Dreaming About Business Analysis With Yulia Kosarenko

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In this conversation, Yulia Kosarenko and I explore the possibilities of Artificial Intelligence to support business analysis and the need to become a trusted advisor to get new data from the business.

Yulia owns Why Change Consulting Inc and is a professor at Humber College in Toronto. She is the author of the book Business Analyst: A Profession and a Mindset, as well as a conference speaker, consultant, and trainer.

Here are just a few of the highlights in this episode:

๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป ๐Ÿ˜” "I was interested in programming and thought, I'm going to be a programmer. But then things got a little boring." Yulia shares how she originally discovered programming through a library book, but she soon got bored once she started as a programmer. But when she got more involved with business, she found her love for business analysis because of her passion for working with people.

๐Ÿ™…๐Ÿปโ€โ™€๏ธ "I don't think that AI will replace business analysts." Yulia emphasises the importance of the human aspect in the business analysis profession. She highlights that collaboration, facilitation, and creating shared understanding are crucial to the role and that the need for these skills will only continue to grow.

๐Ÿ›Œ๐Ÿป ๐Ÿ’ญ ๐Ÿค– "I'll tell you what I'm dreaming of and hoping that AI can do for all business analysts." Yulia imagines AI assisting with business analysis documentation. She values documentation and believes it helps prevent misinterpretations and ambiguities in communication. Yulia suggests AI will be used to summarise meetings and documentation, freeing analysts from all tedious tasks.

๐Ÿช„ ๐Ÿง  "The role of the internal consultant should be more on our minds, not just someone who runs around capturing user stories." Yulia sees a tendency for BAs to act as order-takers at the project manager's beck and call. But talking more with executives to truly understand the essence of that problem or the opportunity they want to capitalise on and become trusted advisors to the business sponsors.

๐Ÿข โ†”๏ธ ๐Ÿ•ต๐Ÿปโ€โ™€๏ธ "Sometimes I see business analysts becoming too distant from business." Yulia worries our requirements don't hit the mark when we're too far away from the business. She highlights that we can gather new information with all of our senses and that talking, observing and immersing ourselves in the operations is what is needed to bring fresh data into this world.

๐Ÿšฎ "Business analysts are in a good position to try and help reduce waste." Yulia ends off by summarising that business analysts need to be striving to reduce political, historical, and economic waste in businesses.

Tune into the episode below or listen on Apple Podcasts, Google Podcasts, Spotify, or your podcast player of choice. You can watch the interview on YouTube here.

Brought to you by Business Change Academy skills development and career building business analysis courses.

The transcript of this episode can be read here.

  • [00:43] Yulia's starting story in the business analysis profession and what keeps her involved
  • [05:36] Seeing the business analysis profession develop and change from the past until the present
  • [09:12] Highlighting the crucial trends that will develop business analysis into the future
  • [10:54] Ideas about how Artificial Intelligence (AI) could help support the business analyst role
  • [18:33] Thinking old skills, new skills, and the skills coming to the fore
  • [23:04] Considering whether AI can draft systems analysis diagrams
  • [27:29] Stepping closer to the business to fulfil the responsibility of a business analyst
  • [32:05] Seeing a deviation toward decision support and assisting better business decisions
  • [36:49] What a typical day, week or month might be like for a future business analyst
  • [41:33] Summarising the goal that business analysts need to be striving for

What was your favourite quote or insight from this episode? Please let me know in the comments. ๐Ÿ‘‡๐Ÿ‘‡๐Ÿ‘‡

๐Ÿง  Add your brains to the  ๐Ÿ‘‰ Future Business Analyst survey.

Joe Newbert 0:00
Hey everyone, it's Joe. Welcome to another episode of the future business analyst podcast. My guest today is Yulia Kosarenko, owner of Why Change Consulting Inc, professor at Humber College in Toronto. She is the author of the book, business analysts to profession and a mindset, as well as being a conference speaker, a consultant, and a trainer. In fact, another all round business analysis enthusiasts, welcome to the show. Yulia.

Yulia Koserenko 0:34
Thank you, Joe, it's great to be here.

Joe Newbert 0:36
Ya know, it's great to have you here. The place I usually like to start with these is your start your start in business analysis, how you first got involved in the profession, and what it is about the profession that keeps you going keeps you involved. So perhaps you can share your, your starting story with us.

Yulia Koserenko 1:02
Of course, it depends how far back can go with that. But if I were to go to the very beginning, it all started with a book on programming from a local library, a book on Fortran, many, many, many years ago, which really made me interested in information technology. And then I ended up going into computer science degree. I was really interested in programming, and I thought, I'm gonna be a programmer. But then things got a little boring. I ended up getting involved with, I guess, business side of things all the time. And ask him questions and working with stakeholders, and I was unhappy being just a developer. And then when I moved to Canada, I decided it's time to figure out what I told him what to do. So I did some personality surveys, I went to see career consultant, we had a lot of chats. And then somehow, from all these conversations, I figured out business analysis sounds great, because it allows me to work with people. So they truly the pivoting point for me, was it I like to work with people. And I like to understand why am I doing STS or why do you need to do a certain change or write the program. And then that's where I started. So I took a few courses and got my first job as a B, and from then on ba BSA lead BA, business architect, and I could never give up to always be I guess, a business analyst at heart, even when they see one in I'm buying the tickets and things don't work, or I'm going on the website. And there's a strange label and they get all upset and say.

Joe Newbert 2:46
Yeah, I think that I think that's it is all you know, a lot of blogs, a lot of posts are always about these experiences that we have, where they don't go quite how we might like them to go that sort of logic or that experience. I think that's, that's wonderful in it. And it sounds like a lot of people fall into this job. It is quite accidental. But I'm hearing a slightly different story from you that this was a very conscious decision based on certainly some of the experiences that you'd had in programming and similar roles. And it sounds like because you say you're sort of working in the business, but you had this programming, you sort of already had that, that balance of perhaps the two sides of a business analyst when you got into the role.

Yulia Koserenko 3:33
Yeah, I think I had both, I ended up realizing that if I worked on the technical side, I always, so to speak, put my nose into business side of things. And at times when they ended up working for business, I was always interfering. There was one developers do that wanted to know what they're doing and talk to them. So I was always in the middle. And I always wanted to see both sides and how they interact. Which literally then I realized sounds like business analysis. That's exactly what it what it is for. So I think it was partially conscious choice because I did go through that exercise of career counselor. But partially it was just realization that I don't want to give up on technology on understanding how programming works. I'm still interested. But I don't want to give up on working with people. I couldn't sit in a cubicle all day and program. Right.

Joe Newbert 4:29
Yeah, yeah. Yeah. I think the first question I want to come to you with based on on that little background that you shared is, I suppose first of all, you've been in this this role a little while, you know, back sort of analyst programmer days, that's sort of what I'm picking up. And maybe over time, you've got experience of what the business analysts Job is Hubert also dropped in a few different job titles there. Some were probably synonyms for the business analyst summer, our roles that dovetail like, like the architect, but given that you've spent some time in this profession, have you seen the definition of business analysis change from sort of, then until now? And then the follow up question would be do you? Do you maybe see the business analyst job role changing from now into the future? And what might those changes that you've seen be? And those ones you foresee be?

Yulia Koserenko 5:36
Yeah, it's interesting. I think that early on, especially as I guess, early business analyst about em wolgan, from Requirements Engineer role, or even just, you know, software engineer role, business analysis definition was probably more software oriented, it was more about software requirements, defining requirements capturing, documenting them. And gradually, it evolved to look more at the business side, and starting with understanding the business needs first, and then trying to translate it into requirements. So I think they probably the be a sale system analyst role probably is a little older in that sense, because at that time really was interaction of requirements, or software engineer was business directly. Even at some point, I worked in a company where I was business systems analyst, but I had the business analyst counterpart. And I think now, we live as a definition becomes a little bit closer to business to understand and business needs. Because again, even if you think about software engineering itself, it evolved. Nobody writes, In Code anymore, we are software languages, and the exercise of coding becomes more high level. So it becomes closer to natural language. And it becomes more of a problem definition. And I think that impacts B roll as well. And now, again, if you think in the past, so many projects involved only one system. So you ended up a system analyst you work on one system, you write use case specifications for that system, you create sequence diagrams for that system. But it's so different now. Because if you look at the typical landscape, or even medium sized company, let alone the company. It's so complex, there are so many different products and systems interacting. So it's more not about system analysis. It's about developing solutions. And the solution for business. And I know I'm threading into architecture, but I think, because this analysis is coming closer, you may have been talking about it in my circle today. What's the difference between system and solution? It's not the same thing, solution to support business capability to me and called 10 systems, 15 integrations, five external sources, and so many processes, right. So business analysts now are concerned with solutions that support business capabilities. And that can be much bigger than just, you know, being an expert in one system and how that works.

Joe Newbert 8:21
Yeah, and I mean, as I was listening to you, I was saying you sort of transfer into into sort of Enterprise Architecture, maybe business architecture, that kind of thing, because it is hugely important, as you say things used to be simpler. And these days, they're much more complex. And when we introduce something new, we need to and understand a what we're introducing and be the landscape that we are introducing it to. So I'm definitely picking up from you the juicy sort of closer ties with between sort of business analysis and business architecture. And of course, they share a lot of the same thinking and a lot of the same skills. Are there any other trends that you see coming along aside? Business architecture?

Yulia Koserenko 9:12
Absolutely. And I know that there's one trend that definitely is on everyone's mind, nowadays, and that's data analytics and AI and machine learning. That definitely is an important trend. I don't think that AI will replace business analysts. I think, if anything, business analysts need to become more of an advisors to business in terms of the limitations of AI its capabilities, but also limitations and what inputs to create, so that you get, I guess, outputs from this new technology. So I think business analysts need to be very savvy of new technologies being developed and how they can be used to solve business problems. AI is just one of them. But definitely there are others. And I think that human aspect on business analysis profession is not only going away, it's becoming even more important. Because we interact with human beings on projects, we do things together, we collaborate, we facilitate that, meetings, interactions, and someone has to do that, that's then becomes even more important that our projects become more transcend countries now transcend time zones transcend different cultures. So I think that that element of a bringing people together and creating a shared understanding of what's the problem and try and understand you know, how we want to solve it is a crucial role, and it'll only grow

Joe Newbert 10:54
those great points in there. Yulia, I first loved how you framed AI, you know, it's not going to replace us that sort of talking towards AI being a competitor, right, competing with us over something, but you, you reframed it as being one of the many solution options that might be available to solve a business problem. So it becomes a tool for us in that sense, right? Perhaps a tool for us as business analysts that we can use to do our work, but also a tool for business so that they can run their operations in better ways. I can also hear that you've put some thought into this. So let's go down a little path here. Before we come back to some of the other stuff. Can you maybe give us some examples of how you see a AI supporting the be a role and what we do?

Yulia Koserenko 11:58
I'll tell you what I'm dreaming off and hoping for that AI can do for all business analysts. Documentation there. It to me again, the maybe because you can say I'm old school, you know, I'm from the school that values the documentation. And I still do and I think that without documentation, our communication suffers because misinterpretations, ambiguities come into play. And we all know how nobody likes to do the commentation. Nobody likes to take meeting notes, and it's sometimes fall through the cracks. So you'll have that long, long medium, and you'll have a video recording of that meeting. But there are no notes, there are no summary who is going to rewatch the recording, to extract like three important nuggets. So honestly, even if we could just use AI, to summarize meetings for us to summarize documentation to help with the research and information collection, I think this is going to be truly a huge help from an intelligence tool. Because making conclusions from those notes, may require thinking where business analysts can bring additional value. But that more mechanical work that we all realized, the language summarization and extraction of important points that the AI tools can do well, I think that could help and imagine the Fit freed up analysts from all the tedious tasks of capturing sayings. You know, how sometimes they say, Oh, how can you facilitate a meeting, and without a scribe, so imagine the AI is doing all this intelligence scribing smart scribing, but you are still the one who goes to the trenches, so to speak. So because AI, if you think about it, AI can only regurgitate information that was already collected, right? In the process of doing it, it looks for the most probable outcomes. So it's all secondary information. They you as analyst, you can get new primary information, you can observe the process, you can listen to customers, you can look at customer service, you can speak to your business. So you can gather new information, to have some fresh ideas and thoughts. And then I think we all need to learn how to use AI as a tool for us to be more productive so that we can do, I guess, more high level thinking tasks.

Joe Newbert 14:32
Yeah, it's sort of like hard labor, isn't it? Sometimes, that documentation and it will be wonderful to be able to, to have some support with that. I think the the caveat is that, that support must be good. You and I were talking about transcriptions before the show started in the accuracy around spelling and things so obviously, we're still at risk of perhaps the products that AI produced being a little substandard, not just misspellings. But like you say, if it's trying to find those nuggets, it's trying to highlight the salient points for us, we are going to perhaps need to check its work. I mean, certainly at the beginning, I know that this is going to evolve over time and get better. And that started to make me think is like, you know, in the past, we produce documentation. And the idea is that we then have a peer that we can give that work to, and they can cast their eye over it, you know, and maybe point out some things we don't see. So maybe we need to sort of peer review a AI's work going forward, and sort of we just see a little shift in the responsibility on documentation. Is that something that you maybe see happening?

Yulia Koserenko 15:49
Yeah, no, definitely, I think that's a great idea. And it probably even goes both ways, because we need to cross check what AI has produced. But also we can use AI to validate our own documentation. And I think of one company where I started my B career here in Canada, we had a really good system of peer reviews, we had a big large group of analysts, we had our rotation. And it was like a community to work like you had to review someone's work one a couple of times a month. Because as I'm writing it, I don't see my own contradictions, because I'm kind of deep in that medium. It's like similar to a, you know, discussion we just had whether it's okay to be a B and QA in a split roll. I know that sometimes happens. But if you think about it, I like my QA partner to catch my mistakes, right. And sometimes a good QA will say, hey, this doesn't make sense. So I can test them on. But if you're doing both roles, you don't see it, right? Because you just wrote it, you think it's total and same was peer reviews at AI can probably catch some contradictions and gaps also, or we probably can train it. Imagine if you have the tool that you are training, to look for certain things that you have that feedback loop going when you cross reference it, and then it's learning what you're looking for. So I think it's both ways we can definitely use it. I agree, we have to check things. And you know, as analysts, probably we aren't doing it anyway. But just having a someone else to do summarization, and then us review that. I think it would be huge, huge boost in terms of productivity, and also in helping us maybe see our own shortcomings.

Joe Newbert 17:38
Yeah. And it might also stop people saying, we need a scribe, where's the BA, you know, they can they can maybe find somebody else to do it?

What sort of, what sort of skills are we going to need going going forward? I'm sort of trying to think what you said earlier, I feel like you use the word creativity, but I'm not sure if I was replacing one of your words, with my words, you mentioned people at the beginning, and that we can get some support here. So if some of those things are taken away from us, what sort of skills are kind of come to the fore? Are they skills that we've already got? Or are there going to be some new skills that we need to develop as well?

Yulia Koserenko 18:33
I'm sure there will be new skills, we will need to develop some of them, we may realize some of them, we may not realize yet, we just have to be, I guess constantly assessing it. Definitely, I guess the skill of using AI tools is a skill, we need to practice it, it's on my long list of things to practice to kind of figure out how I can do it. Um, I think one of the skills we already have that I want to emphasize, I believe we will need it going forward until AI can do it. And I don't know why it doesn't do it yet, is modeling is creating models and diagrams, because AI is not good at creating pictures. And I've read some interesting experiments done about it and you know, pretty big fields, so to speak. Because I think that ability to draw on screen to make a simple picture, as you're saying you're brainstorming or you're trying to understand the context of a problem or just join the mind map. It is still a mostly human ability. So until AI learns how to do it even better, and I'll believe it when they see it. I think that becomes a very, very critical issue because if you think about anything that we can express in language, whether we write requirements over the we put user stories in the backlog or whether we get AI to summarize, you know, 200 via hours of videos for us, it's still expressed as language. And whenever we have large volumes of language for our brain, it's very hard to understand the structure of all that content to understand the interconnection between pieces, even if there are headings and sub headings still. And most of all, I think it's hard for us to assess validity and completeness and contradictions when it's just a lot of text, our brains, and also AI brains, I don't think always can catch up. So creating diagrams and models help us to establish connections between pieces and see the structure better, and determine if something is missing. Because once you put it in a picture, you suddenly say, Oh, how about this, there seems to be an empty space here. So that ability to model not only is still important, but I think becomes even more important for us in business analysis process. Because, as we always needed it, we needed even more now because of the complexity of the problems we are facing. And also because sometimes there's taxes just becomes overwhelming, you know?

Joe Newbert 21:16
Yeah, I'm glad you pulled it back to modeling, after you spoke last at about three questions in my head, and I probably went with the wrong one into skill. So I'm actually going to pull it back a little bit to modeling. So it's gonna say to you from that documentation that you were talking about, like handing that documentation over, I was thinking quite narrowly, but quite deliberately, narrowly, about requirements, requirements, documents, user stories, that kind of like paperwork. But I do think there is opportunity to get into modeling, whether it's a data model or a process model, you lifted it up a bit bit higher to that sort of initial sort of rich picture, mind map kind of stuff. But if I think about analysis, there's sort of two main parts to it. I know I'm going to summarize this probably, too, simply for many people now. But there's logic. And there's creativity. Those are essentially the two parts of it. And you were mentioning at the beginning, that it was that logical approach that problem solving sort of attracted you to get into the role. And I do feel like AI can probably play a part with logic, a cane, because logic is like maths, in a way. And something like aI should be able to do engineering structures where there's fixed rules and guidelines, as long as it's got those rules, and it knows those dependencies in it. And it knows the language to speak in back. So do you, do you think we could maybe reach a point where some of those diagrams that we typically produce as part of systems analysis, are getting initial straw man versions? Delivered by AI?

Yulia Koserenko 23:04
I still don't think it's possible. I would never want to say no, because I don't truly know all the capabilities of AI and probably you don't know. So I believe that we will experiment with me Sure. Because anything that we can, you know, make create to make us more productive, should be useful. Probably be can separate, you know, that creativity, as you talked about rich pictures, that level of creativity comes from us coming up with analogies, or maybe some cultural representations, right? So choosing a picture, it was like synonyms, it's like using figures of speech. Sometimes, we're still more proficient than AI because of the you know, like, maybe we have a little private joke in our company. And this is how the usual drill thinks. But I think most structured models, and I'm thinking more about systems analysis and design, probably things like data models, I mean, hopefully, I know he can generate autogenerate Yardies. But go in a little into conceptual modeling, wouldn't be nice if we could analyze, say, and a whole bunch of knowledge based information like user manuals, instructions ever smells, and generate the conceptual model out of that. It probably is realistic, I certainly would be interested in seeing how it develops. And again, maybe just as a tool, and then you look at it and you validate it and you add things to it, or maybe you reconnect things or rename things, but it might actually point you to something you are missing because as humans, I think, just because of limitations in our processing capacity, we can hold too many things in our brains. That's that's why we Yeah, so it's a very complex landscape and For example, if the timing is short, and if you need to, or you know, I was just one of my MLST, my previous meeting was just mentioned, hey, what do I do if I get involved? She gets assigned to any project, when almost all design is done, and they're getting into testing? How do I quickly catch up? Wouldn't be nice if she had a mind map that would help her grab the right knowledge at the right time. So I think they're getting actually jaw more to knowledge management. Yeah. Which I think requirements management is a very good foundation to build knowledge management for an enterprise on. So AI, I think is a very good tool to experiment with knowledge management, because it's another thing just like documentation that organizations say they want to have. And nobody spends time on, because it's hard to prove the ROI. And then you end up with a lot of knowledge, and all in the brains of people and in the code, and none of it in an easily accessible way.

Joe Newbert 26:00
Yeah, yeah. And it's always done later on when somebody actually needs to know that for some future implementation. I also think, you know, part of what we do is about coming up with solution solution options. They're in a bit of an English phrase here, but there are many ways to skin a cat. I don't know if you're familiar with that phrase. But there are different options. So I feel like as well, because of this logic, we could throw some stuff at it. And you could come up with different permutations, different versions of things. And then that's something for us to, to sift and sort through. But let's come to this word human. I feel like we've we've perhaps covered the machine world for now. Yeah, I think there's a lot about background research that can be done with AI, I think it can take away a lot of the grunt work, as you highlighted there. It's sort of consolidation of stuff that's already known. That's in the past. That's knowledge that's not new. And part of the traits of the human is the ability to come up with some new that creative stuff. Doo doo doo. See, and you talked earlier about, you know, getting closer and closer to the face of the business. How do you see the BA role evolving? Do you see it changing? Do you see our responsibilities growing when it comes to business when it comes up to creativity?

Yulia Koserenko 27:29
Yeah, it's it's an interesting question. And, you know, sometimes I see business analysts, becoming too distant from business. And I have this picture that I used in one of my articles of just peaks on the sky scrapers above the clouds, and you're somewhere up there. And you have very vague idea of what is truly happening in business. And I think that sometimes our projects fail. And our requirements don't hit the mark, when we're too far away. Or we, we expect product owner to know everything, and that product owner has been on the ground five years ago. So I think that the further we are from the real from the trenches from the field from the manufacturing floor, the harder it is for us to see. So we also like aI become just processors of pre processed information that someone collected for us. So perhaps this is where our because if you think about it, we can gather new information with all of our senses, right? We have the auditory senses, we can see things we can listen, maybe we are going to be more like investigative reporters that actually go and see what's happening. Because if you think what's happening with the news today, you know, a few reporters report on the news and then 300 different new sanctions just reproduce the same news from secondary information. So maybe that observing or that immersion, or talking to customers, is what is needed to bring fresh data into this world of because yeah, we will have lots of tools to process the data. But if you don't feel it was fresh data was new ideas, which is going to be kind of in the rock and just spinning our wheels in place. So I think that definitely may be one of the directions is being more observer and experimenter. And I think adults what allows you to maintain that human connection with people other than just sitting somewhere in the office and, you know, reviewing information. On the other hand, I mean, we should be using data more. So that's another interesting I think development for the future. If you think of 10 years ago, 20 years ago, whenever you ask business analysts would do the Do the first thing they mentioned would be

Joe Newbert 30:02
process models, right? Yep, exactly.

Yulia Koserenko 30:06
Up mmm, I create process models, which is, which is very important, we still do that, we still need to do that. But if you think about it, we develop process models from, from the words of other people from secondary information, or maybe from observation. And we were observing just one of the many possible paths. So things like process mining, for example, can completely change how you look at process modeling, and process analysis. Or instead of us describing using words, if then else and all the decision points in the process, maybe we are going to start using embedded analytics more and get data. So I think we can use some of the abilities to use data instead of just these, like logical derivation rules to completely change how we manage some of this process optimization. So it's interesting, right on one side, be used more and more tools and technology and data. On the other side, we don't want to lose that human connection aspect. I think that will be very dangerous.

Joe Newbert 31:12
But it is. And as you're speaking, I thinking that that's really the risk that you're highlighting, if we if we sort of come back to the to one of the questions that's probably on people's lips at the moment, it's, you know, will AI replace my, my job? You know, am I at risk of being automated by AI? And as you're talking? I'm realizing the answer to that is yes. If you don't bring any human elements into your work, as you say, if you're just that person in the back office, you're sort of processing stuff, you're that knowledge worker, then yes, there is a chance that that part of your job is at risk. And unless you start to get more customer focus more at the coalface getting out, off your bum, behind the desk, onto the shop floor, then then there's a chance

Yulia Koserenko 32:05
there was a chance here, and maybe that's where he can go and go deviate a little bit into decision support. Because truly, I mean, we need AI, we need data, and we need business analysis. A lot of times truly, what is the outcome of that is making better business decisions? Yeah. So. And that, I guess, the reason I also thought about it is because of bias, because if you think about what else have you need humans for when we use AI is we need to constantly be monitoring for bias. Because AI and machine learning is a little bit like self fulfilling prophecy of keeps trending towards the average or the larger group. Yeah. And that's models become bias, will who is going to monitor for bias and how we will monitor it, maybe that's why we need humans and high critical thinking, to constantly monitor and evaluate our any models that be used for biases for basically discriminating against exceptional cases, there is a an excellent book on the topic, it's called weapons of mass destruction, which really looks into this exceptional cases how sometimes the model on average performs really well, but it control it hurts some individual people that just don't fit it. So I think that if we think about decision, augmentation versus decision automation, this is where we need humans, where AI may be capable of solving, say, 80% of straightforward cases, but we need to be able to segregate the cases that need review that need human review, because maybe if we automate that they will be fallen to the bias of the model. So somehow, we need to make sure that that monitoring and constant adjustment to avoid bias to use new information, it needs to be managed by people who are, let me say smarter than Yeah. And maybe that's where business analysts can stand is identifying when beneath human value, for example, you know, if you're a judge, how much fidelity is good enough for you to allow AI to make a decision in a court case? Is it 99%? Or is it 95%? Or is it 90%? Or is it 99.9? And who is going to look under the borderline cases. So same, I think was business decisions. And I think it's just worse a lot of research to identify, identify, which part of I guess the the automation is valid, and where do you need to pay even more attention because of the tendency of AI models to fall victim to bias?

Joe Newbert 34:58
Yeah, I agree it's sort of it's based on what it's based on. So it's like bias in bias out a bit like garbage in garbage out. Right. So some of the questions that we need to be asking is, what is the background of this particular AI tool? Where, what's its foundation? Where did it get its information from? And also making sure that when we asked that question, we're not asking that question with a bias, that we're not looking for particular sources that we got some diversity in there that it's holistic, that it's inclusive. Yes. And augmented decisions, business intelligence, that kind of thing, because we don't want to necessarily leave those decisions to AI because I guess some people might be looking to AI and tools to give them decisions. And it's almost like, I mean, I've always felt that BAs are a little bit fearful of data, usually in the SDLC phase, when it comes to modeling it, as you say, we're sort of I do the processes, I perhaps don't step into into data as much and now that that responsibility is extending, isn't it sort of beyond the product in order to support the business? Yeah, I think that'd be really good. Let's, let's come back a bit sort of next question. Following on from before you talked about, maybe getting up from behind the desk going out in the cold face? What do you think a typical, I know, there's no such thing as a typical day, because every day is never the same for a business analyst. But what might a typical week or month be like for for a BA?

Yulia Koserenko 36:49
Yeah, I'm definitely much less sitting at the desk, for sure, at all the typing and review and documentation, I think, is changing drastically. So perhaps all of us will have our own AI assistant that we can, you know, talk to as we are going about and observing and communicating. Or maybe even who knows, maybe at some point AI can take videos and analyze the content of that. Probably not unrealistic. So I think a we are going to the places I guess where we can gather new information, which could be talking to real people talking to experts. You know, maybe you are, I don't know going to the store, going to the supermarket. or talking to experts, if it honestly sounds a little bit like investigative journalism, in a way, and communication with people. So that facilitation aspect, engaging people at perhaps we don't have traditional mediums where everyone sits and looks at the PowerPoint, that's great. Hopefully, that will be a thing of the past, yes. But you still need to create discussions. So we talked about solutions option a little right. And this is where sometimes you need that creativity, you need to look at your problem from a very different angle to come up with a different solution. So maybe that brainstorming aspect, or talking to your stakeholders trying to truly understand the essence of their problem, to understand the root causes, or explain to them, the results, for example of preliminary analysis to help them you know, generate the next level of questions or give you more information. So I think it's still a little bit sounds like that translation of technology outputs for business. I think what probably will change I hope what will change is the level at which bees will communicate, I hope they will communicate much more with middle and senior executives. Because today I think there is a tendency that BAs in order take care pm will tell them what to do, they'll just go ahead and execute it. But PMS don't necessarily it's not their job to get into the essence of business problems, right. That's what bees do. So bees, hopefully we'll be talking more to say executive sponsor to interview them to truly understand the, I guess the essence of that problem or the opportunity that they want to capitalize on and on the other hand, become more of advisors to their business sponsors rather than order takers because they understand the capabilities of technology better. They understand the landscape of the company, the complexity, the different projects, because of course they talked As business analysts, they exchange knowledge and information, they know what's going on. So they can help visit their sponsors to formulate better requirements, because they advise them. So they, I think the role of internal consultant should be more on our mind, not just someone who runs around capturing user stories.

Joe Newbert 40:21
Yeah. internal management consultant, trusted adviser, definitely and almost think not, not just necessarily helping to shape requirements, but to shape goals, shape objectives, to, to unpack the critical success factors, you know, the KPIs that, that support them and really drive out some of that strategic stuff. Because it's about business change, isn't it, it's the end in mind. And the end in mind is increasing revenue, more customers, more customers giving repeat business. And it'd be wonderful if we, if we could take that I mean, a phrase I've used in the past. And it's more of a mantra, perhaps that I, by repeating may come true, rather than a reflection of how things are right now. But I feel like bas could be the CEOs army, you know, unleashed on the organization to go forth to have a look around, and to come up with some of those creative ideas.

Yulia Koserenko 41:21
Yeah, no, absolutely. I like it. It's like the, you know, the agent that, you know, you inject in your blood and then goes around and checks every Senri.

Joe Newbert 41:33
Yes, like that. And a word that's possibly not been set yet, but I think it's coming through in order to do that. inquisitiveness, curiosity. writes, we've got to want to poker knows it.

Yulia Koserenko 41:51
Yes, yes, we want to be want to get that primary information, right the to do the research, to ask questions and to, I guess, not be afraid of what we can find. Because the challenge was that is you're gonna remake find the skeleton, or you may find a bigger problem than what you expect that so I think that inquisitiveness comes along was, you know, you need a little bit of a bravery to set maybe against the group saying, and standing up, if you discovered some stuff, and you disagree, maybe was a direction or you know, how sometimes projects, this code gets cascaded down from above, but maybe what you discover invalidate some of it or require that it's expanded. And it's sometimes scary to speak up. So I think that that internal advisor role also is to help you be able to be more vocal, because based on the nature of your work, you can, you can actually discover some strange or interesting or important things faster than anybody else. Because you're, you're digging around, right, you're like, you're poking your microphone, everywhere you're going, you're looking into things, opening up the dust bins, and so on. So you may be discovering things just like journalist or investigator, but what are you going to do with those discoveries, because they may be important. So I think also being out, I guess, more brave, and maybe that's part of what leaders and industry need to keep supporting that. So that doesn't stand out. So I know a little bit more empowered, and not scared to do that. Because eventually it it is all for the benefit. And I think that if I were to try and I guess summarize what analysts need to be striving for, it's, it's to reduce waste. I mean, you think about the typical organization, there is so much waste for various reasons, political, historical, economical, but we are always steal resources and money in time everywhere. And that's not a good thing for us as humanity. So I think business analysts are in a good position to try and help reduce waste in businesses which, you know, internal political help everybody else.

Joe Newbert 44:13
Yeah, no, it will. And I feel like there's some irony in there because organizations are about trying to get rid of waste, but at the same time, they actually create alternative waste different waste in parallel.

Yulia Koserenko 44:32
They do Yeah, it's it. Waste is very difficult to get

Joe Newbert 44:38
very, we come up with new creative ways of making waste once we've eradicated the last lot, a lot of waste. So inquisitiveness, being that investigator almost like a detective. I'm thinking we're looking for evidence, right? You've talked about data. So data is going to be evidence of certain things and Facts. You've talked about viewpoints, different perspectives, but then also the need to get many people's perspective. So we don't just rely on that one particular person's view on things. It's about being collective. Reducing why so I think you've had a great dream today for for business analysis.

Yulia Koserenko 45:24
I will continue Tindering.

Joe Newbert 45:26
Yeah, we must. I think we must, because it's through having aspirations of these dreams that I think we are going to be affects some change in his profession.

Yulia Koserenko 45:38
Yeah, and this is why we are all doing what we are doing right. We are speaking and writing and talking about how business analysis should involve this is what?

Joe Newbert 45:49
Yeah, nice, fantastic. And I think that's a great note to end on that we need to reduce waste and just make this profession better and make it better for the organizations and the customers that we do it for. So okay, I think so. Well, thank you for being here on the problem a year earlier. It's been a lot of fun. It's been great talking with you.

Yulia Koserenko 46:13
Thank you, Joe. Great questions, and I enjoyed our chat. And hopefully some of these things will come to some.

Joe Newbert 46:21
I'm sure they will. I wish you the best and growing your business analysts future and those of the people that you help to thank you. Thank you.

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About Yulia Kosarenko

Yulia Kosarenko (in/yulia-kosarenko/) is the owner of Why Change Consulting Inc and is a Professor at Humber College in Toronto. She is the author of the book Business Analyst: A Profession and a Mindset, a business analysis enthusiast, conference speaker, consultant and trainer. Yulia encourages her business analytics students to learn about business analysis and business analysts to learn about data, data analysis techniques, and business analytics. 

About Joe Newbert

Joe Newbert (/joenewbert) is is a consultant, a writer, a speaker, but above all, a teacher. As Chief Training Officer at Business Change Academy, he delivers some of the best business analysis training on the planet. He co-authored the original IIBAยฎ Business Analysis Competency Model and served as Non-Executive Director on the IIBAยฎ South Africa Strategy Board. Joe is Showrunner at the business analysis podcast network OneSixEight FM as well as Editor-In-Chief at the Inter-View Report. And he also writes in fits and starts at Newbert's Blog.


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