Бесплатный фрагмент - Digital Transformation for Chiefs and Owners. Volume 1. Immersion

Foreword

Greetings, dear reader! My name is Jimsher. My managerial experience accounts for more than 10 years, 8 of which have taken place at the interface of IT and production. Additionally, I possess experience in crisis management. I myself have stuffed a large number of bumps and went through a large number of problems and conflicts. I had to face situations of layoffs.

If you’re reading this book, you’ve probably already heard of digitalization, digital transformation, and maybe even got lost and now you want to figure out what it is and how it can be applied in business.

My main occupation is to get the owners between Scylla and Charybdis to build control systems and to do digital transformations. And this book will help you get to know the world better.

What is the difference between this book and others? The fact that technology is not central here. Of course, we’re going to go through the theory, we’re going to get to know the basic technologies and systems, but it’s also going to include real stories and my personal experience, and we’re going to look at the causes of most of the problems, and we’re going to look at how to work with the changes that the state is preparing for us, and here ESG and how to start implementing the figure in small steps with the maximum result.

This book is written primarily for industrial executives and entrepreneurs who are at a crossroads and are tormented by the questions: Do we start digitalizing and digitalizing? And if we start, how? What people are needed, how not to lose your team and money? How long will this process take? What are the risks and pitfalls here? What will it lead to? What technologies make sense to use? What will happen if you don’t do it?»

In my experience, these are the people that ultimately determine success. However, they are also the main cause of failure or problems: inflated expectations and impatience, the wrong team, insufficient resources or their dispersion, the faith of «storytellers».

It’s also a curiosity book. However, it is contraindicated to specialists in IT, automation and anyone who likes «accurate and large-scale research with scientific confirmation of each thesis». It’ll probably give people like that heartburn.

I really hope I was able to find a balance between abstraction and immersion. Without basic technical training it is very difficult to manage «technicians»: managers need to understand what their specialists say, and specialists are interested in their managers. And this book will serve as a bridge between TOP and technicians.

For the most impatient, I will say at once that digitalization and digital transformation are a long process. On average, it takes 2—3 years, and in large companies — about 5 years. Why so much? Because, first of all, it’s not about technology, but about people, processes and internal culture.

Everything must be ready for radical changes but the main thing — top managers, because they will have to solve a huge number of conflicts. Additionally, if you’re not ready, you should stay on the beach and just have fun, save money for a prosperous old age. It is impossible to change others, but to remain the same. It will be profanation. This is also one of the key tools of management — system restriction theory. It is often the leaders and their thinking that become the constraints of their companies and divisions.

A large number of people are talking, and I think it’s reasonable, about the Fourth Industrial Revolution, which is a radical change of attitude.

As a reminder:

— 1st Revolution — the invention of the machine, the avoidance of manual labor;

— 2nd revolution — the invention of the conveyor, radical increase in productivity;

— 3rd Revolution — the emergence of the Internet, mobile communication, the spread of computers;

— 4th Revolution — the use of end-to-end digital technology and robotics.

However, any revolution is either an opportunity to take things to the next level or a risk to lose everything.

Working with people in different industries, I learned one simple thing: few people understand digitalization and digital transformation. For some, it is something strange and complicated, for others — renamed automation.

Having studied many requests of companies, including on portals on search of employees, I have repeatedly seen it. Most simply want to attract those who will introduce the next 1C, production planning system or financial management. So basically, we’re talking about good automation machines, which are called digital transformation managers (CDTs, CDOs), IT business partners.

This book is the result of a huge amount of pain experienced and mistakes passed. Not only my own, but also colleagues from different industries and companies — all those with whom I communicated. Its purpose is to «dispel the fog», immerse you in the world of numbers with the help of human language, to show how it works, to help avoid expensive mistakes, loss of time and disappointments.

Well, in conclusion, I want to warn: some of the points and recommendations will intersect and repeat. Additionally, there are reasons for this.

First, digital transformation and digitalization involve many dimensions: technology, psychology, process management. And some recommendations cut across several areas. Additionally, knowing how many people do not like to wait until the end, you have to give recommendations immediately.

Secondly, I love the saying «repetition — mother of learning». Going back to the most important things a few times, you’re likely to remember them and start applying them in life. At least I like to think so.

There will also be a large number of links in the book. In the paper version they will be presented as a QR code, and in the electronic version — as active links. Everything for your convenience!

Gratitude

Here I want to express my gratitude to all the people who influenced me and became, in fact, co-authors of this book. These are my relatives and colleagues, principals, teachers at the university, coaches at the sports school. However, I would like to thank especially:

— my parents;

— son Valery;

— wife Alice;

— my coach — Evgeny Bazhov.

Additionally, of course, Aleksandra Peremyshlin, who acted as my personal editor and shared his ideas.

Chapter 1. Point of Entry

This book is my vision of digital transformation. Additionally, our visions and opinions are shaped by our experiences and how we experienced them, what we thought, what we learned from them.

Until 2015, I did not expect to be so immersed in IT and management.

I often held organizational positions: I was the head of the school and sports school, deputy platoon commander at the military training center of my university, corps commander in the student operational squad, implemented various projects (project management is also a kind of management) and developed product strategies. However, with the management of IT-commands and the implementation of digital solutions I have never before encountered.

In high school, I didn’t burn much with computers either, but I loved literature, geography, history, algebra, geometry, biology and chemistry. At the university he received a chemical and technological education. Moreover, my first personal PC appeared at the age of 17 at the 1st year of the university. It was an ancient $200 miracle that worked every other time.

However, I’ve always been curious, looking for opportunities to work more efficiently and cost less, to structure work and use data to make quality decisions.

Gradually I sank deeper into the path. He first automatized weekly and monthly reports, then participated in the implementation of the asset management system, introduced a software package for collectors, analyzed digital projects in the Ministry of Energy…

For the time being, I have over a dozen different projects. Among them are the introduction of existing digital products in production, and the search for new management solutions. One of them I am working on right now.

What is digitalization, digital transformation?

The absolute majority of people, both representatives of production and business, and IT employees in production, do not distinguish between these two concepts. So, one of the most frequently asked questions is, how is automation different from digitalization and digitalization different from digital transformation?

In general, in the field of digitization, it is extremely important to remember one thing: it is not about technology, but about people and processes, about the ability to sit down and negotiate.

Let’s look at the classic definition of automation. It is an activity aimed at reducing the number and labor intensity of human manual labor in daily activities.

That is, automation proper is not always connected with IT.

Additionally, in the context of our topic, automation is the digitization and embedding of IT-technologies into existing processes, their acceleration due to the minimization of manual labor and the initial establishment of process management.

The key effect here is to accelerate processes and reduce the risk of human error.

Let us give examples:

— introduction of a «classical» ERP for resource management and planning or electronic document management systems that copy existing practices of «paper work» and often complicate the process;

— robotization and automation of routine operations (for example, RPA, which we will consider later).

Digitalization, on the other hand, is the introduction of digital technologies and systems, allowing to restructure business processes according to the principles of lean production and to make them more efficient and flexible, to start working with data and making decisions based on them. This process reduces internal costs and increases efficiency, allowing for competitive advantages within the existing business model.

That is, the key effect here — reduction of costs in the execution of processes, their improvement and creation of «flexibility».

Examples:

— implementation of enterprise management system with data warehouse, complex integration between IT-systems, optimization of business processes and use of end-to-end analytics and cross-functional indicators for decision making;

— Switch to cloud technologies and cloud services for the organization of work, using SaaS, PaaS, IaaS;

— Using blockchain contracts to exclude legal disputes between counterparties on complex projects.

Many experienced automators say digitalization is the conversion of data into units and zeros, and, of course, right… However, I propose to formulate definitions and look at everything from the standpoint of the answer to the question: «Why, to what?». That is, we share definitions based on the goals and objectives of the technologies.

This view arose because of the many automation projects seen, often limited to the translation of processes into IT systems. Meanwhile, the work grew more and more, and the processes did not change radically.

Additionally, digitalization is not always accompanied by automation. For example, the introduction of cloud-based document tools does not eliminate manual labour or automate processes. However, now you can work anywhere, you do not need to increase your server capacity, which reduces costs and removes restrictions. You don’t need to be in the office behind a working computer and invest in new equipment.

Another example is the use of the Trello board, which also does not automate anything by default, but allows you to rebuild the workflow, work more efficiently with information and be freer to work behind any device with an Internet connection. At the same time, the performance of the team sometimes increases by 40—50%.

Finally, I want to share the opinion of Peter Drucker, who in his book «Challenges of the XXI Century» gave a very good definition of the evolution of the term IT (information technology): previously there was a focus on T — technology, because they were our limiting factor, and the whole point was reduced to their development and implementation, as it gave advantages; now the emphasis shifts to the I — information, because you need to learn to process it with less cost, systematize, analyze and make decisions based on it.

As a result, automation is often about technology and digitalization is about information.

Perhaps now the difference between automation and digitalization should be clearer. However, what about the term «digital transformation»?

It’s a little more complicated than that, and arguing, arguing, and breaking copies is even more complicated.

Different sources interpret the term differently. For example: achieving operational efficiency and flexibility using digital technologies (Forrester Research); business model that allows you to create values and generate income (Gartner); attracting customers at any point of contact (Altimeter Group).

As you can see, there is no consensus and it is unlikely to appear soon.

At the same time, the term «digital transformation» is applicable in the context of one company or industry, but each of them in turn is an integral part of the digital economy.

What is this puzzle?

Roughly speaking, the digital economy is one in which all major transactions take place in a digital platform space that processes customer data and makes algorithmic decisions, reducing transaction times and the number of intermediaries.

If you, my dear reader, want to read all kinds of definitions, then use the QR code or the active link below.

From the definitions above it can be concluded that digital transformation is a global restructuring of business and management systems, processes using the results of digitalization and automation to increase commercial potential and increase profits. Main «effects» of digital transformation:

— a multiplier reduction in processing costs (receiving, transmitting, processing, analyzing);

— change of organizational structure, functions, culture;

— creating new products and business models;

— actively using cross-sectional analytics for decision making;

— «direct» digital channels of communication with customers;

— development and testing of new products based on hypothesis research results.

That is, the key effect here — the creation of new personalized products for «target audience» in combination with multiple reduction of internal costs.

Examples:

— exclusion of intermediate stages from the chain «manufacturer — consumer»; direct communication and delivery directly to the buyer (e.g. through the use of Amazon, Aliexpress platforms, etc.);

— bringing a new product or service to the market (for example, making parts to individual order size) without the need for complex negotiations;

— moving to a new business model (for example, instead of selling chemical fertilizers, switching to pay for the treated area and the results achieved or a subscription model).

In the end, the essence of transformation is not to introduce some IT systems or to abandon paper, but to completely rebuild the business model and organizational structure. Some of the divisions will cease to exist, so the digital and classical business will always a priori compete for the future.

Stages on the Path of Transformation

We have defined the concepts and objectives of transformational change, and now we need to understand the way to go, what are its main stages?

First, let us look back and recall that digital transformation is the final step in the complex transformation of an organization. Thus, the main stages of complex transformation are: automation, digitalization and re-engineering of business processes, digital transformation.

In this case, the first and second stages may switch places (personally I consider this replacement the best option).

Consider these steps in more detail.

1. Automation, process conversion to IT

For example, electronic workflows often duplicate existing paper practices and only complicate the work. Without optimization, the process gets worse (those who worked in large corporations, probably faced with SAP or 1C systems).

Pros:

— acceleration of current processes;

— new ways to perform operations;

— process control may appear;

— integrates the work of less connected divisions and departments.

2. Digitalization

Rewriting, including with IT, active use of process optimization technologies. First, re-engineering techniques are applied to build optimal processes, and then they are translated into «digit».

Pros:

— optimizes the organization. structure and job responsibilities;

— simplifies processes and provides flexibility, reduces costs.

In addition, if you initially deal with digitalization, and only after this automation, you will save money, because when you re-invent processes, eliminate losses and implement the first digital tools, automation will be easier and cheaper, and the effect is higher.

A simple example. Now you have a process with a large number of agreements that you have decided to automate. We did a good deal of work, we spent money, everything works in the IT system, but the process is still the same as it was. You still have to wait for the key man to put his visa or delegate the task.

In the principles of lean production, all this is considered a loss — actions that do not create value for the end consumer, but increase the internal costs.

Additionally, there will be many such trials.

Additionally, if you initially think about how to simplify this process, do the simulation and selection of the necessary scenarios for automation, then you have to automate less, which is cheaper. And once again you don’t have to automate after optimization. That is, you won’t pay twice for solving the same problem.

3. Digital Transformation

I repeat a key point: unlike automation, TP does not focus on internal processes, but allows you to use new technologies for business and growth.

However, personally, when working with small and medium-sized businesses, I start with an analysis of the. structure. It often turns out that there is no clear and unified understanding of who does what, what is responsible for, what authority, responsibility, resources. Additionally, how do you work on business processes when everything changes weekly? What’s the point of introducing and automating something?

Below, in chapter 4, you will be familiarized with one case of restructuring and without system configuration. The result was paralysis of production and chaos. Now imagine that such changes are constant. There is more harm than good.

Business benefit

Well, we’ve discussed a few details, but I guess a large number of people still have some questions about why the hell would I? What’s in it for me? Explain on your fingers!». Let me try.

1. Increased efficiency and productivity

How: reducing transaction losses by simplifying and optimizing operational tasks and processes, increasing productivity (including eliminating duplication of functions), eliminating intermediaries (effective value creation) reducing transaction costs for obtaining information and services, simplifying the organizational structure.

Example: It is estimated that every year companies with about 1,000 employees lose an average of $1 million just because of duplication of previous work.

2. Increasing the turnover rate

How: reduction of contract execution time (acceleration of the process of purchase of own goods, acceleration and simplification of documents circulation, procurement processes, deliveries), introduction of new products and services on the market, introduction of direct communication and reduction of communication time with customers and clients.

3. Realizing the potential of staff

Such as: reducing the operational burden on repetitive and simple operations, increasing flexibility for staff, including attracting the most talented staff, including from other regions; reducing staff turnover.

4. Improving the quality of management decisions and responsiveness to changes

As follows: use of transparent and structured data for analysis and assessment, acceleration of information acquisition and processing, reduction of industrial risks (early detection of threats).

5. Unlocking new opportunities and diversifying sources of income

As: creation of new products and services, business models, creation and development of new markets, both in understanding the new niche in the economy of the current market, and expansion to new territories.

For example, contrary to the traditional model of working with the most profitable customers, you can attract a large number of small customers.

That is, we cover the part of clients that was previously unprofitable to include in the work because of too high costs for their support. Together, they can make even more money than the major customers.

Let me give you an example from consulting. A good consultant will cost expensive: from $ 150 to $ 500 per hour depending on the industry, direction, qualification and type of client (legal or natural person).

As a result, we get 2 main restrictions:

— for a consultant as an entrepreneur: he is limited in his earnings by his time;

— for customers: to get the effect, you need to hire an expensive specialist, and not everyone can afford it.

What if the consulting company created a digital advisor? Mathematically describe experiences and best practices, build relationships and simulate possible scenarios, then form a neural network and through deep learning gradually develop it (more about this in the next part). Then the system, based on input data, will generate recommendations.

After all, in fact, this is how consultants work. Only a few of them are able to combine different tools and deviate from the rules to achieve results. Additionally, this too can be taught to the system.

How much do you think it would cost to have access to such a system? Even if 20—40 dollars a month, it would still be a multiple reduction of the entrance threshold.

As a result, we get:

— The consulting company can multiply its cash income;

— The customer gets a modern tool for reasonable money. So now he can try the service, and if he likes it, use it as a working tool, and if he doesn’t, it’s still far cheaper than a live consultant.

This example shows how the classic consultant service changes to a digital product that benefits everyone: the company reaches more customers and the customers themselves reduce their costs.

Additionally, it turns out that one company can serve, let’s say, not five customers for $1 million a month, but five hundred for $20 million a month. Moreover, this is one of the key areas of digital economy development.

Business Digital Competence

What kind of digital pathways can business do? What can be digitized for digital transformation?

The Boston Consulting Group introduced a matrix of competencies that companies need to become digital. It includes 6 areas:

— digital business;

— digital marketing;

— digital products;

— Digital Analytics;

— digital manufacturing (industry 4.0);

— new ways of working.

Let’s take a closer look at what these are and what kind of people they need.

Digital business

This is a strategic function, which was previously handled by the Director of development, but now the focus is moving to numbers, and 3 more areas have appeared:

— digitalization and business transformation;

— digital venture investments;

— digital laboratory.

Digitalization of business is a change in the business model and the introduction of technologies into the company’s processes. As a result, operating losses should decrease, and business profitability and revenue should increase.

Digital venture investments — search for new business niches and promising start-ups to invest in. Accordingly, the quality of investments in these niches is assessed.

The digital laboratory creates promising partnerships and organizes a factory of pilot projects. The indicators are the number and success of the pilot projects launched.

Within the direction of digitalization and business transformation, the following directions can be distinguished:

— IT infrastructure and its optimization;

— production, including maintenance and repairs, industrial safety, operational analytics, automation of reporting, etc.;

— logistics and logistics;

— organizational efficiency and record keeping;

— sales and current products/services, i.e. customer service;

— economics and finance, including accounting;

— frames.

Required roles in the digital transformation team

The following list is not a record in the employment record, but a list of the roles to be performed.

— CDTO (Chief Digital Transformation Officer, or Head of Digital Transformation).

The main ideologist, who chooses the goals and direction of the movement, agrees the budget and manages the implementation of the transformation, coordinating all projects, interacting with external parties and inspiring his team.

— CA (Chief Architector, or Chief Architect).

It is responsible for the practical implementation of transformation in the form of connecting all components: business processes, applications, data warehouses, interfaces of interaction. It is not desirable for one person to combine the roles of chief architect and project manager, as their roles and responsibilities in the project are very different. The project manager is primarily the manager and the chief architect is the technician.

— CDO (Chief Data Officer, or Data Manager).

Responsible for the timely provision of the necessary data and analytics, coordinates their collection, storage and processing, forms a data processing unit.

— CTO (Chief Transformation Officer, or Digital Design and Process Manager).

Responsible for the implementation of the process approach and the design of new digital services, the study of processes, needs and customers.

Also important:

— lawyer (monitors changes, innovations and nuances of legislation, especially in the field of intellectual and data rights);

— Specialist for Interaction with External Organizations (contacts with data owners used in the project);

— information security specialist (responsible for data protection, which inevitably attract increased interest immediately after informing the external environment). In general, you should not neglect information security. As soon as you become visible on the market, there are people who want to get your data. However, do not close, because most of your data loses its value with the speed of fading banana from the store.

At the same time, it is necessary to understand that the current CIO (the head of the IT direction) is not suitable for the post of CDTO. Or it will need a long-term overhaul of its thinking and work priorities. We will address this issue in more detail in one of the following chapters.

Digital marketing

This is a function for generating digital content and managing end-to-end communication through digital channels. The main tasks are:

— the production of digital content;

— brand management in digital space and communication channels (messengers, social networks, email, etc.);

— Interact with users to collect feedback and learn preferences;

— launch of advertising campaigns.

The main roles here are:

— SMM specialist;

— MarTech specialist;

— Target Analyst;

— SEO specialist.

SMM-specialist is engaged in the promotion of business on the platforms of user content, i.e. in social networks, blogs, on educational platforms and in the framework of advertising campaigns.

MarTech specialist manages digital marketing technologies, i.e., tools that help plan, implement and automate the marketing activity of the company, measure its results and ensure continuous interaction with the audience. Now there are many tools that allow marketers to work with data and digitize routine processes.

The target scientist works with the target audience through targeted advertising.

The SEO specialist is responsible for transferring the traffic of Internet users to your resources (sites, applications).

Digital products

It’s about creating new digital products in the form of analytics services, applications.

How digital products differ from «classic»:

• communication between the user and the seller is direct, through digital channels;

• The processes involved in providing the service use modern tools — digital platforms, chat bots, machine learning, big data and the like.

The following key roles are required in this area.

1. Product Manager / Product Owner (more correctly «Product Manager») is an entrepreneur within the company who manages the product, forms requirements for it, negotiates with partners, manages its profit. It’s a key role for a digital product.

The product manager must have the following skills.

— Knowledge of psychology, market laws and subject matter

In order to create something, you need to understand the market and see its trends. This gives the ability to generate ideas and form hypotheses from them.

— Creative thinking

The ability to create is a very rare quality associated with going beyond the usual limits. Creative thinking improves the quality and quantity of ideas.

— Understanding Customer Needs

It’s not about what you’re interested in, it’s about what solves your client’s problems. This guarantees a public response, which means that the product will be used more often.

— Knowledge of IT

You can’t come up with a product without knowing what technology it’s going to be made with. Already at the stage of the project you need to know its future prospects and opportunities (transformation, expansion, integration).

— Ability to cooperate

One in a field is not a warrior. Although it often happens in startups that the product manager works as a marketer, developer, and salesman, but in the first place he is the organizer, which means he needs to be able to work in a team. In addition, it is important to build cooperation with other teams and companies. As the practice in the B2B market shows, the end customer wants to receive a comprehensive product, and the more technical partners, worked out system integrations, the higher the chance of success. Again, as practice has shown, relying on system integrators is very risky.

— Knowledge of basic product management tools and data analysis skills

No matter how smart you are, you need to own tools and be able to analyze data. It is not enough just to see data. Practice confirms that most companies suffer from neglect of analytics. Those who do not make the same mistake make better and more informed decisions.

2. UX/UI is a specialist who focuses on the development of a convenient digital product that will be comfortable and enjoyable for the customer. It manages the interfaces of your sites, applications, services: their logic, fonts, colors and so on.

One of the key competences here — knowledge of the principles of lean production. That is, the ability to organize the product interface so that the user does not have to make unnecessary moves.

Digital analytics

This is the collection and systematization of data from all channels and sources. What is known as Big Data, or big data? What it is, we will figure out later, but what kind of people are needed, is already clear. These are different analysts. Additionally, although it seems that they are all engaged in the same, they are distinguished by «specialization».

In general, they’re all data analysts. However, there are, for example, data scientists who are engaged in «science» and form mathematical models necessary for better design. The main direction of such a specialist is to predict and form new hypotheses.

Classical data analysts perform slightly more understandable tasks: collect, process, study, visualize and interpret the collected data about events that have already occurred.

Digital production (industry 4.0)

Technology, sensors, robotics, and artificial intelligence are beginning to be used in production, and some of the solutions in the pipeline are no longer human, but machine-driven. Design initially takes place in a digital environment along with the creation of digital counterparts, which inevitably leads to the discovery of new roles.

— The CAD engineer creates prototypes directly in the digital environment. In order to start production and collect operational data from them. Such a person should deeply understand mathematics, engineering and digital tools.

— The Robotics Specialist (RPA) knows how to build software robots and understands application scenarios. This is a business analyst with programming skills. A little later, we’ll look at who RPAs are, what kinds they are, what effects they have.

— Process Analytics analyzes and works in BPM-solutions (business process simulation systems), to which we will also return. The main difficulty — a large number of rules and standards to describe business processes. At the same time, ordinary people understand them with difficulty. It was therefore necessary to strike a balance between detail and accessibility for staff. And it is better to make several descriptions — one detailed for analysts and several role-playing ones that fit on sheet A4.

— The computer vision and learning specialist helps machines «see» and distinguish objects, people, animals and the world around. The machine itself will not understand what it sees — it needs to be trained. Computer vision is one of the promising areas. We will consider it below.

New ways of working

It’s about how we work and think at work. New ways of thinking are not so much about Agile’s flexible methodology and different approaches based on it as about philosophy, about leaving decision-making for creative potential.

For example, these include the use of digital tools to organize work: kanban tables, knowledge management, exchange of ideas, organization of online meetings and hybrid graphics. Also, important here is the use of digital tools for seamless communication: calendar, mail, messengers, audio calls, video meetings, task trackers.

There’s one new role — evangelist, or agile coach. Its main task is to explain that it is possible to build business processes in a different way, to work more flexibly. In essence, it should educate the organization’s employees about digital lifestyles. The more people use digital tools, the more they will understand this culture. For example, there will be no need to meet in person if you can use the Zoom service or if you dislike many Teams.

New Types of Business Models

The number brings the ability to create new business models. Let’s look at them.

— Free model (special model)

For example, it uses on Google. It’s about monetization through embedded advertising. That is, such companies sell you, your attention and time, and analytics of your behavior.

— Subscription model

Instead of one large purchase you make a subscription to the service, that is, you include constant «invisible» payments.

We all know Netflix or Office 365. These products are examples of the classic subscription model. The user receives access, updates, services, etc. on a monthly/annual basis.

Plus, for the company — stable flow of money regardless of the season or other factors.

— Freemium-model

Users have free access to the basic (Free) version of the product, which is usually limited in the most valuable features. To use more features or resources, you must upgrade to a paid version (Premium).

An example is Spotify. Everyone can use the service for free (and receive advertising), but if you want more features and better quality, you need to pay for a monthly subscription. This is also a great example of how business models can be mixed.

Now it’s one of the most popular models. In the free version you can «sew» advertising and earn on it, and if the user does not want to receive it, then earn directly.

— Model on request

On-Demand works, for example, in online video stores, where you get the right to watch a movie for a certain period of time (Amazon Video, Apple TV+ and so on. d.).

The same system is used when you book a consultant and pay depending on how long you need help.

— E-commerce model

This is an example of trading platforms (Aliexpress, Amazon) or online stores. Today, it is also the best-known business model on the web, as you can buy almost anything online.

— Platform model (two-way marketplace)

The bilateral market is something that we see quite often on the Internet. Sellers and buyers use the third-party platform (Yandex Market, Ozon) to trade their goods and services.

The biggest problem with this business model is its complexity and dynamics. If you don’t have sellers, you’ll never attract buyers, and if you don’t have buyers, you’ll lose sellers. Thus, the bilateral platform should carefully scale demand and supply simultaneously to keep both sides attractive.

— Ecosystem model

Digital ecosystems are one of the most complex and powerful business models. A striking example is Apple. If you’re in the ecosystem, it’s gonna be hard to get out. Try switching from Android to iOS or back — this is not the easiest task for the ordinary person. However, inside the ecosystem you are comfortable, you get used to «single purse».

— Ownership Access Model/Sharing Model

This is the so-called «sharing economy». Such a system allows you to pay for a product, service or offer for a certain amount of time without obtaining real property rights. This can be a car rental (for example, Yandex Drive, Delimobile), apartment rental (for example, Airbnb) or even industrial machinery. An example of the latter is «Kamaz». As part of their digital transformation strategy, they launch short-term truck rental services. And this was made possible by the widespread use of digital technology.

This business model is one of the most revolutionary when one considers its impact on ownership and the resulting revenues. The car could suddenly become a source of income instead of just generating costs.

— Experience model

Adding experience to products that would not have been possible without digital technology. One example is Tesla, which has brought the automotive industry a whole new digital experience by adding digital services and even a digital ecosystem to its cars, which are now the main engine for their business model.

— Service Model

Here we talk about the fact that the user pays not for the product, but for the service. For example, the fertilizer producer does not supply the customer with fertilizer, but combines expertise and resources, providing services for the treatment of the area and increase of the crop.

He’s got big data that he’s learning to develop more efficient fertilizers, he’s got cheaper technology (economies of scale), more advanced logistics, and so on.

Or, for example, buying industrial equipment, you do not fill your head with questions of its maintenance. The manufacturer collects the data, analyzes it and organizes the service itself.

Preparing for digital transformation and digitization

Before you implement any changes, you need to understand what is now? What is your starting point? On this depends the whole further strategy.

The conscious construction of digital transformation involves, in one form or another, the following three steps:

1) definition of the «basic» situation, starting point;

2) target level definition (where do we want to go?);

3) planning actions to reach the target level.

In principle, as will be seen below, there is nothing new here, all this is combined with the basic methods of change implementation.

Digital maturity is the ability to use digital tools to achieve key performance, or more precisely, to shape a better value proposition for customers.

Among the various ways to measure the level of digital maturity I can distinguish the RANHiGS technique. It includes seven assessment areas and describes their maturity levels.

— Digital culture

The level of organizational culture supporting the processes of continuous improvement and innovation, change management.

— Frames

Compliance of personnel with competencies necessary for successful work in the digital economy.

— Processes

Application of process management practices: process optimization methods, lean production, design thinking. Process analysis, monitoring and continuous updating.

— Digital Products

Analysis of existing products and activities with them. The product is a solution to the user’s need, carrying value for the latter.

— Models

Use of information models in the organization, their constant updating, validity and inclusion in processes.

— Data

Access to the necessary data in real time with the necessary level of security. Completeness and quality of data for decision making.

— Infrastructure and tools

Access to modern digital infrastructure and ensure operation on all types of devices.

My focus in my work is more on:

— industry and company performance versus competitors;

— the use of modern technologies and work with data;

— working with operational processes, including their optimization;

— approach to project management and implementation of changes, creation of products;

— human Resources and current organizational development.

An example of my approach can be seen in the second book in the chapter on digitalization strategy.

Organizational changes in digitalization

For example, in my practice there was a case when the company changed its organizational structure three times. Smart plans came from Moscow, orders were issued, posts were renamed. However, did something change in the work on the ground? No!

Just the paper was one step further from life. Well, some were «optimized».

What kind of changes should digitalization and digital transformation lead to?

— Transition from complex hierarchical structures, that is, transition to 3—4 levels of management — from the general director to the master of the site.

— Review of policies, structure of units, number of staff and complexity of procedures.

That did not imply a reduction in staff, but a redeployment of staff to improve efficiency.

People must be sure of tomorrow. Only then will they accept the changes and be willing to share ideas.

— New roles, functions and needs for new competencies and skills emerge.

Currently, there are no employees who have all the necessary skills. And there are not even requirements for new roles. It is necessary to consider this.

— Change of organizational. culture — new approaches to personnel management, new system of values, withdrawal from management from the position of force and punishment, fines.

Transformational change will require more skilled human resources that are highly mobile and do not adopt outdated management models.

Here you will have to combine softness and discipline, to be able to fairly punish and manage employees depending on their level of «maturity». In my opinion, this is one of the key issues. We cannot go into anarchy or, on the contrary, into dictatorship. Which means you have to learn from the CEOs.

Skills are needed to manage staff using both financial and non-financial motivation. The use of financial motivation alone is very inefficient, has short-term effects and leads to stratification within the company.

Additionally, most importantly, you can’t change culture without changing the first people. If the Chief Engineer can’t use the IT system, but charges everything to 1—2 engineers and asks to print help — it’s just fiction and money in junk.

If you implement a complex asset management system to verify the execution of the budget, and all «opponents» just punish, then the approach to planning repairs from this will not change, but the turnover of personnel you provided.

In addition, it is necessary to actively work with external innovations, launch a large number of pilot projects. Why? Because it tells you that you’re willing to take risks and experience experiences, even negative ones. It’s important to be able to accept it, to analyze it, to learn, not to blame it. Then competences will be not only in the company’s reports and knowledge bases, but also in people.

Middle management must be actively involved in the change process. Many projects do not achieve their objectives at the level of middle and technical management. People are busy with their linear activities and do not know how to manage projects. As a result, we get combined resistance. Breaking this cycle is difficult, but necessary.

At least 30% of the people engaged in innovation have to undergo special training. This helps to form a common vision of where the organization is going, as well as to avoid conflicts and thoughts from the category «again something was invented above, now they will move and let’s go back».

Possible models of digitization and transformation

It is possible to digitize one of four models.

— Informal model

Someone at the company is doing the numbers for some reason. For example, the department of repair or maintenance began to implement digital tools and did it successfully. The downside is that the model does not cover the whole organization and the full potential of technology, but for many companies it becomes the starting point.

— Centralized model

The head of the organization or board of directors understands that digital technology and digitalization are very important for the company. They hire the CDTO, assign it a large unit, empower it — and it digitalizes the company.

The vast majority of companies in Russia are at this stage with an IT or Transformation Director at the head.

The advantage is that you can coordinate the movement and work of the whole organization.

Minus is the speed. The bigger the company, the more regulations, rules, restrictions, the more communication you need to conduct inside the company to launch a pilot project. After all, there is also resistance, as many leaders do not need this digitalization. As a result, companies invest in technology, and business customers simply ignore new tools, they impose them.

I participated in the project on this model and I can say that if the curator at the head of such a project does not have the necessary competencies, it can bury the whole project. Of course, in the reports everything will be shown beautifully — we can report like nowhere else. However, if you go down to the level of those who work «in the fields», you will understand how much money is wasted. The project in which I was involved, with a total budget of more than 1 billion rubles became my personal pain, forcing to learn from the mistakes of the curator. Additionally, communication with colleagues on the floor shows that such a model often only increases the time frame and budgets.

— Distributed model

Each division has its own digital office, which determines its own solutions and services.

In this case, the CDTO functionality is crushed and embedded into the existing structure so that these small offices start transforming their units from within. Then each division has its own «small» head or leader in numbers, and the big director of the number becomes unnecessary and leaves the company, because all can develop their own competencies. It’s a new culture.

Plus is the speed of deployment and how fast the changes are coming.

Minus — duplication of costs. Often such teams begin to push elbows, in terms of the cost of the whole organization they are not very effective, constantly «invent bicycles», and they always have something to optimize.

— Hybrid model

It’s that all small digital offices need a focal point. His task is to build uniform rules of work, development, to form processes and tools, to develop a common strategy, so that local offices already use his knowledge. It becomes a methodological centre and coordinates all the centres so that they work towards a common goal and are synchronized with the strategy.

As an analogy you can take the company Google, which develops its «market place». Developers do not create apps from scratch, and use many proprietary ready-made applications from Google. For example, if you want to use maps in your app, you take ready-made maps from Google.

If you want to do local digitization, you need to understand the level of organizational development of your company. If you have a centralized organizational environment with a large digital block, you can assume that there will be problems with speed. If you have a distributed environment, there will be a problem with duplication and unnecessary costs to develop your products.

Always keep your structure in mind — this will protect you from irrelevant actions.

Risks

The introduction of any changes is risky, and digitalization and digital transformation projects are no exception. With dozens of projects of different sizes behind me, I can confidently say that risk management is a key element.

What risks do we have?

— Information security

New information systems, huge volumes of data require protection from losses and «hacking», as well as from incorrect operations. New levels of security are needed. New requirements for personal data protection are emerging. However, it should be remembered that if you start to ban everything that is not allowed, your people will look for workarounds, thus increasing the risks. Then it looks like you’re protecting everything, and the data is still compromised. And the key here — the ability to evaluate what is really valuable, and what you need only or becomes obsolete at the moment of appearance.

— Resistance to change and company culture

In the process of transforming your business, you will inevitably encounter staff resistance to innovation and in a favourable scenario you will lose 10—15% of employees.

There are many reasons for this. A special chapter will be devoted to working with resistance. It is worth noting the issue of culture, because culture has a strategy for breakfast. If you don’t know how to work with change and teamwork, nothing good can come of it.

— Terms

When implemented correctly, when changes are introduced from the top, from top, and supported by ordinary employees, digital transformation takes 2.5—3 years. In large companies this period may extend to 4—5 years.

Even the first results will be visible only after 9—12 months, but most managers lay 6—9 months for the entire transformation. For this period, you will have time to implement only 1—2 projects on digitalization or automation.

— Non-streamlined processes

This is one of the key elements of digital transformation. You can go the classic way — first automate what you have, and then reengineer processes. However, automation without prior optimization is not feasible. You will spend a lot of time and resources, but in the end, you will create a very heavy and clumsy system. Optimization is the most important step in the transition from manual labor to automated labor.

— Unstructured data

If you can’t structure data, transform it into useful information, then everything else is meaningless.

— Staff competencies

Digital technologies place a new level of demands on the knowledge and competencies of both the people adopting these technologies and the users. Large-scale measures are needed to educate and retrain, motivate and overcome resistance to innovation.

At the same time, research of one of the federal projects showed that in Russia only 26% of people have advanced digital skills. According to DICE, in Europe, this figure reaches 57%.

— Costs and people overburdened

Digital transformation is a high-cost initiative without guarantees of success.

According to research, only 20% of the changes are implemented successfully. Others fail for various reasons, including because of its redundancy. If there are 250 changes per person per year, you can hardly expect a positive result.

— Technology Disappointment

Gartner’s Gartner Hype Curve, a consulting agency, issues a report every August. This is a graph of public expectations about a particular technology. According to the agency, in the ideal case, the technology successively passes five stages: the launch of the technology, the peak of elevated expectations, the bottom of frustration, the slope of education, the plateau of productivity. However, it also happens that technology doesn’t make it past stage three — the bottom of disappointment.

Of course, it should be remembered that the Gartner chart is just a forecast, there are exceptions to it, but it still helps to assess the risks of early use of new technologies.

In summary, the most common causes of failure are:

— the unpreparedness of the IT systems used;

— data quality and availability;

— poor quality work with change management;

— Poor quality project management.

We will discuss this issue in a separate chapter.

Chapter summary

— The introduction of digital technologies is one of the tasks of digitalization. The global challenge is digital transformation, with a redefinition of processes, goals, models and strategies.

— Digital transformation is only a tool. More important is the overall quality of management, team. You cannot focus only on numbers.

You need highly qualified employees. Additionally, this means that policy management and aggressive management will no longer be applicable. Such employees are highly mobile, with mismanagement you will have to constantly recruit new personnel, teach them, and then lose them, and so on.

— In the Gartner diagram, many digital technologies are near the peak of high expectations. Disappointment will follow, and only after we learn how to use all these tools will there be value.

— There are no employees on the market with full set of necessary competencies. Team members are likely to have one or two strong competencies to use.

— Basic digital literacy increases the likelihood of digital transformation succeeding.

When implementing digital projects, a large number of users are affected, and the level of their basic IT training can vary. The accumulated statistics showed that «tightening» of basic PC skills significantly increases the probability of successful implementation of digital solutions in general.

— Basic for all competences are the ability to solve poorly structured problems, system and critical thinking, digital skills.

— The introduction of technologies and transformation can be carried out independently: allocate resources and people, work according to a matrix.

Your chances of success are 20 to 30%, at least 10% of your employees will be gone, you will have to bear a huge cost and the implementation period will be 2.5—3 years.

I recommend finding a company that specializes in digital transformation and will communicate with people while remaining independent of domestic policy. It will digitize the project, prepare proposals for optimization and necessary changes in culture, processes, necessary training programs for employees and will implement everything gradually. Then you won’t have to get rid of the time team on this project.

— To understand whether you need digitalization and digital transformation, ask yourself a few questions:

— how competitive is your industry?

— is it possible to replace your product or service with a digital one?

— do you have market preferences that are not available to other members?

— what is the threshold of entry?

Answers to them will already give an idea of how far you need to move forward. Additionally, the main criterion — it is necessary to deal with digitization if your revenue depends on a few key customers.

Chapter 2. Technology. Pros, Cons, Personal Opinions

I’m sure many of you at first thought this was going to be a major section. However, since this book is not for techs, I’m going to try to make it simple. After all, our task is to see the essence and begin to navigate the digital technologies, to understand how to use them for business.

Well, for those who think this topic is the main one, I suggest clicking on the QR code or the link below. There you will find a large number of visualizations, links and videos.

Internet of Things (IoT, IIoT)

The Internet of Things (IoT — Internet of Things, IIoT — Industry Internet of Things — industrial Internet of Things) is a network of interconnected Internet devices. These are so-called «smart devices», although this name is not quite correct.

What is it for?

First, to collect and share data that can be analysed and based on such analysis, to make decisions. And secondly, to remotely control connected objects or devices.

According to Strategy Analytics, in 2018 there were 23 billion devices connected to the Internet of things worldwide. And by 2025, about 80 billion IoT devices are expected.

The IoT development was made possible by the reduction of the cost of the Internet (it has decreased several times in 10 years), as well as the reduction of computing power and sensors.

At the same time, it must be understood that the Internet of things is not so much a conditionally «smart» kettle, socket and so on, as a big data generator (which we will talk about a little later), something that analysts and data will work with later Scientists to form new proposals and generate ideas.

IoT, for example, will allow:

— to develop predictive analysis and prevent accidents or catastrophes at industrial facilities;

— adjust road traffic according to traffic density;

— make recommendations for improving efficiency.

Application scenarios here are limited only to imagination.

The main advantages are mobility and generation of «pure» data, that is, the exclusion of errors that occur when you enter data.

I believe that the Internet of things, including the Internet of Industrial Things, is one of those technologies that will fundamentally affect all aspects of our lives. Only in 5—10 years.

Now it is necessary to discuss how the Internet of things works. Do you have to pull cables everywhere or put routers?

Nay.

Different wireless communication systems can be used to organize data exchange, not necessarily mobile networks, but depending on goals and objectives.

First, consider LPWAN — long-range networks.

LPWAN

LPWAN (Low-power Wide-area Network, long-range energy efficient network) is a technology for the wireless transmission of small volumes of data over long distances. Since the volumes are small, the low data transmission rate is enough to achieve a longer range of reception.

This technology is designed to collect telemetry and interaction between machines (M2M). In fact, it is one of the key wireless technologies for IoT systems.

The LPWAN network approach is similar to that of mobile networks. A device or modem with an LPWAN module sends data via a radio channel to the base station. The station receives signals from all devices within its range, digitizes and transmits to a remote server using an accessible communication channel: wired Internet or cellular.

Benefits of LPWAN

— Long range — from 10 to 15 km

— Low power consumption in sensors

— Relatively high range even in the city

— It’s easy to build networks and add new objects

— Easy to use — can be done without permits and pay for radio frequency spectrum

Drawbacks of LPWAN

— Low speed — only necessary data can be transferred

— High delay between data transfer sessions

— There is no single standard for creating compatible solutions from different manufacturers

There are two main options for implementing the LPWAN network:

• licensed frequency range (increased power, relatively high speed, no interference);

• unlicensed frequency range (low power, low speed, limited transmission cycle, possible interference from other participants).

3 main technologies of construction of LPWAN-networks:

• NB-IoT — evolution of cellular communication;

• UNB (unlicensed LPWAN) — SigFox in the world;

• LoRa is a broadband unlicensed LPWAN.

NB-IoT

NB-IoT (Narrow Band Internet of Things) is a cellular communication standard for low-volume telemetry devices: medical sensors, resource meters, smart home devices, etc.

NB-IoT is one of three IoT standards developed for cellular networks:

— eMTC (improved machine-type communication) — has the highest capacity and is deployed on LTE (4G) hardware;

— NB-IoT — can be deployed both on LTE cellular network hardware and separately, including on top of GSM;

— EC-GSM-IoT — provides the least bandwidth and deploys over GSM networks.

— Advantages of NB-IoT

— Flexible power management of devices (up to 10 years in a network with a capacity of 5 W*h)

— High network bandwidth (hundreds of thousands of connections to the base station)

— Low cost of devices

LoRaWAN

LoRaWAN is an open communication protocol that defines the system architecture. It was designed to link low-cost devices that can run on batteries (batteries).

According to IoT Analytics, it was the most widely used low-power global network (LPWAN) technology in the second half of 2020.

The LoRa technology is primarily required for machine-to-machine interaction, and can service up to 1 million devices in a single network, giving them a 10-year autonomy from a single AA battery (a regular palm battery).

For the review to be objective, disadvantages and limitations must be addressed.

The most important limitation for organizations wishing to implement IoT is the cost and time of project implementation. Another factor was the limited expertise of the staff.

Technological shortcomings include the following:

— power supply (either have low speed and frequency of data, or need to arrange power supply);

— dimensions (not all sensors can be miniature);

— equipment calibration (reliability of readings);

— dependence on the data network;

— lack of common protocols and standards for transmitted data, which may make it difficult to process, integrate, and analyze data even on a single production scale (in February 2022, the new ISO/IEC 30162:2022 standard was released, but the transition to uniform rules will still be difficult);

— vulnerability to external attacks and subsequent data leaks or intruders gaining access to hardware management.

5G

You’ve probably heard of the 5G. That this is a breakthrough in communications, and no new flagship can be a breakthrough without the 5G. After all, without it it is impossible to look at the smartphone new series at 4 or 8K. Therefore, you need to buy smartphones only with this module and pay 100—150$ more than the version with the 4G module.

However, very few know that the standard itself was not designed for video in YouTube or TikTok, but for the scale development and implementation of digital services. Its «chip» is flexible combination of ultralow latency (URLLC), high speed (eMBB) and reliability of the communication channel (mMTC), depending on what is needed by a particular subscriber.

Basically, it’s a connection for the IoT. It may not be entirely suitable for an industrial IoT, but for a smart city, healthcare, and industrial enterprises in the city it is the ideal option.

So, what is the difference between 5G and 4G/LTE?

— Eight times better energy efficiency

— 10—100 times the speed

— 100 times more subscribers per base station

All those who are engaged in digitalization in production, and even just implementing ACS TP, know that the main problem is to organize data transmission to or from sensors. The solution of this issue in accordance with all the rules of the company is sometimes several times more expensive than «iron» and software.

Additionally, I hope that with the development of 5G technology, this problem will become less and less relevant.

In addition, the development of this technology will also help the implementation of more advanced IT systems, especially MES, APS, EAM, BIM. More about them — in the next chapter. All these systems need information from sensors without human intervention.

However, there is an unpleasant moment for many. All this will require other competencies from the employees. This means that the «optimization» of the organizational structure and the increase of social tension will begin.

6G

China and the US are already developing standards for 6th generation networks. However, why?

To ensure further growth of smart device deployment! 5G still has limited capacity.

Some sources suggest peak speeds of up to 1 Tbit/s. Average speed of several hundred Mbit/s. The average signal latency is 1 ms, which is useful for applications that require minimal latency, such as autopilots and virtual reality. The number of active devices that can connect to 6G per unit time will also be several times higher than 5G.

«The 6G Era will offer new possibilities for creating brain-computer interfaces», says Dr Mahyar Shirvanimogaddam of Sydney University. An example of such development is the electronic chip for paralyzed and people with CNS disorders, which is created by Elon Musk’s startup.

In this case, the 6G has one great advantage — it is possible to upgrade the existing 5G towers for its implementation, while the 5G had to build new base stations.

It is now believed that 6G may be introduced in the early 2030s.

Neural networks, machine and deep learning (ML & DL), speech and text recognition systems

So, we’re getting to the future — neural networks, artificial intelligence, machine revolutions and other horror stories.

Neural networks are perhaps the most interesting technology. With the support of the Internet of Things, 5G and Big Data, it will bring revolutionary changes to our lives.

Additionally, artificial intelligence is any mathematical method that can simulate human intelligence.

Oh as our favorite advertisers and marketers are satisfied… Now any, the simplest neuronetwork can proudly be called «Artificial Intelligence».

However, artificial intelligence is still divided into strong and weak. In 2019, scientists came close to creating a strong AI, the equivalent of human consciousness. This ability not only to distinguish a pen from a pencil or a cat from a dog (according to this principle all neural networks work, it is weak AI), but also to navigate changing conditions, choose specific solutions, model and predict the development of the situation.

A strong AI will be indispensable in intelligent transportation and transportation systems, cognitive assistants. However, this is the future, and what is now?

Now there are learning neural networks. An artificial neural network is a mathematical model modeled on the neural networks that make up the brains of living things. Such systems learn to perform tasks by treating them without specific programming for specific applications. This can be found in Yandex Music, Tesla autopilots, referral systems for doctors and managers.

Therefore, here are the two main trends:

— machine learning (ML — machine learning);

— deep learning (DL — deep learning).

Machine learning is statistical methods that enable computers to improve the quality of the task with experience and training. So it’s about how the neural networks of living organisms work.

Deep learning is not only learning a machine with the help of a person who says what is right and what is not, but also self-learning systems. This is the simultaneous use of different methods of training and data analysis.

However, how do these neural networks teach? What’s the magic?

Actually, in fact, nothing. It’s like training a dog. Neuronetworks show, for example, a picture and say that it is depicted. The neural network must then respond, and if the answer is wrong, it is corrected. An approximate algorithm is given below.

As a result, it turns out that each «neuron» of such a network learns to recognize, refers to it this picture, or rather its part, or not.

Neural networks and machine learning apply:

— for forecasting and decision making;

— image recognition and generation, including «pictures» and voice recordings;

— complex data analysis without clear relationships;

— process streamlining.

The application value of this can be seen in the examples of the creation of unmanned cars (decision-making), the search for illegal content (data analysis), the prediction of diseases (pattern recognition and linkage search). At the same time, on the haip it is pattern recognition and generative models (chatGPT, midjourney, etc.). However, business problems are still poorly solved. At the same time, 9 out of 10 students now go to study exactly on pattern recognition and machine vision.

The AI + IoT link deserves special attention:

— AI receives net big data (about them in the next section) in which there are no human factor errors to learn and search relationships;

— IoT’s effectiveness increases as it becomes possible to create predictive (predictive) analytics and early detection of deviations.

Okay, this is all a theory. I want to share a real example of how neuronetworks can be used in business.

In the summer of 2021, I was approached by an entrepreneur from the realtor sector. He is engaged in the rental of real estate, including daily rent. Its goal is to increase the pool of rented apartments and change the status of an entrepreneur to a full-fledged organization. The nearest plans are to launch the site and mobile application.

I happen to be a client myself. And at our meeting I noticed a very big problem — the long preparation of the contract: it takes up to 30 minutes for the registration of all the details and signing. This is both the limitation of the loss generating system and the inconvenience for the customer.

Imagine that you want to spend time with a girl, but you have to wait half an hour for your passport details to be entered into the contract, checked and signed.

Now there is only one option to eliminate this inconvenience — ask for passport photos in advance and manually enter all the data into the template of the contract. As you can imagine, that’s not very convenient either.

How can digital tools help solve this problem, but also provide the basis for working with data and analytics?

Neural networks. The client sends photo passports, the neural network recognizes data and enters the template or database. It remains only to print out the ready contract or to sign in electronic form. Additionally, the advantage here is that all passports are standardized. The series and the number are always printed in the same color and font, the division code too, and the list of issuing units is not very large. To teach such a neuronetwork can be easy and fast. Cope even student in the thesis. As a result, the business saves on development, and the student gets a current thesis. Besides, every time we make a mistake, the neural net gets smarter.

As a result, instead of 30 minutes, the signing of the contract takes about 5. That is, with an eight-hour working day, 1 person will be able to conclude not 8 contracts (30 minutes for registration and 30 minutes for the road), but 13—14. Additionally, this is with a conservative approach — without electronic signature, access to the apartment through a mobile app and smart locks. However, I believe that immediately implement «fancy» solutions and do not need. There’s a high probability of spending money on something that doesn’t create value or reduce costs. This will be the next step after the client receives the result and competence.

Restrictions

Personally, I see the following limitations in this direction.

— Quality and quantity of data. Neuronets are demanding on quality and quantity of source data. However, this problem is being solved. If previously it was necessary to listen to several hours of audio recordings to synthesize your speech, now only a few minutes. Additionally, the next generation will only take a few seconds. However, they still need a lot of tagged and structured data. Additionally, every mistake affects the ultimate quality of the trained model.

— The quality of the «teachers». Neuronetworks teach people. Additionally, there are a lot of limitations: who teaches what, on what data, for what.

— Ethical component. I mean the eternal dispute of who to shoot down the autopilot in a desperate situation: an adult, a child or a pensioner. There are countless such disputes. There is no ethics, good or evil for artificial intelligence.

So, for example, during the test mission, the drone under the control of the AI set the task of destroying the enemy’s air defence systems. If successful, the AI would receive points for passing the test. The final decision whether the target would be destroyed would have to be made by the UAV operator. During a training mission, he ordered the drone not to destroy the target. In the end, AI decided to kill the cameraman because the man was preventing him from doing his job.

After the incident, the AI was taught that killing the operator was wrong and points would be removed for such actions. The AI then decided to destroy the communication tower used to communicate with the drone so that the operator could not interfere with it.

— Neural networks cannot evaluate data for reality and logic.

— The readiness of people. We must expect a huge resistance of people whose work will be taken by the networks.

— Fear of the unknown. Sooner or later, the neural networks will become smarter than us. Additionally, people are afraid of this, which means that they will retard development and impose numerous restrictions.

— Unpredictability. Sometimes it all goes as intended, and sometimes (even if the neural network does its job well) even the creators struggle to understand how the algorithms work. Lack of predictability makes it extremely difficult to correct and correct errors in neural network algorithms.

— Activity constraint. AI algorithms are good for performing targeted tasks, but do not generalize their knowledge. Unlike humans, an AI trained to play chess cannot play another similar game, such as checkers. In addition, even in-depth training is not good at processing data that deviates from his teaching examples. To use the same ChatGPT effectively, you need to be an industry expert from the beginning and formulate a conscious and clear request, and then check the correctness of the answer.

— Costs of creation and operation. To create neuronetworks requires a lot of money. According to a report by Guosheng Securities, the cost of learning the natural language processing model GPT-3 is about $1.4 million. It may take $2 million to learn a larger model. For example, ChatGPT only requires over 30,000 NVIDIA A100 GPUs to handle all user requests. Electricity will cost about $50,000 a day. Team and resources (money, equipment) are required to ensure their «vital activity». It is also necessary to consider the cost of engineers for escort.

P.S.

Machine learning is moving towards an increasingly low threshold of entry. Very soon it will be as a website builder, where basic application does not need special knowledge and skills.

Creation of neural networks and data-companies is already developing on the model of «service as a service», for example, DSaaS — Data Science as a Service.

The introduction to machine learning can begin with AUTO ML, its free version, or DSaaS with initial audit, consulting and data markup. At the same time, even data markup can be obtained for free. All this reduces the threshold of entry.

The branch neuronetworks will be created and the direction of recommendatory networks, so-called digital advisers or solutions of the class «support and decision-making system (DSS) for various business tasks» will be developed more actively.

I discussed the AI issue in detail in a separate series of articles available via QR and link.

Big Data (Big Data)

Big data (big data) is the cumulative name for structured and unstructured data. Additionally, in volumes that are simply impossible to handle manually.

Often this is still understood as tools and approaches to work with such data: how to structure, analyze and use for specific tasks and purposes.

Unstructured data is information that has no predefined structure or is not organized in a specific order.

Application Field

— Process Optimization. For example, big banks use big data to train a chat bot — a program that can replace a live employee with simple questions, and if necessary, will switch to a specialist. Or the detection of losses generated by these processes.

— Forecasting. By analysing big sales data, companies can predict customer behaviour and customer demand depending on the season or the location of goods on the shelf. They are also used to predict equipment failures.

— Model Construction. The analysis of data on equipment helps to build models of the most profitable operation or economic models of production activities.

— Sources of Big Data Collection

— Social — all uploaded photos and sent messages, calls, in general everything that a person does on the Internet.

— Machine — generated by machines, sensors and the «Internet of things»: smartphones, smart speakers, light bulbs and smart home systems, video cameras in the streets, weather satellites.

— Transactions — purchases, transfers of money, deliveries of goods and operations with ATMs.

— Corporate databases and archives. Although some sources do not assign them to Big Data. Here there are disputes. Additionally, the main problem — non-compliance with the criteria of «renewability» of data. More about this a little below.

Big Data Categories

— Structured data. Have a related table and tag structure. For example, Excel tables that are linked together.

— Semi-structured or loosely structured data. They do not correspond to the strict structure of tables and relationships but have «labels» that separate semantic elements and provide a hierarchical structure of records. Like information in e-mails.

— Unstructured data. They have no structure, order, hierarchy at all. For example, plain text, like in this book, is image files, audio and video.

Such data is processed on the basis of special algorithms: first, the data is filtered according to the conditions that the researcher sets, sorted and distributed among individual computers (nodes). The nodes then calculate their data blocks in parallel and transmit the result of the computation to the next stage.

Big data feature

According to different sources, big data have three, four and, according to some opinions, five, six or even eight components. However, let’s focus on what I think is the most sensible concept of four components.

— Volume (volume): Information should be a lot. Usually speak of quantity from 2 terabytes. Companies can collect a huge amount of information, the size of which becomes a critical factor in analytics.

— Velocity (speed): data must be updated, otherwise they become obsolete and lose value. Almost everything that happens around us (search queries, social networks) produces new data, many of which can be used for analysis.

— Variety (variety): generated information is heterogeneous and can be presented in different formats: video, text, tables, numerical sequences, sensor readings.

— Veracity (reliability): the quality of the data analysed. They must be reliable and valuable for analysis, so that they can be trusted. Low-fidelity data also contain a high percentage of meaningless information, which is called noise and has no value.

Restrictions on the Big Data Implementation

The main limitation is the quality of the raw data, critical thinking (what do we want to see? What pain? — This is done ontological models), the right selection of competencies. Well, and most importantly — people. Data-Scientists are engaged in work with the data. Additionally, there is one common joke: 90% of the data-scientists are data-satanists.

Digital doppelgangers

A digital double is a digital/virtual model of any object, system, process or person. In its conception, it accurately reproduces the shape and actions of the physical original and is synchronized with it. The error between the double and the real object must not exceed 5%.

It must be understood that it is almost impossible to create an absolute digital counterpart, so it is important to determine which domain is rationally modelled.

The concept of the digital counterpart was first described in 2002 by Michael Grieves, a professor at the University of Michigan. In the book «The Origin of Digital Doubles» he divided them into three main parts:

1) physical product in real space;

2) virtual product in virtual space;

3) data and information that combine virtual and physical products.

The digital double itself can be:

— prototype — the analogue of the real object in the virtual world, which contains all the data for the production of the original;

— a copy — a history of operation and data about all characteristics of the physical object, including the 3D model, the copy operates in parallel with the original;

— an aggregated double — a combined system of a digital double and a real object that can be controlled and shared from a single information space.

The development of artificial intelligence and the cheapening of the Internet of Things have made technology the most advanced. Digital doubles began to receive «clean» big data about the behaviour of real objects, it became possible to predict equipment failures long before accidents. Although the latter thesis is quite controversial, this direction is actively developing.

As a result, the digital double is a synergy of 3D technologies, including augmented or virtual reality, artificial intelligence, the Internet of Things. It’s a synthesis of several technologies and basic sciences.

The digital counterparts themselves can be divided into four levels.

• The double of the individual assembly unit simulates the most critical assembly unit. It can be a specific bearing, motor brushes, stator winding or pump motor. In general, the one that has the greatest risk of failure.

• The twin of the unit simulates the operation of the entire unit. For example, the gas turbine unit or the entire pump.

• The production system double simulates several assets linked together: the production line or the entire plant.

• Process counterpart — this is no longer about «hardware» but about process modelling. For example, when implementing MES- or APS-systems. We’ll talk about them in the next chapter.

What problems can digital duplicate technology solve?

• It becomes possible to reduce the number of changes and costs already at the stage of designing the equipment or plant, which allows to significantly reduce costs at the remaining stages of the life cycle. Additionally, it also avoids critical errors, which cannot be changed at the stage of operation.

— By collecting, visualizing and analyzing data, it is possible to take preventive measures before serious accidents and damage to equipment.

— Optimize maintenance costs while increasing overall reliability. The ability to predict failures allows to repair the equipment on the actual condition, and not on the «calendar». It is not necessary to keep a large amount of equipment in stock, that is, to freeze working capital.

Build the most efficient operating regimes and minimize production costs. The longer the accumulation of data and the deeper the analytics, the more efficient optimization will be.

It is very important not to confuse the types of forecasting. Lately, working with the market of various IT solutions, I constantly see confusion in the concepts of predictive analytics and machine detection of anomalies in the operation of equipment. That is, using machine detection of deviations, they speak about the introduction of a new, predictive approach to the organization of service.

On the one hand, both neural networks actually work. When machine detection of anomalies of the neuronet also finds deviations, which allows to perform maintenance to a serious failure and replace only worn-out element.

However, let’s take a closer look at the definition of predictive analytics.

A predictive (or predictive, predictive) analysis is a prediction based on historical data.

So, it’s the ability to predict equipment failures before the abnormality happens. When the operational performance is still normal, but already begin to develop trends to deviation.

If you go to a very domestic level, the detection of anomalies — it is when you have a change of pressure and you are warned about it before you have a headache or begin to have heart problems. And predictive analytics is when things are still normal, but you have changed your diet, your sleep quality or something, respectively, the processes in your body that will subsequently lead to an increase in pressure.

As a result, the main difference is the depth of the dive, the availability of the skills and the horizon of prediction. Anomaly detection is a short-term prediction to avoid a crisis. To do this, you do not need to study historical data for a long period of time, for example, several years.

A full-fledged predictive analysis is a long-term prediction. You get more time to make decisions and work out measures: plan the purchase of new equipment or spare parts, call a repair team at a lower price or change the mode of operation of the equipment to prevent any deviations.

That’s what I think, but maybe there are alternative opinions, especially from marketers. The most important constraint I see at the moment is the complexity and cost of technology. Creating mathematical models is long and expensive, and the risk of error is high. It is necessary to combine technical knowledge about the object, practical experience, knowledge in modelling and visualization, observance of standards in real objects. Not all technical solutions are justified, as not every company has all competencies.

So, I think it’s useful for the industry to start with accident analysis, to identify the critical components of the assets and to model them. That is, to use an approach from the system constraint theory.

This will, first, minimize the risk of errors. Second, to enter this direction at a lower cost and to get an effect on which you can rely in the future. Third, accumulate expertise in working with data, making decisions based on them and «complicating» models. Having your own data competence is one of the key conditions for successful digitalization.

It is worth remembering that for now it is a new technology. Additionally, on the same cycle Gartner, it must pass the «valley of disappointment». Then later, when the digital competencies become more common and the neural networks become more massive, we’re going to use the digital counterparts to the full.

Clouds, online analytics and remote control

The concept of digital transformation involves the active use of clouds, online analytics, and remote-control capabilities.

The National Institute of Standards and Technology (NIST) identified the following cloud characteristics:

— self-service on demand (self-service on demand) — the consumer determines his own needs: speed of access, productivity «iron», its availability, the amount of necessary memory;

— access to resources from any device connected to the network — it does not matter which computer or smartphone the user logs on from, as long as it is connected to the Internet;

— pooling of resources (resource pooling) — suppliers complete «iron» for quick balancing between consumers, that is, the consumer indicates what he needs, but the distribution between specific machines is assumed by the supplier;

— flexibility — the consumer can change the range of necessary services and their scope at any time without unnecessary communication and agreement with the supplier;

— automatic metering of service consumption.

However, what are the benefits of the cloud for business?

— Ability not to «freeze» resources by investing in fixed assets and future expenses (for repair, upgrade and modernization). This simplifies accounting and tax work, allows resources to be directed to development. Key — you can increase the number of digital tools without the need to constantly purchase server hardware and storage systems.

— Savings on the wage fund (ZP + taxes of expensive professionals for infrastructure maintenance) and operating system (electricity, rent of premises, etc.).

— Saves time to start and start using IT infrastructure or digital product.

— More efficient use of computing power. It is not necessary to build a redundant network to cover loads during the peak or to suffer from «brakes» and «glitches» of the system, to risk «falling» with data loss. This is the provider’s task, and it will fulfill it better. Plus, the principle of separation of responsibility is included, and data preservation is its task.

— Information availability in the office, at home and on business trips. This allows you to work more flexibly and efficiently, hire people from other regions.

There are many cloud technology models: SaaS, IaaS, PaaS, CaaS, DRaaS, BaaS, DBaaS, MaaS, DaaS, STaaS, NaaS. Let’s talk a little bit more about them.

— SaaS (Software as a Service) — Software as a service.

The client receives software via the Internet: mail services, cloud version 1C, Trello and so on. You can list it endlessly.

— IaaS (Infrastructure as a Service) — infrastructure as a service.

Provision of virtual servers, hard disks and any IT infrastructure for rent. It’s basically a replica of physical infrastructure, but you don’t have to buy it.

— PaaS (Platform as a Service) — Platform as a service.

Rent a full-fledged virtual platform, including both «iron» and database management systems, security systems and so on. The service is very popular with software developers.

These are the three most popular models that everyone should know about. And to better understand the details, consider the simple scheme below.

Difference between the three most common models

— CaaS (Communications as a Service) — communication as a service.

This is the provision of telephony, video communication, messengers and so on. At the same time, all the necessary software is located in the cloud provider.

— WaaS (Workplace as a Service) — Workplace as a service.

As the name suggests, this is the provision of workplaces with the installation and configuration of all the necessary software.

— DRaaS (Disaster Recovery as a Service) — Disaster recovery as a service.

— BaaS (Backup as a Service) — backup as a service.

— DBaaS (Data Base as a Service) — database as a service.

The client pays the rent depending on the number of users and the volume of the database.

— MaaS (Monitoring as a Service) — monitoring as a service.

Allows you to organize centralized monitoring of all IT-systems with a single-entry point.

— DaaS (Desktop as a Service) — Desktop as a service.

Remote desktops for rent from any device. In fact, it is a variety of WaaS.

— STaaS (Storage as a Service) — Storage as a service.

Providing disk space in the provider’s cloud. That is, a variety of IaaS. For a user, this space will be a regular network folder or a local drive.

— NaaS (Network as a Service) — Network as a service.

Includes routing tools, security management, and various network protocols.

For a better understanding I will give another example, from the series «Bugs» on TNT.

The whole 2nd season revolves around one main problem: bought «cheap» equipment cannot work steadily: the power is not enough, it overheats, then not enough power. Additionally, when one of the servers fails, the whole system collapses. Well, in the end, one of the partners copies the entire database onto a thumb drive and tries to escape with it.

As you can see, there are many limitations and bottlenecks in the system.

What would happen if the characters of the series chose IAAS or PAAS?

— At the start, they wouldn’t worry about losing a bag of money. You wouldn’t have to buy the cheapest, least efficient, least reliable equipment.

— It would not be necessary to think about cooling and overheating. You can safely work with your laptop in a comfortable environment.

— You wouldn’t have to go on a quest with stealing a transformer from a nearby village.

— There would be no scenes of jealousy and failure of the whole service due to server failure.

— The partner could not have copied the database and the program code to the external drive and erased everything from the laptop.

Another criterion for separating clouds is the deployment model.

— Private cloud

It is used within the same organization and among its contractors. Often within the «corporate» circuit. That is, it is not possible to access it from a working PC or not from an internal network. It is mostly managed by the organization itself, but also by the service provider.

As a rule, has performance limitations — slow access. There are also high costs: the economy is changing almost completely, it is no longer possible to save on staff, premises and so on. Additionally, another question of convenience. Corporate IT phones rarely know how to create workable solutions.

Among the advantages worth noting security. Well, and for a large business — economies of scale.

Creation of «clouds» on the basis of own data processing centers just can be attributed to private clouds, which is relevant for large business.

— Public Cloud

It is created for use by a large number of users, without being tied to any particular organization or IT infrastructure.

Of the advantages — high speed and, as a rule, usability.

— Public Cloud

Created for users from several organizations who share a common task.

— Hybrid Cloud

A combination of different clouds that remain unique but have a connection, such as for certain data transfer or load balancing.

Cloud computing has its drawbacks. I’ll give you the main ones.

— The Internet’s reliability requirements. It is sometimes necessary to have a permanent and reliable connection, which is difficult to organize.

— Requirements for Internet speed. Some applications do not work well with slow internet.

— Performance constraints are possible. Programs can run more slowly than on a local computer.

— Not all programs or their functionality are available remotely. This is especially true for «corporate» systems. Much is only available on a local system installed on a PC.

— Priorovisna — Building a private cloud requires considerable resources. Additionally, this is already disadvantageous for small and medium-sized businesses.

— Confidentiality of data. Most experts agree that confidential data should not be stored in a public cloud.

— Data availability. When choosing a SaaS solution, you must consider the availability of your data. Not all providers are ready to provide access to your data. Additionally, data is the blood of the digital economy.

— Data security can be compromised. This creates limitations in information security policies for large companies. However, the key word here is «can». It depends on who provides cloud services. If this someone securely encrypts the data, constantly makes their backups, has been in the market for such services for several years and has a good reputation, then the threat to data security may never occur. If data in the cloud is lost, it is lost forever. That’s a fact. However, losing it is much more difficult than losing it on a local computer.

I believe that clouds are needed even for offline business. As I wrote above, it helps managers to organize, including remote work (be productive everywhere), as well as to hire the best people without reference to the territory.

The question is, which approach to choose: IaaS, SaaS, PaaS?

I believe that the model of use depends on the scale of business. For small and medium-sized businesses more relevant than SaaS and PaaS. This allows more efficient use of existing resources and faster development.

For medium and large businesses, it makes sense to consider IaaS and PaaS. So, you save on economies of scale. In addition, you will be able to implement information security policy better and more flexibly.

Digital platforms

Digital platforms are one of the foundations of the transition to a digital economy. Digital platforms make it possible to unite «producers» and consumers on the same platform, eliminating unnecessary intermediaries. This is a reduction in information processing costs not only on the scale of a single process and a company, it is a fundamental restructuring of the market. Thanks to this, services and goods become more accessible, better and cheaper.

Digital platforms are the final point of many digital transformation strategies.

The platform is those products and services that connect two or more user groups in bilateral markets. The main difference between platforms and large IT systems is the access model. The platform implies free access. If there are limitations, for example, as for «State Services», it is more logical to talk about a large IT-system, or about «internal platform».