sg and all these sorts of things as well, that's going to become more and more important, you know, and it's going to be finance that leading that. You know, technically it's not just finances responsibility to ensure that a company is running sustainably right. But it comes back to your point. The beginning is that anything that's data drip and tends to be pushed towards finance, rightly or wrongly, yeah, so I think that's that's very good insight from you there.

You also spoke on recently about how people are sometimes swept into digital transformation too quickly, maybe because it's still a bit of a buzz phrase. You know everybody wants to transform digitally, especially with the advent of new AI technology, so it's. I'm wondering whether you can talk a little bit more about what you meant there by maybe people needing to put the brakes on a little bit before they go head first into a digital transformation project. Can you talk about that a little bit?

0:35:37 - Tariq

Yeah, so you might be able to relate, based on your other podcast also. What happens generally when we talk about automation or we talk about about digitization what we call a shiny tool syndrome, right, someone in the organization gets a demo of a great tool which is a cutting edge tool and which is the one of the you know which which promises a lot of return. That happens and then somehow we want to get that tool right away and we want to make sure that that gets implemented and we start extracting value for out of it. Now what happens in that scenario is that the tool gets implemented only to realize after some time maybe a year's time or whatever, depending upon the tool that originally that tool or originally our processes were not designed in a way to cater for that tool. Data was not structured in that we did not have that level of data governance or data management in order to cater for that. What that results in in what we call tailoring the solution. Right, so we start tailoring the solution to make it make it savvy for our processes, and so, for example, if we have a complexity in our process process, we try to make, we try to tailor the tool in a way that it can help us. Still still, with the same complexity still remaining there, that tool can help us, you know, still deliver results.

So what happens in that scenario is that the complexity results in that the tool becomes, or those tools or those technologies become, bit of a, you know, part of a legacy spaghetti that we sometimes call. They just remain there. We look at that and we say, oh yeah, we have a tool which we, which we, you know, which we go for. But many a time these kind of scenarios I have seen with with some of the more general case studies that I have, i have read through in which in which these kind of scenarios have happened. So, and when I say go file, do not go all in or do not go fast in that one, i don't take me wrong, that you know, at the end of the day, the digital transformation is all about agility, right, so we have to move fast, definitely need to move fast. However, the way we need to it's more about how do we approach digital transformation. So definitely go for first, move advantage, try on new products, right, try on different technologies, but then make sure that technology is is largely within the scope of deep strategy that we have, right. So overall strategy that a business is carrying around the digital transformation, or the finance transformation for that matter, that tool sits somewhere in there.

Go for small pilot projects to create excitement, to get the funding from, from your, from the executives, to to, to demonstrate the results and the demonstrate the, the power that these technologies can unlock and then move fast on those pilots. Right to not let those pilot You know pilot projects, you know burning in pilot budgetary, which is quite a quite a common phenomenon as well, right, a lot of pilots get into the pilot pilot project, get into the pilot budgetary and remains as a pilot project to move from pilot to to full scale. That's where your agility and your buying from your senior stakeholders and buying from a broader organization comes into play, right. So everyone, from top down and cross functionally, should be aligned and should be motivated and excited to go ahead with that technology. So that's what I meant by by you know pilot pergatory is amazing.

0:39:49 - Adam

I'm gonna, i'm gonna steal that one is not. Funnily enough, it's not terminology that I've heard before, but you know you're right. I mean makes another. Another term is proof of concept.

0:39:58 - Tariq

Right, you know proof of concept and the proof of concept it comes in and then you know, i just, it just is there.

0:40:05 - Adam

Yeah, absolutely. But. But yeah, I mean, and what I'm saying generally now is, with the advent of tools that are that have more out the box, it does enable you to to do more with a trial, for example. You know, and I'm talking about I'm not talking about changing the RP systems that's a big, big yeah.

But with the stuff that you know, get, get the pilot done quickly. You know, low cost, prove the concept and then have those, those conversations. But but, going back to your point earlier, sometimes you do need to slow down before you can speed up and and it comes down to that, right, well, let's, you know, we've had the phrase on the podcast before you know whether it was Einstein or whoever. You know, you give me a problem and I'll spend, you know, 95% of the time thinking about the problem and 5% of the time solving the problem. Because I know, if we have more time, developing a well thought out plan to then reduce the time for implementation is a better approach than trying to that due diligence phase, trying to skip that foundational process stage and then moves too quickly on the on the latter stages. So I think, i think, 100% valid.

0:41:18 - Tariq

Absolutely and and just building on on on that item A lot of projects. In fact, there is a study that was done by a number of different consulting firms, like BCG McKinsey I don't remember exactly who has quoted that, but it is said that around 70 to 80% of the digital transformation initiatives they actually fail. Organizations are not able to generate the expected outcomes And one of the biggest reasons is this kind of is more around the culture of the organization, right? So I might be going a bit off topic, but it's still relevant when we talk about bringing the going fast, having that tradeoff and having that balance between going fast but then still going strategically, putting your 95% of your thinking there before going into, before taking into, going into action, but still making sure that we are not slow but we are also not overdoing the on indium of agility.

So, absolutely, this is, this is it's it's a complex, multi-prosfunctional, multi-dimension problem to solve, right? There is no one solution to solve a digital transformation journey, or to solve the implementation of any new technologies or tech stacks, for that matter. How do you do? it depends upon how your organization is structured. What are your operating models, how? how is the data flowing between throughout the organization right. What is the, what is the, the extent, what is the velocity of the data, what is the volume, what is the value of those typical, typical matrices that we wear? So it's it's very important that we are taking those things into consideration before just moving ahead with any tool, any next tool available in the market.

0:43:20 - Adam

Yeah, very valid. And yeah, just coming back to your point there about you know these things being considered and, depending on your operating model, depends on the sorts of decisions that you make, and so on and so forth. There's, you know, a lot of these projects now crossover multiple departments as, as we've been speaking about, you know it's it's especially for smaller businesses. Now, you know, i know you work for a massive organization, but you know you've probably got lots of video organizations within a massive organization, so you are still able to maintain that agility right, but implementing solutions in a silo now just doesn't work anymore. You know, and, and I think people embark on these projects thinking, you know, oh yeah, i'll take the lead, you know I've got a bit of time. You know I'll support it. You know it's not going to be an issue, but but then they get, you know, further down the line and then they find out, right, well, actually, this isn't, it's not a two week project, you know, it's a six month project And it's a oh no, you're going to need this expertise and you're going to need this resource And it's oh so I can't just do it myself. You see what I mean And it's it's at that point that you're then into winning the hearts and minds of your peers, because you've then got everybody else in on the journey with you.

And that's when the partnership comes in, you know, and your ability to create the impact, because you know you don't want to be in that position where you feel like you're you're trying to drive change and you're the only one that has something to say. You know there is that influence piece that needs to come in to say look guys, you know we need to wake up here. You know this is a priority project less and less sorted, So huge amount of variables, never an easy job, but but hopefully it's. It's like we were saying you've got the right people moving in the right direction And you know these things have a way of working themselves out.

0:45:05 - Tariq

I guess Yeah, and and that's where the concept of what we call the shared responsibility comes into play right? So when the organization creates the digital transformation as a shared responsibility and not just a responsibility for say, for example, it function right Or say, for example, data function right, but it's the shared responsibility across the organization. So there are a number of case studies that I came across Nike, burberry, they are. They are really great examples around how they transform their customer experiences, like I mean in a holistic way, bringing all all, how they, how they transform the way that customer were engaging with their products physically and how they were doing it digitally. So the experience that I've been created is a, is a, is a, it's a. There are a lot of good learnings to be taken from there, so do I would encourage the people listening to actually go out and search bit of those, those case studies as well, to get further insights into this.

0:46:08 - Adam

Yeah, and it helps. it helps tell that story, doesn't it? You know, it makes, makes a bit more real when you can see the other and and of course some organizations do have pretty big budgets to go through these transformation projects right Of course, but you can.

There's always a way of achieving similar results at a lesser scale. I always say you know, so you should still be able to transform your business, irrespective of your size. It's exactly the way. So, yeah, i saw and again it's another big company example recently, but I was, i was, i've been yesterday and one of my colleagues was saying to me yeah, hearing some really interesting things in more of the sales and marketing space and it's the trucks from our finance conversation a little bit.

But you know, if you imagine, like a car manufacturer, you know, like you know luxury car brand or whatever, where you're wanting to make that buying process more immersive, it might be that you end up in a car showroom, or maybe you don't even go to the showroom. Right, you just sit in your living room at home. If you can't see a car, that's the spec that you want, you know, so there's not one in the showroom with, you know red leather seats or you know the bang and all of some sound system or whatever happens to be. You can, either in the showroom or at home, put on your VR heads and build that spec that says, right, okay, so let's, let's put that and you can sit in the car with with the spec you want, right, and the car analogy is done to death. Obviously you know they've been. I think it was Mercedes that first started doing the sort of virtual showroom where you could kind of do the model and that sort of.

But it's nice to see, but we've also got to temper that with what is realistic over what is just kind of the sky stuff, because it's great but you've always got to look. So that obviously could mean a tremendous amount of value for that organization, right, but it'd be pointless other organizations trying to emulate that model unless they can flex that to say, right, well, that's a really good idea. Could we do something similar but in a slightly different format to you know, our business model?

0:48:08 - Tariq

Yeah, Exactly, adam, and that's that's where my earlier point that I was mentioning around looking at your specific scenario, your operating models, how data is flowing through, and digital transformation, or technology, is not just for big organizations, right Today. it was true, i would say 20 years ago, 25 years ago, but today the amount of cheap I would say rather cheap processing power that we have available in the form of cloud computing and in the form of SaaS, mortals, it's actually not just big organization thing anymore, right? Smaller organization. and again, you don't have to go all in. All you need to do is to make sure that you have a strategy, you know what your goals are, what is your logic for success Through the digital transformation?

why do you want to do it? What are those problems that you are trying to solve? Then take those steps, small steps, and start building those text stacks. Now, this technological capability that you build, you need to build over time in a way that you can. also, today, the technology is available where you can actually scale it as you grow. right, and you want, you need to do is make sure that your three different technologies that you are taking, or three different tools, they integrate with each other properly through APIs and through other other other means. So it's all about how you approach, right. If you are, then go for just the next available tool in the market and start applying it into the organization, big or small that is, that could be a hit or miss, right, because you never know if that's going to actually deliver the value that you want to generate from that.

0:50:03 - Adam

Yeah, and it's a valid point on the integrations as well. Unfortunately, now with the advent of new technologies, it is becoming easier to do that piece because there are more native cloud solutions And I think Zapp here are also talking about leveraging chat, tpt, like technology for people to be able to say to Zapp I'm using these systems, tell me how I need to map the fields, and then obviously the model goes away and comes back and says these are your mappings, this is what you need to, and so on and so forth. So that's still kind of early stage stuff, but we'll get there. The only thing that I will say and it sounds like you've got this sorted anyway is coming back to those goals and what you want to actually achieve.

It's great to put a strategy in place and build a list of 20 different tools that could add value in certain areas, but you just need to be very carefully. Don't end up with like a patchwork quilt of all of these different applications, because you often find that some problems don't need solving from technology and you can end up overengineering some. But likewise, coming back to the point earlier about wanting to actually know, as opposed to just rely on marketing collateral over what the utility is of a tool. I think people need to be mindful of that as well because, as I say, there is some pretty good marketing out there now and pretty soon. Who?

0:51:28 - Tariq

is Who is.

0:51:30 - Adam

Who is, without realising it right?

0:51:34 - Tariq

And that's the problem, right, you end up probably getting too many tools that you need. It's like buying too many clothes and then have nothing to wear.

0:51:47 - Adam

Yeah, that's it, and that's what I always recommend is and I go through this process personally and as a business is a bit of an audit in the same way that you'd audit your supplier spend are any of the applications we're using already developing areas and replace another tool. For example, because the pace of technology now is so fast, you might find that you're now using two tools where one could actually serve both parts. That you just didn't know because when you could turn it at the time that capability wasn't there. So my recommendation is always speak to the supplier of that system. Talk to them about the way that you're working, because you may find that you can cut five down to three. You know three down to two, but it's until you have that review and have those conversations that you know which way to go really.

0:52:39 - Tariq

Yeah, and Adam. This reminds me a quote from Bill Gates, actually. So he once said that the first step, or the early phase, was any digital transformation journey. I don't remember the exact word, but the meaning was that the earlier phases of any digital transformation journey is to simplify your processes, remove the complexity and automate your workflows. Only then you should go for machine learning. If you haven't done those, do not go for machine learning, and that's what the problem comes into play, or that's what we need to be very aware of.

Is that, first of all, if I were to start a transformation, first of all I would look at the processes. What are those processes that I really need to? and you rightly pointed out, not everything needs a technological solution. Not everything is a tech solution. There might be.

There might just be a matter of simplifying your core process first right, which will eliminate number of steps which you were actually perceiving earlier that it will be replaced by technology, but they were probably not needed at all. Right. So you streamline you, you de-mystify your complex processes first, and then you look at your operating models in parallel. You look at your operating models and other workflows and see what you can automate, what you can digitize, where you can apply machine learning, what are the areas that you can then just see it makes sense to keep them manual for the moment, right, and maybe in the longer term have a plan to somehow them to the technology as well. So, so yeah, this is how we need to approach this, and rather than just going for the, for the tool, or going for technology and just looking technology as a magic pill for all the elements I think that's that's also not true.

0:54:47 - Adam

Yeah, yeah, absolutely right, and just using the big handful there of you know, maybe keeping some things manual, you know, knowing that it's probably not going to be a manual for everyone, you know, but the moment of big enough problems to solve, right, but an example there and it comes back to the power BI point right, and it's not just in terms of the level of problems to solve, but it also relates to that learning piece as well.

So if, again, using power BI as an example, now with the AI and power BI you can put your data in and you can click a button that says automatically create me a dashboard, it'll go away and it'll look at the variables and it say, oh, that sort of data could fit, you know, a pie chart or this is best in a matrix or whatever it happens to be, and it will put everything in and it's great, right, as a time saving exercise, it's fabulous and you can get some really quick wins. But if you're new to data visualization, there's often an argument to say right, well, don't go to the fully automated approach first, you know, build a simple dashboard from scratch, because then you understand the foundation, you know, you understand the fundamental elements of building a data visualization and then use the AI later because you've got that grounding. Coming back to what we were saying about toding and all that sort of stuff, you know sometimes you've got to do this stuff manually to build that baseline and that expertise, to be able to automate less wrong.

0:56:10 - Tariq

Yeah, as they say, as they say, sometimes you need to take a step back in order to move in the right direction. Yeah, yeah, so.

0:56:19 - Adam

So I've got two more questions, because I appreciate it's late for you. It's coming up in your time, is it? Tara? can you probably want to finish for the day? So two questions. One is the one that you've probably seen, that I asked at the end of the show, so I don't know whether you've had a think about that or not. But no, the ultimate question is the people that are maybe four or five years behind where you are and looking at the state of technology, you know they know they need to get more into data because, as you say, data is the new oil, and so on and so forth. How would you recommend that they approach? taking on projects, upskilling, maybe taking into account some of the not necessarily mistakes, but some of the hurdles that you've experienced in the past? what sort of recommendations would you make?

0:57:08 - Tariq

Yeah, adam, i would be. The one line that I would always say is that be ruthless. And when I say be ruthless, be ruthless about your development. Okay, right, you need to invest in yourself. That is the only way to stay relevant. First of all, do not hesitate to learn what you have learned so far, right, and learn what you're not used to. Secondly, there is no shame in acknowledging that. There's no shame in acknowledging the lack of knowledge, right, so be open about it. Be very curious, right, and commit yourself to a plan when upskilling yourself. That's the baseline, right? You need to make sure that you have. You are aware of those technologies you are aware you don't need to do, you don't need to do coding where you don't need to become a data scientist. I mean, if you want to become, you can. It's a very lucrative field, i would say. But get a basic understanding with internet, so many resources are available out there right now Free resources in the form of YouTube. You can go to the code camp. You can go to things like FreePower BI Microsoft offers FreePower BI, things that are all those sort of things. But have a plan in place.

First of all, think about what you want to achieve right. Do you want to move more towards a digital transformation strategy space? Or do you want to move more towards the data analytics or data science space? right? So that will be called, because that will determine where, which way do you want to go, what different resources you are going to tap in order for you to build that way. Now, whichever area that you choose, commit a time to it right. I make sure that on Saturday mornings, i have some time block for myself where I upscale myself right. Every single Saturday morning, i do that right. This is the most, one of the most important things for me to develop myself in my career and be able and anyways otherwise be a bit more informed person as well.

Secondly, be a little bit tool agnostic. Yes, learn a tool right. Learn. For example, if you want to learn Power BI, go for it. If you want to learn some other click sense or some other tools, go for it.

Of course, you will need some tool in order for you to learn around a bit of a data visualization and those kind of areas. But be open. Right Tools come and go right. I mean, one tool is applicable today, it might not be there. So build your basic knowledge.

Right Now the basic knowledge comes into play around. How do you do? by its very definition, is there just to help you do some stuff? right? The real power comes in when you are able to tell the story behind that, right. So work on your data storytelling skills right. Make sure that you tell the right story to the right people at the right time. Right, you can learn as many tools as you want. You can learn to code, you can become a data scientist or you can become any any programmer or developer.

But if we cannot explain right the point that we want to make, the decision that we want to make, and how do we influence people to make those decisions, it will not be of much use. So my advice will be to always be curious, commit yourselves, invest in time. If you want to really go beyond that, you can invest some money also, and, you know, go for a little bit more structured training courses also. But again, if that's not your thing, you can also you can still find a number of free resources out there.

So I, over past three years that's the kind of a habit that I developed right to upscale myself and keep, keep learning new things and be on top of things from. So, for example, my, the professional bodies I remember all they have really good resources available on a data science, data analytics, data visualizations and those sort of things. I keep on tapping into those to keep, keep, keep yourself up bridge with those with those resources. Tap into those resources. If five years ago, in fact, when I started doing my that's where I would say that was a turning point for me when I started doing this data science, i actually started doing the data science certification. I stopped doing it for around a year and then I started it again.

1:02:09 - Adam

Okay right.

1:02:10 - Tariq

So, so, so, and now if, when I look in retrospect, i'm like I should have probably started two years before that, one year I just spent in planning, right, okay, i'll do this, i'll do, i'll do something and I will go on the internet and I look at three different courses and I'll be like this looks to good. Oh no, this doesn't look good, this is too expensive, and I spent a year on that. So, in retrospect, if I look at that, i would, i would, i would have started even earlier. So, to all the, all, the, all the listeners who are actually really passionate about this thing, and then I mean we as a finance, i do believe that we don't have a choice but to be passionate about technology because it is going to come right one way or the other. Whether we like it or not, whether we avoid, it is going to come.

Technology is coming right. How do we embrace it? how do we make it as part of our daily routine and how do we actually extract the value, the? how do we actually unlock the technology to be a better strategic business partners, to be to be the navigators of the business right, rather than, you know, just doing our day to day routine and doing complex are doing bit of a routine work and you know transaction work and complex work. So we move on to a bit more of business partnering part, so that is amazing.

1:03:35 - Adam

That is amazing. There is so much. But I guess I mean just just to summarize a little bit. I think it is fair. You know, first have a goal. You know I make sure you're passionate about it, because it is one thing to upskill, because you think that is a good idea and because everybody else is doing. But you know it has come back to a previous point are you passionate enough to follow through? So I think I think that is a really valid point.

Second piece on, you know the using the infinitely available free resources, I think is as well right. And yeah, i think it is a reminder for anybody that is, you know, a chartered accountant or has some sort of certification, to go to their governing bodies and look at the resources and look at those sections, because there is probably a load of stuff there that they have not looked at in a while. It is probably there and ready to use, right. And then on the tools piece, i totally agree that you know it is better to be a business intelligence data visualizer expert in a broader sense than it is to be a data visualization power, bi only, for example, because you don't want to want to limit yourself there, but you're dead right in saying that behind the tool there is always that end goal, which is how do we communicate the story, you know. So how do we inform people as best as possible? because then that's going to stack the odds in our favor when it comes to the business, partnering and the creating impacts in relation.

And then the last piece that I'll say is it comes back to your time about ring fencing time on a Saturday morning. I think people very quickly get swept into the dirge of the day today. So, especially if they work from home, you know. So they shove some breakfast in their face and then think oh, you know, i've got all of this stuff to do, you know it's coming up to month end and I've got so much to prep for. So they immediately they're into work and then, five o'clock, they think what happened? where did my day go?

But in just coming back to our point about taking a step, a step back, find time for you first, yes. So as opposed to putting yourself second after work, put yourself first, yeah, because to remain relevant, as you say, you are going to need to develop those skills, and you're not going to be able to develop those skills if you're absolutely exhausted and didn't find time during the day. It's the same with investing money, right? The number one recommendation in investing money is that you pay yourself into your savings first when you get paid, rather than waiting to see what's left in the bank at the end of the month, because it's probably not likely, because you're going to have much left in the bank Yeah, personally, now, of course, the living rights.

So ring fence that first, you know, prioritise this on purpose, and that's something that I'll really emphasise. So thanks for touching on that as well. And yeah, so let me go to the last question is we've talked about some tools on the podcast, but for you and it could be professionally, it could be personally as well Is there an app on your phone, a gadget, yet another piece of software that you use all the time, that you just couldn't live without?

1:06:31 - Tariq

Yeah, great, great question, adam. Yeah, i would say, the app that I use most these days is Notion. Yes, with another notion So I cannot live without it. Honestly, all my to-dos, all my reading lists, my upskilling education piece, all the research that I do on in my free time, everything is on Notion. Notion owns me. If something happens to it, I am doomed Yeah.

1:07:08 - Adam

Well, that's it. I often refer to Notion as my second brain, because everything, yeah, exactly, and I always I'll be sorry.

going back to our point about condensing multiple systems into one is I used to run. I used to run one though I used to run ever know. you know all that sort of stuff. And then I moved on to Notion and I had one area that was my can band to do, and then another area, all of my other notes, but everything goes in there. Literally. I pull out links to my emails and drop my emails in there so I can prioritize them. Exactly Everything from an organization point of view goes in there. So I wholeheartedly agree with you, tarik Love, the facts I've got a great plan.

I will definitely be sending you links to Notion templates.

1:07:48 - Tariq

Absolutely, absolutely. That'll be great.

1:07:51 - Adam

People are building building businesses off the back of Notion templates. Of course, yeah, a bit money in it. sorry here, but no we'll let you go. I really appreciate that. It's been an absolute pleasure having you on the show And I really hope you have a good evening.

1:08:04 - Tariq

Thank you so much, adam. Thank you, i really appreciate your time. This is the song Cheers Tarik, Cheers.

Transcribed by https://podium.page


tech for finance

©2022 by Adam Shilton. Privacy Policy - Terms of Use

©2022 by Adam Shilton. Privacy Policy - Terms of Use