Интервью с BI-экспертом: путь в IT аналитику и секреты профессии | IT Baza Podcast

There arises a certain off-site event. People travel out of town together. In the regions, in large companies, it is easier to find than in Moscow. Is it worth going there? I think so! The probability of success, it increases very, very many times. We have observed this. Hello everyone. We are on the IT Baza channel. We examine career paths here, look at the IT field, and invite the most interesting guests. And today we will have a very interesting episode. Today we will talk about Business Intelligence BI. Subscribe to our channel. Be sure to write any comments in the comments, negative, positive. Give likes if you like what we do. So, let me give you a little context, what BI or Business Intelligence is. Let's imagine an enterprise. There is a "zoo" of systems separately, there are accounting systems separately, there are warehouse accounting systems, there are customer relationship management systems CRM systems, and so on. Systems can be different, narrow-focused, some applied. And sooner or later, the owners or top management have a need to see the whole picture for the entire enterprise, regardless of whether it is small or large. This is where a whole host of problems begin. Because it is necessary to collect data from each system about clients, shipments, warehouses, about some positions. And this is where a powerful request arises for a system that would combine all indicators, all dimensions, collect all reports into one, give a centralized picture. And even better, if it were beautiful graphs or, as they are also called, dashboards, which would show us the health of our company. And today we have a very interesting person with us, an expert, Stas, who has been implementing such systems for more than a decade. Stas, thank you for coming to us. And today we will question you on the topic of what BI is and how to implement it correctly, for whom it is in demand, for whom it is relevant, for whom not so much. Well, first of all, how did you end up in this particular specialty? How did your choice happen at all? Uh-huh. Hello everyone. Uh, well, I ended up in this specialization almost by accident. So it's been almost 20 years. In 2006, while in my last year of university, I decided to start my career in earnest, so to speak. I joined a company that was then one of the promising consulting companies. It was a training ground for кадров, a team that dealt with incidents, yes, that is, with analyzing errors in data warehouses. I moved there first, well, and then, a week later, I was offered to move to the team that, well, dealt with reporting, let's say. At that time, naturally, I didn't understand the difference, nor the essence of the work. What kind of reports for whom. So, it was just interesting. What was the client? It was a leading telecom. Uh-huh. Yes. Well, in principle, there are probably no secrets. Here. Well, and accordingly, at that time, there was an active phase of building a data warehouse, and reporting on it, yes, reporting that covered all areas and directions of business. Well, and accordingly, by the will of fate, let's say, I said: "Why not? Let's try reporting." Here. Here. That's how it is. So, speaking in simple terms, I understand that Beeline, it probably has hundreds of systems of all kinds, right? Well, the main ones, of course, billing systems, inter-operator settlement systems, and so on. Do I understand correctly that architecturally it looked like this? That is, there are systems, they collect all data into a single centralized data warehouse, and then, based on the collected aggregated data, you build visualizations. These were such projects, yes, of this kind, if we are talking about the term BI at all, then people can interpret it in two broad definitions, perhaps. The first is, in a broad sense, when we include data selection from all possible systems that we have in the organization's landscape in this concept. We collect, as a rule, in a data warehouse, specialized tools for storing, for collecting this data. Well, and, of course, a separate specialized tool for visualizing this data, yes. Here. Well, accordingly, in a narrower sense, when we consider the class of BI systems, these are the platforms, the software that allows you to build some visual picture over various data sources, or in tabular form, in general, to provide that very report, figures, indicators understandable for business, yes, like. Uh-huh. I've prepared a small chronological note here. Historical, yes, historical excursion. Historical excursion. I'll read it to you now. Actually, BI is not an innovative topic. In the distant year of 1865, when the English writer Richard Miller Davenport used the term intelligence in his work. By the way, intelligence is an interesting word, data reconnaissance, yes, well, essentially, yes, essentially, actually, if we look at the essence of what is called Business Intelligence, it is, well, like, uncovering, recognizing reconnaissance in data, yes, that is, their deep, that is, identification, deep analysis, essentially. Here. And in general, then it meant rather intuitive market understanding, and already in the 1950s, large corporations began to use the first electronic accounting and processing systems. At that time, there were no personal computers yet, there were the largest mainframes. And then ABI was, in essence, statistics and accounting reports. And here, perhaps, Stas says that nothing has changed since then, like, yes. Here. Well, well, you can joke about it, yes. You can joke about it, yes. But in reality, of course, the devil is in the details. In particular, in the seventies, relational databases and the SQL language appeared. This allowed storing large arrays of information, well, and working with them efficiently, yes. So this is, you could say, one of the breakthroughs – the emergence of this SQL language. Well, and, of course, relational, convenient, fast databases. And, accordingly, in 1989, Howard Dresner, I think, from Gartner. Gartner is a rating agency, like on the standard course market and so on, and this is, well, at least I encounter Gartner more often in the IT sphere. Maybe they conduct some other ratings, but I often see them in IT. These famous Gartner squares, yes. And it was Howard Dresner from Gartner who introduced the modern definition of BI as a concept and methodology for improving, pay attention, business decision-making using facts collected in reports and analyses. Here, by the way, this is a very strong argument in favor of BI systems, because any decision must always be based, first and foremost, on reliable data. And here, as it were, this concept sounded more clearly. And in the nineties, the first BI platforms in the modern sense appeared. That is, Cognos from IBM, Business Objects from SAP. Well, they were separate before, and then they were bought out, yes, by the German SAP, MicroStrategy, and later Oracle joined them. And Oracle BI specifically is not a niche solution, well, they also, how to say, bought startups, what is called now, yes, and implemented it, well, under their flag, using their server applications, well, WebLogic in this case. These solutions already allow collecting data from different sources, generating reports and charts. Well, to put it very simply. In the 2000s, there was a growth in popularity, primarily in the West. This is visualization in the form of graphs and so-called self-service, well, still in a limited volume, perhaps, when users, not versed in SQL, can nevertheless, with the help of applied tools, build graphs and reports as they need them. Well, yes. So the first phase, this was precisely in the 2000s, was to give tools to ordinary users, well, who don't have technical skills. Well, at least, as is commonly said now, to create Excel pivot tables, that is, to change the level of detail, apply filters, well, that is, to look at indicators in different, that is, aggregated information in different dimensions, essentially, as they also say, multidimensional cubes, yes, in fact, this term OLAP, which is from the nineties, yes, this is actually from the perspective of the interface, it was transferred to the systems of the 2000s, like, yes, to give the user the opportunity to easily and simply look at data from different angles. Here. Well, as it implies. Well, that's essentially it. And why is this needed? This is needed precisely to, among other things, identify cause-and-effect relationships. Let's imagine you have a top-level report on revenue from various branches or divisions of the enterprise. You can further add a dimension. This is revenue by employees, to identify the best employees. That is, not just to understand which division has the most revenue, for example, but also which employee brought it, through clients, through attracting clients, brought that very revenue. Well, you are now, in fact, giving an example of what, well, happened in the 2000s, yes, that is, the tools appeared when the user himself can look at the data from different angles, with different aggregations, yes, like at the same revenue indicator, well, filter out the top, best, worst, filter, select a specific branch, and so on. That is, all this, conditionally, was supposed to be done in two clicks without any IT knowledge, yes. And it would seem that we have achieved happiness, but no. In 2010, BI moved to the cloud, integrated with machine learning and artificial intelligence, open-source solutions like Superset, Metabase appeared. The focus is increasingly shifting to self-service, that is, to self-service. More degrees of freedom are given to data analysts. Moreover, predictive analytics, forecasting are also actively developing, models are developing, all sorts of models that allow not just to build reports on, well, let's say, on retrospect, on past events, but also allow to create reports, what if, that is, when various hypotheses are applied, and based on these hypotheses, reports are built, for example, what will happen if we increase prices, lower prices, where will we lose, where will we increase revenue. Predictive analytics, yes, that's what it's called. Let's go back to your career path and your projects. What happened next, besides VimpelCom? And what else was interesting at VimpelCom? Well, it's Beeline. I'll probably say that a lot of interesting things were happening at VimpelCom then. That is, then, I won't be afraid to say it, the system that we built, being still young and inexperienced people, it, well, it served for many, many years. That is, if we are now talking about data warehouses or complex systems and data warehouses, yes, and reporting systems. That is, in fact, as you were just saying, in the 2010s, this trend for big data started, that is, the Hadoop ecosystem, data, as it's called, was actively developing. Here. Well, that is, the term self-service appeared, which specifically in relation to BI platforms, at this stage of development, meant, well, let's say, giving the user the opportunity not just to present ready-made data, but also to give them the opportunity to independently, without involving specialists, load data into the system, yes? That is, we, roughly speaking, have an understanding that we have, say, three "vitrines" in five databases, well, or vice versa. Here. And we must, accordingly, collect them, that is, there is an enterprise or corporation within the perimeter, there are some of its own systems, and databases, yes, which all... And a data warehouse can also be filled with external sources, for example, in the case of banks, it could be a credit history bureau, some requests. Here I'm talking about something else. That is, the eternal problem, because of which, in fact, this functionality was born, which later acquired the name self-service, BI, well, and then self-service ETL, they even started calling it. That is, it's, roughly speaking, the IT service, yes, which uploads huge, complex, new data from various sources into the warehouse, is always late for the constantly changing business requirements. It means, for example, connecting any new sources. Well, because, roughly speaking, we have a new product, we have a new accounting system, that is, well, roughly speaking, physically it's a new database. By the time we put this data into the warehouse according to all procedures, build a metamodel over it, it usually takes a significant period of time. Well, it has always been like this and even now remains. It is difficult, long, and even expensive for business to wait this time, yes? That is, they, roughly speaking, launch a product, and they immediately need to measure its effectiveness. Hence the need arose to provide a tool that would allow business to take general data from the data warehouse and add some external source, external to the warehouse, and combine it all in visualization in such a way that, well, essentially, to make conclusions, to gain that very business intelligence, to understand how your decision, for example, about launching a new product, yes, how it affected, I don't know, the company's revenue, whether the product is effective or not effective. You know, a visual image immediately comes to mind. It's like an octopus, the head of which is, in essence, graphs, charts, and some pivot tables. Its tentacles are from where, well, like, you know, from where it pulls data, yes, and as you say, these tentacles can constantly increase when a new data source is added and enriches our consolidated information. Essentially, yes. And from here, by the way, comes the next pain that IT experienced after we released this octopus into the wild, so to speak, yes. It's to establish organizational control that will allow this octopus not to grow too much, so as not to harm the systems themselves. After the launch of self-service analytics, yes, these products, this problem arose. It's, well, Gartner wrote about it in its reports, it's the so-called decentralized BI, that is, when the IT services, in essence, no longer control what business users are doing, yes, and this becomes a disaster. Well, and we, accordingly, moved, well, essentially, back to that cycle when, in these flexible tools for independent analysis, control and access restriction mechanisms begin to be introduced. In order not to, we don't overload the servers with requests like: "Show me all clients," so that we don't overload the servers and send them to... Well, and here there are two aspects, as it were. The first, as you correctly said, is that we must fit within certain limits in terms of performance, yes, not to overload systems, and so on. And the second is the security aspect. This is also a very important moment now, yes, because, naturally, if everyone is given access to go everywhere, then at some point this data may go somewhere wrong. If before the pendulum swung towards, give more freedom for analysis, for data extraction, then now a trend is observed that since personal data has started to leak, very powerful restrictions are now being imposed on personal data. And essentially, the capabilities of a data analyst are now narrowing. That is, they can see some consolidated information. And data, for example, on accounts, on cards, are either masked, that is, or depersonalization of customer data occurs, that is, when, for example, instead of Ivanov, Sidorov or some, well, especially in the financial sphere, yes, it's very, yes, it's what's related to finance, to deals. That is, if we look at the picture of the enterprise, please, there are no personal details, there are branches and so on, but indeed, more and more restrictions are being imposed now that are related to trade secrets and personal data. And I would generally say that the trend, it's like, it has swung back and forth. That is, first we wanted to move away from flat reports and give users graphs, it seemed like we gave controlled access. Then, accordingly, this functionality appeared. Do whatever you want, we won't keep up with you anyway. Well, then, roughly speaking, the user created uncontrolled chaos, and the pendulum swung back in the other direction. And these cycles, it actually feels like they fit into approximately 7-year periods. You can estimate it like that. Well, by the way, there is another problem here. In different systems, there is data about clients. That is, CRM stores one piece of data about a client. Then some system, well, if, for example, it's a bank, it's some kind of banking system or a card system, stores, well, the same data about the client, but phones may already differ, the address appears somewhere, and master data management systems arise, yes, which should standardize data. This also happens before reporting, I understand. This is, well, at the level of building the data warehouse itself. Of course, if we have a large organization, then this unification of common dimensions, if we speak in terms of BI platforms, is a very important aspect, because, well, roughly speaking, if you have, I don't know, branches named one way in one accounting system and differently in another, then even if you take BI or, I don't know, SQM, you will still have to do what is called hardcoding, yes, that is, this translation of values. Here. But for this, MDM systems have long been invented, and they are, well, for BI platforms, probably a very important part of the corporate IT landscape, when we talk about creating data warehouses, MDM systems are very important, because they allow us to unify all the main reference books. And reference books are essentially the dimensions of any BI platform, yes? That is, within, well, like a coordinate system, essentially, for the metamodels of BI platforms. That is, in order to build complete, good, centralized reporting in an enterprise, you need to do, if we speak in broad strokes, yes. First, build a warehouse that will, like an octopus, collect data from all sources, from the warehouse, from accounting, from CRM systems, and so on. The second stage, accordingly, is at the stage of collecting data in the warehouse, to do standardization. Standardization, cleaning, transformation, yes, cleaning, yes, data quality check, so that there are no distortions, and so on. That is, reliability is checked at the data preparation stage. This takes a certain amount of time, it can take, yes, even days, as a rule, this nightly load, and now everyone needs speed, that is, online analytics should be, yes, that's it, well, that is, this is what requires very high capacity. And only then, essentially, as the third stage, we do visualization over the warehouse. That is, when we have already aggregated the data monthly or weekly or the price, then we can build visualization. We can already build. Well, there is such an interesting point here. That is, as I said, self-service was born from the fact that business needed it as quickly as possible. So, in fact, analytics itself, yes, it, well, not now, in the sense, this trend has been there for 10 years in the West and at least, well, that is, before 2020, it started with us, it's the desire to get data, well, essentially online. Yes, that is, this led to the birth, well, essentially, virtual data marts, virtual data warehouses. This led to systems like Trino, which allow not to materialize, not to collect all data, as in the classic genre, in one warehouse, yes, but essentially, and the BI platform through this so-called Trino, on the fly, to collect data, unify it and aggregate it, well, and, accordingly, present it in visual form. Well, that is, essentially, you mean that building reports can be multi-level. First, we collect certain detailed "vitrines", well, essentially, collected data, yes, when we talk about a "vitrine". Well, let's do it, for example, so, certain aggregated data, then over the "vitrine" we can do further aggregation. This is the classic approach, essentially. That is, what in principle has been proven over many years, when we can indeed get, well, what is called, reliable data, yes, get it, when everything is verified. Here. But as I already say, yes, the business needs are such that they need analytics, well, almost online. This immediately leads to a whole list of risks, yes, that is, it's both access control, and control of overloading the source systems with their incorrect requests. Well, and to the growth, let's say, of capacities that are necessary, well, to implement, to get that very analytics in this very pseudo-online mode. Well, and, frankly speaking, probably, despite the fact that it is very seriously advertised by many, well, vendors, this trend started even before 2020, well, we can probably only achieve this now by sacrificing, essentially, data quality. That is, if, well, we all use, let's say, mobile applications, yes? For example, we look, we open, say, online banking, yes, we look at our transactions, transaction history, yes, we sometimes want to look. And this is also a high-load system, especially if we imagine, for example, taking any bank, even a medium-sized one, yes, it has at least, say, 100,000 clients, maybe even more. That is, this essentially means 100,000 users, yes, who use, well, a mobile application, which, in fact, should respond very quickly to their every action. Yes, here the response is also important, yes, the response is important, yes, the response time should be minimal, and on the go, well, even if they don't manipulate these multidimensional cubes themselves, they want to see some static reports about themselves, yes. Here. Well, and frankly speaking, many corporate reports are not much more complicated, yes, like. Well, here I'm just saying that you can't just fill it with powerful hardware. Here, the optimization of the algorithms themselves comes to the rescue, yes, some intermediate ones. Well, for example, we limit the user's ability to view history for the entire period, yes, well, the difference between, well, let's call it, business applications, which, in principle, work with a large data array, from BI analytics, from BI, essentially, from Business Intelligence, is that, well, as a user, you always look at, well, an indexed small piece in a large warehouse. There are, accordingly, techniques that allow you to retrieve this piece quite quickly from a large warehouse. Yes, when we talk about real BI analytics, we always imply that we are operating with a large company data array, that is, well, with many clients at once. Here. And here, other questions arise. That is, not just selecting, I don't know, conditionally, 100 rows out of a million. Here, questions arise, how quickly on the fly from different systems to get, roughly speaking, 100 million records from one, 500 million from another and aggregate them to 2,000 rows of the final result, which we can then build in the form of tables, charts. And this task, it is, well, much more, let's say, architecturally, it is more complex, yes? That is, the calculations themselves are much more complex here. Look, if we talk about industries, besides telecom, what else did you have? I started implementing, yes, with telecom, yes, and in this regard, Beeline, yes, it became a kind of foundation, where there were many cases, where I, in principle, from this romanticism, let's call it, yes, I started to understand quite quickly how the results of my work are used. And then, quite quickly, the understanding came that, in fact, a huge part of the functionality of BI platforms is not used. People mainly use, well, as a classic story, yes, I want to export to Excel, where to...

From the platform. Here. Well, and, accordingly, it was a very good, like, start. Well, and then further in the tens of years, let's say, there were many clients from the financial sector, the public sector too, so there were a number of projects. Well, and I mean by projects here such substantial ones, where there are at least, well, tens of users. He did something, because there were, of course, many pilots there, the pharma industry was there, and so on. Probably, essentially, the only sector with which I haven't managed to work, yes, the industry is manufacturing and, well, extractive industries. That's what is actually understandable in principle. I hope that's all. You had, it turns out, banks, telecom, retail, I understand, you had, yes, and also you had government agencies, tax monitoring was there, or am I something? Yes, well, that's like a mix of government and financial industry, essentially, yes. So, look, Stas, I understand that these are different, like, these are different products, meaning BI, they are all on the same topic, but the products were different. Can you list, roughly, well, the main BI products that you specifically implemented? Well, it all started with Business Objects, when it, in principle, is one of the, like, founders, let's say, of the entire direction, yes, the progenitor. Then SAP bought it, we already under, so to speak, after rebranding, under the SAP flag, those upgrades were made, and so on. There were also very exciting stories in my time. Then, well, as it turned out, the financial sector quite actively used it, and accordingly, we worked with it for a long time, yes, yes, we worked with it. And then in the early tens of years, we had a trend for iPads, yes. Well, by iPads, I mean mobile analytics, yes, so when the first tablet, so to speak, came out, everyone got infected with the idea that, yes, yes, yes, let's look at everything on a tablet. I recall, by the way, in 2000, I think it was eleven or twelve, I first saw SAP Business Objects on a tablet. They had a demo mode. And, by the way, they even later made a SAP Mobile branch themselves. Well, true, but it was very far behind the leader in this direction at that moment, the MicroStrategy platform. This is, well, let's say, also one of the founders of the BI theme. And, like, management always, in terms of product development, chose interesting, innovative things. And, accordingly, they were the first to guess this need for mobile analytics. And when this trend reached us, then, well, essentially, there was no choice, so to speak. Well, we choose the best, yes. And we, accordingly, got involved in this MicroStrategy topic, although, like, as a product, it was, well, not particularly popular in the Russian market. Well, we learned it quickly, we even had an exciting business trip for certification in Cologne, a city, yes. Yes. And, accordingly, we did several pilots in banking, retail, and telecom. With the goal, essentially, of implementing it for top management, but fully, there was always a stopper on security, yes, so it was very difficult to agree with. And what bothered them? That it's not an import-substituted product. No, no, the topic of import substitution, it wasn't even raised, so to speak. Well, what bothered them was, well, by the way, there's a funny story here too, that what always bothered infosec was that, well, like, a mobile device, you lose it, an attacker gets in and sees what the revenue is for your company. Well, there were stories that someone lost briefcases with paper reports, yes, and that's like. I recall the following story. We, in our time, wanted to offer it to one bank, Home Credit, in particular, it's not a secret now, when I worked at an integrator, we wanted to offer them SAP Mobile. And, among other things, yes, when we had working meetings, two things were considered. First, how to update when you have a local repository of your application, yes, and there is, for example, a group of users, for example, bank representatives, credit representatives, yes, who are scattered across shopping centers. And there were two problems. The first is how to update constantly. And the second is what happens if a person loses a tablet. Well, there was also a third point, if the connection is lost, there is also replication, but that's already a technical story. And I recall, yes, that they said that if a person loses a tablet, and it turns out that someone picked it up, they can look, break the password, and can see, including personal data, banking secrecy. At that time, mobile device management platforms were not yet developed, they were also just being introduced, yes? So, accordingly, well, they were indeed afraid of this topic. Afraid, yes. Well. And, well, therefore, all our ideas didn't manage to get started, but nevertheless, in one very large private bank, we managed to build a project there, well, at the very top, for the bank's board. Well, meaning it was successful, yes, this project took off, which, yes, this, well, this wonderful product was in the sense that it truly provided the opportunity at that moment to build very high-quality, beautiful, and user-friendly analytics on a tablet, or on this smartphone. Stas, well, we've now smoothly approached the topic called criteria for choosing a solution. Let's consider a case. I, for example, am the owner of a medium-sized business segment, yes? So, let's say, let's take some trading company, yes, I sell something wholesale, yes. I need to get, yes, I need to build BI reporting for myself, standardize, centralize data. So, by what criteria should I choose a BI platform? This is, actually, it seems like a simple question, but it's probably the most difficult. It's the most difficult not even for specialists, but for the customer themselves. Meaning, I need to formulate it myself, yes, what do I want from it, yes? This is probably the customer's question to themselves. So, if we even talk about people, like, who are more or less experienced and understand why they are doing this, because we actually very often had stories when people, well, like, it's fashionable to have BI, you know, so the association was BI – it's, well, building, drawing reports, yes? So, in reality, the essence of the term, the essence of what these systems can give to business, well, not many managers, like, yes, understood this moment. Well. And, accordingly, many actions, well, need, need, like, well, okay, let's implement it. Well, accordingly, here it appears, if you set such a criterion, well, there, simply formally, the tool must be able to build flat tables, charts of this type, of that type, then, in principle, all vendors have learned to put checkmarks on these points wonderfully. Well, and pass any selection criteria. But in reality, the question of which system to choose depends on how you will use it. And the customer needs to answer this question themselves. So, it's, how to say, the correct model is to gather a pilot group of key people who will, well, really drive this process, specifically understanding the business from data, and understand what to expect from it. When we gather a group, we talk about how we will implement it. But when we, for example, talk about selection, well, there, at least, there can be different license costs, different performance levels, technically. Understood. Well, for this, as it happens, BI specialists are needed, who, initially, these requirements, how I want to see my business in visual form, meaning how I want to click. Maybe it's enough for me to just export a table, maybe I want to upload data myself. So, all this is called, well, like, selection criteria. And having formed this pool of requirements, specifically from the business perspective, but conscious ones, not just "I want to build a report," but specifically "how I want to analyze my company's data," then here, a BI specialist can translate it into functional requirements for the BI platforms themselves. And I never tire of saying, in principle, well, there were quite a few such mini-projects for choosing BI platforms for customers, in which I participated, like, from the customer's side, yes. No, specifically, like, like, from the customer's side, yes, I researched and helped with the selection, yes. And, well, in principle, these stories always ended with the fact that if you don't do a pilot that more or less covers, say, 80% of typical cases, then you actually won't make a conscious choice. Well. Yes, this is then overlaid by factors of cost, infrastructure cost, specialist cost, and so on. Well. And, of course, the same medium-sized business. Here's a short answer to your question. If you really want to make very cool conclusions from data, then, well, from the products, from everything available, it's clear that it's Qlik, well, or Tableau. Well, possibly Power BI, if you have good substitution, for example, from domestic ones. Do I have a choice or is it difficult? Difficult, yes? No, there is a choice. As they joked in our Russian BI community, yes? Like, all specialists after the twenty-second year rushed to import-substitute BI products. Well, and, as they say, not a single day without a new Russian BI product. That's the story. But among them, there are actually, first of all, companies that have been involved in this topic for many years before that, meaning they at least have this, well, understanding of the topic, they have developments. They started earlier, they simply understand why and how it is used, yes. Well, like, BI platforms in general, accordingly, they can already set more or less specific tasks for developers, yes? Well, there are companies that, as they say, are on the hype. We just take ClickHouse under the hood, we take some Yandex database, Molochouse, yes, which, yes, yes. Which, well, they released it as open source, accordingly. Well. And, accordingly, on top, we take some beautiful visual framework, we go, we sell this beauty, like, yes, meaning, we have, roughly speaking, one dataset, one table, and one table, one query to ClickHouse returns quickly, business – it's all beautiful, so to speak, we grab this hot pie, well, and buy the system. But why am I saying this? To the point that you need to understand that, in general, if you truly evaluate BI platforms not as a tool that, like, generates some table, but it, in principle, probably consists of three such, well, big, huge modules. You can say it simply. The first is, well, the visual part, what is easily sold, like, how beautiful the system looks, how beautiful the charts it builds, and so on. Even bright dashboards, all this flexibility of settings, and so on. And the second, and what, actually, probably, our platforms fundamentally lacked, yes, why they all, even those who used their own developments earlier, switched to using ClickHouse as the core engine. Essentially, well, Click, in this case, it can be both a computational engine and an engine for temporary or permanent storage. Understood. Well. And the third point is metadata management. Well, meaning, in fact, repository management. So, this system. So, metadata is data about data, when we translate, for example, the name of a field in a database table into a business term, yes, for example, yes? Yes. So, here there is, like, a data model, yes? Well, meaning, these are the physical tables, what they contain. And we, naturally, a business user doesn't understand what the "revenue" table is, yes, and what the field "revenue_usd_prev" means in it, like. And for this, to translate, essentially, the repository performs the function of a dictionary for the user, who immediately sees in business terms what to combine with what, how to build a report, yes? Yes. So, this is called a metamodel. So, this is a model that, above the physical model, describes in terms understandable to users, meaning it gives him those very building blocks. That's why Business Objects, business objects – it was very good. They even had pictograms for designations. Why the theme, well, it caught on, meaning there was a measurement cube, blue, yes, like a measure, meaning calculated, they had metaphors for everything, yes, yes. So, it organically fit in, in principle, well, for users, business users who, well, don't know SQL. From this, they moved away, so a kind of mess started with an emphasis on the visual part. Meaning, who can hype whom with some stunning chart. But everyone forgot that there are these two other moments in the system. So, the first is to store and, well, compute data, because how can you compute? Either the source database does all the computations. Well, this is called pushdown, so to speak, of all queries. Or, take the data and do aggregation, do some computations, joins within the system itself. And the more complex the analytics, and the more scenarios of independent work in the BI platform are implied, the more complex this mechanism becomes, and then you start using your own languages, for example, like Business Objects, like Click, well, and Tableau, they have, like, Power BI, they all have, in principle, their own languages for additional data computation, let's say. Actually, the topic is very capacious. And I have one question. For example, when you go on a project, well, from a consulting company, you go to a customer's project, how best to build communication with them? Because you need to understand the person, you can show pictures endlessly, for example, example charts, and the person will say: "Not it." And you say: "Well, what do you need?" He says: "I don't know, but I, well, or explains, contradicts himself, and you can't reach an understanding." Is there any recipe for how to build all this? Well, it's a good question, and at the same time, it's a difficult one. Because there is no magic wand for this. Actually, there is one model – it's like, "Give me some flat report, I'll export it." A pilot, an experiment. As a rule, an experienced specialist already reads, like, the business user, how they will work. Meaning, are they interested, will they be able to get so carried away with this topic to really twist, study data, to draw some conclusions from it? And this is precisely what any self-service BI project allows you to instantly divide users into categories, who are, well, the initiative group, yes, and everyone else, for whom it is enough to simply export a strip report. So, we, roughly speaking, use BI in groups. So, there is mandatory reporting of a fixed form. Every month we receive a sales report, a revenue report, and so on. And there are those inquisitive ones who want to measure the customer base, active customers, yes, they want everything and immediately. It's more difficult with them, but it's interesting. Yes, well, it's always interesting, because you learn, well, as long as you understand what they need, you learn, in principle, the business, for the most part, because they start giving you various real-life cases of this business, and you start getting carried away with the topic yourself, and thinking about how to actually better, like, collect data and present it so that, well, it's convenient for them to build analytics. By the way, do you believe in these team-building events, trips out of town, sports events, meaning team building with customers? It worked, undoubtedly. The year 2020 changed everything. Before 2020, all this worked perfectly, I think. Yes, I think, well, actually, such a milestone, on the one hand, it gave, of course, the advantage of remote work, like, if we look from the IT specialists' side. But if we are still talking about projects, specifically, so that we don't do them formally, but so that they bring great benefit to the business, then, actually, it was better before. Meaning, it was actually better before, you mean understanding, yes? Yes. So, all these trips, meetings, when you, well, personally communicate with the person face-to-face, you, like, well, understand them, they understand you. Of course, here how can you not understand them, well, or interact poorly personally, there's one story. But at least, this chance, it, well, so, like, personal informal pastime, it gave an opportunity to get to know each other better, both from a human point of view and from a professional one, in order, well, to effectively solve a task. And probably, in the BI theme, this is especially important, because we are still talking about visual representation, about how a person, using pictures, essentially thinks, to do something useful with data. Essentially, thinks in images. Yes, thinks in images. Thinks in images, and you need to understand their way of thinking, of course. And when you, roughly speaking, sit in Zoom and communicate with a black screen, you, well, like, lose a large aspect of this humanity, to understand what they really need. So, they give you simple words: "I need a table, with five columns, and so on." But you will never understand why they need it. Well. And here, of course, the previous format gave, well, more opportunities for, well, accelerated understanding of the person. Well, and for greater efficiency. I also recall, such cases when, for example, well, sometimes contact doesn't arise, disloyal, essentially, two groups of people communicate. One are consultants, for example, yes, the other is from the customer's side. Well, sometimes people immediately don't accept something new and have some kind of negativity towards people who have to explain everything from scratch, and so on. Here, an off-site event arises. People go out of town together. They have fun there, yes, they compete together, they have a good time. They spend pleasant time together, they look at each other differently, and their interaction changes from minus to plus. And it becomes much easier, well, just a different level of communication arises after such a, after completely correct. And such stories, well, in my personal practice, they happened more than once, when tense relationships, let's say, or semi-tense relationships, after some such trips or just several meetings, they, well, changed drastically. You understood the person, they understood you, and you tuned in to achieve, well, the result. Simply because you started to understand the person humanly, and they, conversely, yes, and together you have one vector, a common excitement, like, and you, of course, immediately, the probability of success, it significantly, significantly increases. We observed this. I also ask myself the question now, after everyone was moved to remote work, I encountered the fact that, yes, in principle, communication can be built on experience, but the quality of these communications is not always, like, good. And this tool, unfortunately, I now observe it somewhere, but there has become much less of these team-building events. Yes, I agree. Team building. And how do you cope with this? Not at all. Listen, well, actually, classically, we cope. If we take our last, well, my, probably, yes, last, like, really cool project, then everything was done the old-fashioned way, despite it being 2021, they were still walking in masks recently, yes, like, in order for this self-service topic, which few believed in, including myself, in this large retailer, what did we do? We went to the people. Well, you went to the people at their office, yes? Moreover, we chose the office. Well, for some reason, it's also an interesting moment, if from the experience of, like, in many cases, why it's much more active. Well, at least, how to say, to find a truly enthusiastic person who will drive the entire BI process in the regions, in large companies, is easier than in Moscow. Only after some interesting story, some movement, comes from there, do people from headquarters, more skilled, more professional, connect and already, well, like, to the heavens, so to speak, bring this topic to a new level. Well. And we, accordingly, did just that. We just went to the people and personally asked them about their pain. They explained to us in different terms, including using Russian vocabulary, yes, how difficult it is for them to integrate all these pieces of data. What they dream of is just a tool. We listened to them carefully, we talked about, well, stop looking at data in tables, you need to, like, in charts, there are such, there are such, you can look like this, you can look like that. Well, and of course, we promised that we would make them a spaceship. What do you think, is it worth it now for, for example, a beginner IT specialist or a person from a related profession to pay attention to BI and generally to this expertise? It's worth going there, I think, yes, one of the advantages of this direction, I would probably call it multifaceted, meaning a specialist should be, at the same time, like, a business analyst, and a system analyst, and a developer, and a database developer. Meaning, knows, like, source dialects and is a bit of an architect. Like, and even delve a little into business expertise. This will allow you to grow very quickly, gain expertise, and essentially make a kind of career breakthrough in terms of, it's not even about a career breakthrough, but about the fact that you have, like, versatile development happening. There are projects where BI developers are perceived as, well, he should make reports conveyor-belt style. But if we, essentially, consider this direction fully, then a specialist is truly a universal person who should talk to people, understand what they need. Or, like, with business analysts from. By the way, if you were to hire someone for your team, what would you ask them? Like, I'm at an interview with you, what technical skills would you ask me? Gen, this is a difficult question, because it depends on the project goal, on the budget, on the project goal. Look, for example, you have an open vacancy, I'm coming to your team, what would you ask me, SQL or would you ask me SQL? Undoubtedly, yes. Meaning, you can't do without it. So, if we teach the system, then it was also important to have a technical understanding of how, well, a processor works, why this is also a topic for, well, for an expert level BI, it's important. Meaning, if you want to build a truly high-quality, reliable product, then it's not enough to just, well, write SQL, and that's it. Well, or like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, 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like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, like, 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