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Bot-bots - what is it

Bot/bots – what is it?

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In its broadest definition, a bot is an autonomous program designed to perform specific tasks online. Initially created for simple functions, bots have evolved to handle more complex tasks, which can have positive and negative impacts.

What are bots in mobile fraud?

In mobile fraud, bots are automated programs that can operate on real mobile devices or servers, mimicking legitimate user actions such as ad clicks, installs, and in-app engagement. This simulation aims to deceive systems into recognizing fraudulent activities as genuine.

Another type of fraud bot is malware/mobile malware installed on a user’s device. These malware bots generate fake ad impressions, fraudulent clicks, and in-app engagement, and can even initiate fake in-app purchases, all without the user’s consent or awareness.

How to block mobile fraud bots

  • Closed-source SDKs – ensure your attribution provider uses closed-source SDK technology. Unlike open-source SDKs, closed-source codes are significantly harder for fraudsters to unpack and simulate, as the code is not publicly exposed for review and reverse engineering. Review all SDKs in your app, particularly attribution SDKs, and avoid those that use open-source technology to prevent security breaches.
  • SDK security measures – implement hashing or unique tokens to block bot activity in real time. Always use the latest SDK version from your attribution provider to benefit from the most recent security updates and defenses against known bot tactics.
    • Hashing – this process transforms data into a fixed-size hash value, ensuring that sensitive information remains secure during transmission. Hashing helps to verify data integrity and detect any unauthorized changes.
    • Unique tokens – these are dynamically generated, single-use tokens that verify the authenticity of each request. By using unique tokens, you can ensure that each interaction is legitimate and prevent replay attacks where bots attempt to reuse old tokens to gain unauthorized access.
    • Encrypted communication – ensuring that all data transmitted between the app and the server is encrypted adds an additional layer of security, making it more difficult for bots to intercept and manipulate data.
    • Certificate pinning – this technique involves associating a host with their expected X.509 certificate or public key. By doing so, it prevents man-in-the-middle attacks, ensuring that the app communicates only with trusted servers.
    • Dynamic key generation – implementing dynamic keys that change with each session makes it harder for bots to crack the security measures in place, as they would need to break the encryption for each session individually.
    • Rate limiting and throttling – these measures help to control the number of requests a client can make to the server within a certain time frame. By setting these limits, you can prevent bots from overwhelming your system with requests, making it easier to detect and block suspicious activity.
  • Behavioral analysis and anomaly detection – monitoring active user behavior and identifying patterns that deviate from normal activities can help detect bot activities. Advanced solutions like Protect360 use proprietary behavioral anomaly detection to identify and block sources generating non-human traffic automatically.
  • Bot signatures – fraud solutions maintain a real-time database of bot signatures, automatically blacklisting and blocking activities from known fraudulent sources. These signatures include patterns of behavior, known IP addresses, device identifiers, and other unique markers that are characteristic of bot activities. By continuously updating this database with new signatures, fraud solutions can swiftly block any traffic that matches these patterns, effectively preventing bots from causing harm.
  • Behavioral anomalies – identify unusual behavior patterns, such as a high density of installs that follow identical, non-human actions. Solutions like Protect360 use proprietary behavioral anomaly detection to block sources generating such traffic automatically. This detection system monitors user interactions and flags behaviors that deviate significantly from typical human patterns, such as extremely rapid clicks, uniform time intervals between actions, or consistent usage patterns across multiple devices. By analyzing these anomalies, the system can distinguish between genuine user activity and automated bot behavior, ensuring that only legitimate interactions are allowed through. This process involves sophisticated machine learning algorithms that continuously learn and adapt to new bot behaviors, providing a robust defense against evolving threats.
Mobile malware - complex description of cyber criminals techniques

Mobile malware – complex description of cyber criminals techniques

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Mobile malware is harmful software designed to infiltrate mobile phones and tablets through ads or apps. Its purposes include stealing sensitive data, misusing device functions, holding the device for ransom, and generating fake traffic.

What is mobile malware?

Mobile malware refers to malicious software designed to access and harm mobile phones and tablets, typically via ads or apps. Its primary objectives are to steal sensitive data, misuse device functions, hold the device ransom, and create fake traffic. As mobile device usage has increased, so have the threats, with hackers adapting their tactics from desktops to mobile platforms. This evolution underscores the importance of robust mobile security measures.

How mobile malware operates

Mobile malware typically infiltrates devices via malicious apps, especially those downloaded from third-party app stores or side-loaded outside official channels. This malware can be pre-installed on some low-end devices or downloaded without the user’s knowledge through deceptive methods.

What kind of risks and damage are connected with mobile malware?

Mobile malware poses significant risks not only to individual users but also to businesses and mobile marketers. The consequences of mobile malware include:

  • Data breaches – personal and sensitive information can be stolen and misused.
  • Financial loss – through fraudulent transactions and ransomware.
  • Reputation damage – trust in affected apps and brands can be severely damaged.
  • Resource misuse – malware can lead to increased data usage, reduced battery life, and overall poor device performance.

Types and methods of mobile malware

  • Click injection/click hijacking – this method involves malicious apps that detect when a legitimate app is being downloaded and then inject a fake click to claim the attribution. Attackers benefit by earning fraudulent ad revenue meant for legitimate advertisers. This scam consumes device resources and data without user knowledge, leading to potential financial losses for advertisers who lose revenue to fraudsters.

    In more detail, click injection occurs when a malicious app monitors the device for app installation broadcasts. Upon detecting an installation, the malware swiftly generates a fake click that appears to originate from the app being downloaded. This deceives attribution systems into crediting the fraudulent app for the installation, thus diverting marketing budgets to the fraudster instead of the legitimate sources. Consequently, advertisers pay for fake installs, leading to wasted ad spend and skewed campaign metrics, which undermine the effectiveness of their marketing efforts. This not only results in immediate financial losses but also damages the overall efficiency and accuracy of marketing strategies, causing long-term detrimental impacts on the advertiser’s return on investment (ROI).

  • Data theft – malware designed to access and steal personal and financial information stored on the device. Attackers gain access to sensitive data such as login credentials, bank details, and personal information, which they can sell or use for identity theft and fraud. This compromises user privacy and can lead to significant financial and legal consequences for victims. Data theft is particularly dangerous because it targets the most sensitive information stored on a user’s device. Attackers can exploit stolen data in numerous ways, causing extensive harm to the victim:
    • Financial loss – stolen bank details and credit card information can be used to make unauthorized transactions, draining the victim’s accounts.
    • Identity theft – attackers can use personal information to create false identities, apply for loans, and credit cards, or commit other forms of fraud in the victim’s name.
    • Privacy invasion – access to personal data can lead to blackmail, harassment, or further exploitation.
    • Reputation damage – sensitive information leaked or misused can harm the victim’s personal and professional reputation.
    • Legal consequences – victims may face legal challenges if their stolen identity is used for illegal activities.
  • Ransomware – malware that locks the device or encrypts its data, demanding a ransom for its release.
    • How ransomware works – ransomware typically infiltrates a system through phishing emails, malicious downloads, or exploiting vulnerabilities in software. Once the malware is executed, it begins encrypting files on the victim’s device or network. The attackers then display a ransom note, often demanding payment in cryptocurrency to unlock the encrypted data.
      • Infection – the ransomware is delivered through deceptive methods, such as email attachments in spam or links to malicious websites. It can also exploit vulnerabilities in software.
      • Encryption – after gaining access to the system, the ransomware encrypts the victim’s files, making them inaccessible.
      • Ransom demand – a ransom note is displayed, demanding payment for the decryption key. The note typically includes instructions for payment, usually in cryptocurrency, to ensure anonymity.
    • Impact on victims – the impact of a ransomware attack can be devastating, leading to:
      • Financial losses – victims may lose significant amounts of money paying the ransom or dealing with the aftermath of the attack.
      • Data loss – even if the ransom is paid, there is no guarantee that the attackers will provide the decryption key.
      • Operational disruption – businesses can experience severe disruption, halting operations and affecting productivity.
      • Reputation damage – a ransomware attack can damage an organization’s reputation, leading to a loss of trust from customers and stakeholders.
    • Why attackers use ransomware – attackers use ransomware because it is a highly profitable form of cybercrime. The anonymity provided by cryptocurrencies makes it difficult for law enforcement to trace the transactions. Additionally, the widespread use of digital devices and the increasing value of data make ransomware an attractive option for cyber criminals.
    • Preventing ransomware attacks – preventing ransomware attacks involves a combination of proactive measures:
      • Regular backups – regularly back up important data to offline storage to ensure it can be restored without paying a ransom.
      • Security software – use robust antivirus and anti-malware software to detect and block ransomware.
      • Software updates – keep all software and systems updated to patch vulnerabilities that ransomware can exploit.
      • Employee training – educate employees about the risks of phishing and the importance of not clicking on suspicious links or attachments.
      • Access controls – implement strong access controls and limit user permissions to reduce the risk of ransomware spreading within a network.
  • Spyware – spyware refers to a type of malicious software that covertly monitors and records user activities on their devices without their knowledge. This form of malware can infiltrate systems through various means, such as malicious downloads, email attachments or compromised websites. Once installed, spyware can gather extensive information about the user, including their behavior, location, and communications, posing significant risks to both individuals and organizations.

    • How spyware works – spyware operates stealthily, often running in the background and avoiding detection by traditional security measures. It can record keystrokes, capture screenshots, track browsing habits, and even access personal files and emails. This collected data is then transmitted to the attacker, who can use it for various malicious purposes.
    • Why spyware is dangerous – spyware is dangerous because it covertly monitors and records user activities, leading to privacy breaches, blackmail, and unauthorized access to sensitive information. This type of malware can compromise user security, result in significant financial losses, and expose personal and corporate data to exploitation
      • Blackmail – attackers can use sensitive information obtained through spyware to extort money or other favors from victims. For instance, personal photos, private conversations, or confidential business information can be leveraged for blackmail. The fear of exposure can coerce victims into complying with the attackers’ demands, leading to financial and emotional distress.
      • Data breaches – collected data from spyware can be sold to other criminals on the dark web, leading to widespread exploitation. This information can include login credentials, credit card numbers, and personal identification details. The sale and misuse of this data can result in large-scale data breaches, affecting not only the individual victim but also potentially thousands of others if corporate data is compromised.
      • Unauthorized access – attackers can use the gathered information to gain access to other secure systems and accounts. This unauthorized access can lead to further security breaches, including accessing corporate networks, financial accounts, or personal emails. Once inside these systems, attackers can steal more data, cause disruptions, or deploy additional malware.
    • Why hackers use spyware – attackers benefit significantly from spyware in several ways:
      • Blackmail – attackers can use sensitive information obtained through spyware to extort money or other favors from victims. For instance, personal photos, private conversations, or confidential business information can be leveraged for blackmail. The fear of exposure can coerce victims into complying with the attackers’ demands, leading to financial and emotional distress.
      • Financial gain – selling stolen data or blackmailing victims provides a direct financial benefit. Additionally, spyware can be used to steal banking credentials and conduct unauthorized transactions.
      • Espionage – in some cases, spyware is used for corporate or state-sponsored espionage, gathering intelligence on competitors or foreign governments.
      • Control and manipulation – by accessing personal information, attackers can manipulate victims or further exploit their devices and networks for additional malicious activities.
  • Ad fraud – ad fraud involves malware that generates fake ad impressions and clicks, misusing device resources for fraudulent purposes. This malicious activity allows attackers to profit from ad revenue that should rightfully go to legitimate advertisers and publishers. The impact of ad fraud extends beyond financial losses (see attribution fraud), affecting device performance, user experience, and the integrity of the digital advertising industry.
    • How ad fraud works – ad fraud can be perpetrated through various methods, all of which exploit the digital advertising ecosystem to generate illegitimate revenue. The most common techniques include:
      • Fake ad impressions – malware generates false impressions of ads, making it appear as though real users are viewing them.
      • Click fraud – malware simulates user clicks on ads, falsely inflating click-through rates and generating revenue for the fraudsters.
      • Ad stacking – multiple ads are layered on top of one another, where only the top ad is visible, but impressions are counted for all ads in the stack.
      • Pixel stuffing – ads are placed in a 1×1 pixel frame, rendering them invisible to users but still generating impressions and clicks.
    • Impact on the advertising ecosystem
      • Advanced detection tools – use sophisticated algorithms and machine learning to identify and block fraudulent activity in real time.
      • Collaboration – industry stakeholders, including advertisers, publishers, and ad networks, must work together to share information and develop best practices.
      • Transparency – promoting transparency in the ad supply chain helps ensure that all parties can track ad delivery and performance accurately.
      • Regular audits – conducting regular audits of ad campaigns and traffic sources helps identify and mitigate fraudulent activities.
    • Why cybercriminals use ad fraud – attackers are drawn to ad fraud because of the significant financial gains it offers. By generating fake impressions and clicks, they can siphon off substantial ad revenue from legitimate stakeholders. The relative anonymity of digital transactions and the complexity of the advertising ecosystem make it challenging to track and eliminate fraud, further incentivizing malicious actors.
    • Preventing ad fraud – combating ad fraud requires a multifaceted approach that includes technological solutions, industry cooperation, and vigilant monitoring:
      • Advanced detection tools – use sophisticated algorithms and machine learning to identify and block fraudulent activity in real time.
      • Collaboration – industry stakeholders, including advertisers, publishers, and ad networks, must work together to share information and develop best practices.
      • Transparency – promoting transparency in the ad supply chain helps ensure that all parties can track ad delivery and performance accurately.
      • Regular audits – conducting regular audits of ad campaigns and traffic sources helps identify and mitigate fraudulent activities.
Application Programming Interface (API) - complex guideline about APIs (history, examples, benefits, types of APIs, API design principles, API lifecycle management and many more)

Application Programming Interface (API) – complex guideline about APIs (history, examples, benefits, types of APIs, API design principles, API lifecycle management and many more)

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API stands for Application Programming Interface, which is a software intermediary that allows two applications to communicate with each other. Every time you use an app like Facebook, send a quick message or check the weather on your phone, you’re using an API.

Developers use APIs to program apps to hide complexity, organize code, and design reusable components.

Examples of using API in daily life

When you use an app on a mobile phone, the app connects to the internet and sends data to a server. The server then reads this data, interprets it, performs the necessary actions, and sends it back to the phone. The app then interprets the data again and presents the required information to you in a readable form. That’s what an API is – everything happens through an API.

To explain this better, let’s take a familiar example.

Imagine you’re sitting at a table in a restaurant and you have a menu to choose from. The kitchen is the part of the “system” that prepares your order. However, the critical link that would communicate your order to the kitchen and deliver the food back to your table is missing. This is where the waiter or API comes in. The waiter is the messenger – or API – who receives your request or order and tells the kitchen – the system – what to do. Then the waiter delivers the answer; in this case, the food.

Here’s a real example of an API. You may be familiar with the process of searching for flights online. Just like a restaurant, you have a variety of options to choose from, including different cities, departure and return dates, and more. Imagine you book a flight on an airline’s website. You choose a departure city and date, a return city and date, cabin class, and other variables as well.

To book the flight, you interact with the airline’s website to access their database and find out if there are seats available on those dates and what the cost might be.

But what if you don’t use the airline’s website – a channel that has direct access to information? What if you’re using an online travel service like Kayak or Expedia that aggregates information from several airline databases?

In this case, the travel service communicates with the airline’s API. The API is an interface that, like your helpful waiter, can be asked by this online travel service to retrieve information from the airline’s database to book seats, baggage options, etc. The API then takes the airline’s response to your request and passes it back to the online travel service, which then displays the most up-to-date and relevant information to you.

Early beginnings

APIs, or Application Programming Interfaces, have roots dating back to the early days of computing in the 1940s and 1950s. Initially, APIs were developed as reusable software libraries and subroutines to facilitate communication within a single system. This era marked the beginning of APIs as tools for streamlining development and fostering code reuse.

Mainframe era

During the 1960s and 1970s, APIs became more structured and formalized, particularly in the mainframe era. IBM’s introduction of the System/360 family of mainframe computers included comprehensive APIs for hardware access and resource management, embedding APIs deeply into software development processes.

The rise of personal computing

The 1980s and 1990s brought personal computing into the mainstream, with operating systems like Windows and macOS. APIs became essential for developing desktop applications, with Microsoft’s Windows API (WinAPI) and Apple’s Macintosh Toolbox API providing critical functions for graphics, file systems, and user interfaces, thus enhancing the development process significantly.

The internet and web APIs

The mid-1990s saw the internet revolutionize the role of APIs. Web APIs, which allowed different web services to interact over HTTP, emerged during this period. SOAP (Simple Object Access Protocol), introduced in 1998, was among the earliest protocols enabling remote procedure calls over the internet, widely adopted for enterprise integration.

The RESTful revolution

In the early 2000s, REST (Representational State Transfer) emerged as a simpler, more flexible alternative to SOAP. Defined by Roy Fielding in his 2000 doctoral dissertation, REST principles became foundational for web API design. RESTful APIs gained popularity for their simplicity, scalability, and adherence to web standards, becoming the de facto standard for web services.

Mobile and cloud computing

The late 2000s and 2010s saw the rise of mobile and cloud computing, further expanding API importance. Mobile apps relied heavily on APIs to access cloud services, integrate with social media, and interact with backend systems. Companies like Google, Facebook, and Twitter provided robust APIs, enabling developers to build feature-rich mobile applications.

Cloud platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform offered APIs for virtually all their services, from storage and computing to machine learning and analytics. This API-centric approach allowed developers to build scalable and flexible applications, leveraging the power of cloud infrastructure.

Modern API ecosystem

Today, APIs are the backbone of modern software development. The API economy has flourished, with businesses generating significant revenue by providing APIs as products. Companies like Stripe, Twilio, and Plaid are prime examples of businesses built entirely around their APIs, offering services that enable payments, communications, and financial data access, respectively.

Future trends and advancements

As technology continues to evolve, so do APIs. Future trends include the increased use of APIs in artificial intelligence, machine learning, and the Internet of Things (IoT). APIs are also becoming more secure and robust, with advancements in API security protocols and practices to protect against cyber threats.

The future of APIs will likely see greater standardization, improved developer tools, and more powerful integrations, further cementing their role as essential components of modern software development and digital transformation.

Types of APIs

APIs (Application Programming Interfaces) play a crucial role in modern software development, allowing different applications to communicate and interact with each other. They come in various forms, each serving different purposes and audiences. Understanding the different types of APIs is essential for developers and businesses to leverage their potential effectively.

Open APIs

Open APIs, also known as external or public APIs, are available to any developer who wants to use them. These APIs are designed to be easily accessible and are typically well-documented to encourage widespread use. Open APIs are a driving force behind the success of many modern web services and applications, enabling third-party developers to integrate and extend the functionalities of existing platforms.

Examples of open APIs

  • Google Maps API – allows developers to integrate Google Maps into their websites or applications, providing users with location-based services.
  • Twitter API – enables developers to access and interact with Twitter data, allowing for the creation of apps that can post tweets, read timelines, and more.

Open APIs foster innovation by allowing developers to build on top of established platforms, creating new applications and services that benefit from the robust functionalities of the original service.

Partner APIs

Partner APIs are shared externally but only with specific business partners. These APIs are not publicly available and require special access permissions. Partner APIs are designed to enable collaboration between companies, allowing them to integrate their systems and share data securely. They are often used to strengthen business relationships and create seamless experiences across different platforms.

Examples of partner APIs

  • Amazon Marketplace Web Service (MWS) – provides Amazon sellers with access to Amazon’s vast resources for managing inventory, orders, and reports.
  • Salesforce Partner APIs – allow partners to integrate their applications with Salesforce, enhancing the CRM’s functionality and providing tailored solutions for mutual customers.

Partner APIs typically include stringent security measures, such as authentication tokens and encryption, to protect sensitive data and ensure that only authorized users can access the API.

Internal APIs

Internal APIs, also known as private APIs, are used within an organization to streamline internal processes and improve system interoperability. These APIs are not exposed to external users and are designed to connect different internal systems, enabling them to work together more efficiently.

Examples of internal APIs

  • HR systems integration – an internal API might connect the HR system with the payroll system, ensuring that employee data is consistent and up-to-date across both platforms.
  • Internal microservices – in a microservices architecture, internal APIs allow different services within the same organization to communicate and function cohesively.

Internal APIs help organizations optimize their workflows, reduce redundancy, and enhance the overall efficiency of their operations by facilitating seamless data exchange between internal systems.

API design principles

REST (Representational State Transfer)

RESTful APIs follow a set of architectural principles that emphasize stateless communication, resource-based URIs, and the use of standard HTTP methods.

  • Stateless communication – each request from a client to a server must contain all the information needed to understand and process the request. The server does not store any state about the client session on the server side.
  • Resource-based URIs – resources are identified using URIs (Uniform Resource Identifiers). Each resource can be accessed through a unique URL, which represents a specific piece of data.
  • Standard HTTP methods – RESTful APIs utilize standard HTTP methods such as GET, POST, PUT, DELETE, and PATCH to perform operations on resources. For example, GET retrieves a resource, POST creates a new resource, PUT updates an existing resource, and DELETE removes a resource.

Advantages of REST

  • Scalability – statelessness and resource-based URIs make REST APIs highly scalable.
  • Flexibility – REST can be used over any protocol, but it is commonly used with HTTP.
  • Simplicity – RESTful APIs are easy to understand and implement, especially for web services.

Example of RESTful API code:

GET /users/123
Host: api.example.com

This request retrieves the user resource with the ID 123.

SOAP (Simple Object Access Protocol)

A protocol for exchanging structured information in web services, using XML for message format and relying on application layer protocols, mainly HTTP and SMTP.

  • XML-based messaging – SOAP uses XML to encode its messages, which allows for a highly standardized format that is both human-readable and machine-readable.
  • Protocol independence – SOAP can be used over various protocols such as HTTP, SMTP, TCP, and more.
  • WS-security – SOAP supports various security features defined in the WS-Security standard, making it suitable for applications requiring high security.

Advantages of SOAP:

  • Formal contracts – SOAP uses WSDL (Web Services Description Language) to describe the services, which provides a formal contract between the client and server.
  • Extensibility – SOAP’s protocol neutrality and language independence make it highly extensible and versatile.
  • Reliability – SOAP has built-in error handling and can be used for reliable messaging.

Example of SOAP code:

<soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns:ex="http://example.com/">
<soapenv:Header/>
<soapenv:Body>
<ex:getUser>
<ex:userId>123</ex:userId>
</ex:getUser>
</soapenv:Body>
</soapenv:Envelope>

This SOAP message requests user information for the user with ID 123.

GraphQL – A query language for APIs that allows clients to request exactly the data they need, providing more flexibility compared to REST.

  • Client-specified queries – clients can specify the structure of the response, ensuring they receive only the data they need.
  • Single endpoint – unlike REST, which uses different endpoints for different resources, GraphQL uses a single endpoint to handle all queries.
  • Real-time data – GraphQL supports real-time data with subscriptions, allowing clients to receive live updates.

Advantages of GraphQL:

  • Efficiency – reduces over-fetching and under-fetching of data, as clients request exactly what they need.
  • Flexibility – allows for more flexible and dynamic queries compared to REST.
  • Strong typing – the schema defines types and relationships, which helps in validating queries and maintaining robust APIs.

Example:

query {
user(id: "123") {
name
email
}
}

This query retrieves the name and email of the user with ID 123.

API lifecycle management

API lifecycle management encompasses the stages of designing, developing, testing, deploying, and maintaining APIs. Each phase requires careful planning and execution to ensure the API remains functional, secure, and efficient. Effective lifecycle management helps in delivering robust and scalable APIs, facilitating seamless integration, and ensuring ongoing performance and security.

Design

Designing APIs involves defining endpoints, request/response formats, and authentication methods. Best practices include:

  • Consistent naming conventions – ensure endpoint names are clear and logical.
  • Comprehensive documentation – provide detailed instructions and examples to guide developers.
  • Backward compatibility – design APIs to support future versions without breaking existing integrations.

Development

Development involves coding the API endpoints and integrating them with backend services. Common tools and frameworks include:

  • Express.js for Node.js – a minimal and flexible Node.js web application framework.
  • Flask for Python – a micro web framework for Python.
  • Spring Boot for Java – a framework that simplifies the creation of stand-alone, production-grade Spring-based applications.

Testing

Testing ensures that APIs function correctly and meet performance requirements. Types of tests include:

  • Unit tests – testing individual components.
  • Integration tests – verifying that different components work together.
  • Load tests – assessing the API’s performance under heavy load. Common tools for testing include Postman and SoapUI.

Deployment

Deployment involves making the API available to users. This phase includes:

  • Setting up servers – ensure reliable and scalable server infrastructure.
  • Configuring load balancers – distribute incoming network traffic across multiple servers.
  • Implementing security measures – protect the API from unauthorized access and threats.

Maintenance

Ongoing maintenance includes:

  • Monitoring performance – continuously track API performance to detect and resolve issues promptly.
  • Fixing bugs – address any issues that arise during usage.
  • Updating the API – implement new features, improve performance, and ensure compliance with the latest security standards.

API Testing Tools

Effective API testing is crucial for ensuring that APIs function correctly, meet performance requirements, and provide secure communication between systems. Various tools are available to help developers and testers verify the functionality, performance, and security of APIs. Here, we introduce some of the most popular API testing tools: Postman, SoapUI, and JMeter, and explain how they contribute to robust API testing processes.

Postman

Postman is a widely used tool for API development and testing. It provides an intuitive user interface that simplifies the process of sending requests to APIs and analyzing their responses.

  • Request building – Postman allows users to create and save HTTP requests with various methods (GET, POST, PUT, DELETE, etc.), headers, parameters, and body content.
  • Automation and scripting – Postman supports the creation of automated tests using JavaScript. Users can write scripts to validate responses, check performance, and ensure compliance with API specifications.
  • Collections and environments – Users can organize requests into collections and create environments to manage different configurations, such as development, staging, and production.
  • Mock servers – Postman allows the creation of mock servers to simulate API responses, enabling testing even when the real API is not available.
  • Collaboration – Postman provides collaboration features, allowing teams to share collections, tests, and documentation seamlessly.

SoapUI

SoapUI is a robust tool specifically designed for testing SOAP and REST web services. It offers comprehensive features for functional testing, security testing, and load testing.

  • Functional testing – SoapUI allows users to create and run automated functional tests for SOAP and REST APIs. It supports complex test scenarios, data-driven testing, and assertions.
  • Security testing – SoapUI includes features for security testing, such as SQL injection, XML bomb, and cross-site scripting (XSS) tests. It helps identify potential security vulnerabilities in APIs.
  • Load testing – SoapUI supports load testing to evaluate the performance and scalability of APIs under different conditions. Users can simulate multiple concurrent users and analyze the impact on API performance.
  • Service virtualization – SoapUI enables the creation of virtual services that mimic the behavior of real APIs. This feature is useful for testing and development when the actual API is unavailable.

JMeter

JMeter is an open-source tool primarily used for performance testing and load testing of web applications, including APIs. It provides a flexible and extensible platform for creating test plans and analyzing results.

  • Load testing – JMeter allows users to simulate a large number of concurrent users to test the scalability and performance of APIs. It provides detailed metrics on response times, throughput, and error rates.
  • Distributed testing – JMeter supports distributed testing, enabling the execution of tests across multiple machines to generate higher loads and simulate real-world scenarios.
  • Protocol support – JMeter supports various protocols, including HTTP, HTTPS, SOAP, REST, FTP, and more, making it a versatile tool for testing different types of APIs.
  • Custom plugins – JMeter’s extensibility allows users to develop custom plugins or use existing ones to enhance their functionality and tailor it to specific testing needs.
  • Reporting and analysis – JMeter generates comprehensive reports with graphs and statistics, helping users analyze the performance and identify bottlenecks in the API.

These tools collectively provide a comprehensive suite for ensuring that APIs are robust, secure, and performant, enabling developers to deliver high-quality software products. But there are sure many other tools you can use for API testing, which I probably do not know. If you have a hint, you can write it in the comments. 🙂

API documentation

Proper API documentation is crucial for several reasons. It ensures that developers understand how to interact with the API, which reduces the learning curve and promotes efficient development. Well-documented APIs improve integration, minimize errors, and enhance the overall developer experience. Good documentation also supports maintenance and scalability, as it provides clear guidelines for future updates and enhancements.

Comprehensive API documentation should include:

  • Endpoints – a list of available endpoints with descriptions of their purposes.
  • Parameters – detailed information about required and optional parameters for each endpoint, including data types and constraints.
  • Request/Response formats – examples of request and response payloads, including headers and body content.
  • Authentication methods – instructions on how to authenticate requests, including tokens, keys, or other methods.
  • Error codes – a list of possible error codes with explanations to help developers troubleshoot issues.
  • Examples – practical examples of API requests and responses to demonstrate typical usage.

There are several tools available to help create and manage API documentation effectively:

  • Swagger (OpenAPI) – a powerful framework for API documentation that allows you to describe the structure of your APIs in a machine-readable format.
  • Postman – a versatile tool for testing APIs that also offers features for generating and managing documentation.
  • Redoc – a tool for generating interactive API documentation from OpenAPI specifications.
  • Slate – a static site generator for API documentation that creates clean and readable documentation.
  • Apiary – a comprehensive platform for designing, testing, and documenting APIs.

Why do we need an API?

Whether you’re managing existing tools or designing new ones, you can use APIs to simplify the process. The main benefits of APIs include the following:

  • Improved collaboration – the average enterprise uses nearly 1,200 cloud-based applications (legacy is located outside of IBM), many of which are unconnected to each other. APIs enable integration so that these platforms and applications can seamlessly communicate with each other. With this integration, businesses can automate workflows and improve collaboration in the workplace. Without APIs, many businesses would lack connectivity and suffer from information silos that threaten productivity and performance.
  • Easier innovation – APIs offer flexibility and allow companies to connect with new business partners, offer new services to existing markets, and ultimately gain access to new markets that can bring huge profits and drive digital transformation. For example, Stripe started as an API with just seven lines of code. Since then, the company has partnered with many of the world’s largest enterprises, diversified into lending and corporate cards, and was recently valued at $36 billion (link is outside IBM).
  • Monetizing data – many companies choose to offer APIs for free, at least initially, to build an audience of developers around their brand and establish relationships with potential business partners. However, if an API provides access to valuable digital assets, it can be monetized by selling access (this is known as the API economy). When AccuWeather (link is based out of IBM) launched its self-service developer portal to sell a wide range of API packages, it took just 10 months to attract 24,000 developers, sell 11,000 API keys, and build a thriving community in the process.
  • Security – as mentioned above, APIs create an extra layer of protection between your data and the server. Developers can further strengthen API security by using authentication tokens, signatures, and Transport Layer Security (TLS) encryption; implementing API gates to manage and authenticate traffic; and practical, efficient API management.

APIs and security

The data from your phone is never fully accessible to the server, nor is the server ever fully accessible to your phone. Instead, they communicate with each other using small data packets, sharing only what is necessary, such as takeaway food orders. You tell the restaurant what you’d like to eat, it tells you what it needs in return, and eventually, you get your food.

APIs have become so valuable that they make up a large part of many businesses’ revenue. Big companies like Google, eBay, Salesforce.com, Amazon, and Expedia are just a few of the companies that make money from their APIs. The term “API economy” refers to this API market.

But also due to the very high usage of APIs (Application Programming Interfaces) in modern life, APIs have become critical in modern software developmen. However, this increased connectivity also introduces significant security risks. Ensuring the security of APIs is paramount to protect sensitive data, maintain user trust, and comply with regulatory requirements. Here, we discuss the importance of API security and various methods to ensure it.

Authentication and authorization for API

Authentication and authorization are fundamental to API security. Authentication verifies the identity of a user or system, while authorization determines what actions they are permitted to perform.

  • Tokens – tokens are a common method for API authentication. They are generated after a successful login and are used to verify the identity of the user or application making the request. Tokens, such as JSON Web Tokens (JWT), provide a secure way to manage user sessions.
  • OAuth – OAuth is an open standard for access delegation commonly used for token-based authentication. It allows third-party services to exchange information without exposing user credentials. OAuth 2.0 is widely used for securing APIs, providing a robust framework for handling access permissions and scopes.
  • API keys – API keys are unique identifiers that authenticate a request to an API. While not as secure as OAuth or tokens, API keys are still used for simpler authentication scenarios. They should be used with caution and combined with other security measures to enhance protection.

Data encryption for API

Data encryption ensures that the information exchanged between clients and servers remains confidential and secure from eavesdroppers.

  • Transport layer security (TLS) – TLS is a cryptographic protocol designed to provide secure communication over a computer network. It encrypts the data transmitted between the client and the server, making it unreadable to anyone intercepting the communication. Implementing TLS is essential for protecting sensitive information such as user credentials and personal data.
  • End-to-end encryption – in scenarios requiring heightened security, end-to-end encryption can be employed. This ensures that data is encrypted on the sender’s side and only decrypted on the receiver’s side, preventing intermediaries from accessing the content.

Rate limiting for API

Rate limiting is a technique used to control the amount of incoming and outgoing traffic to and from a server. It helps prevent abuse, such as denial-of-service (DoS) attacks and ensures fair usage among users.

  • Request quotas – setting quotas for the number of requests a client can make within a specified time frame helps mitigate the risk of DoS attacks. Exceeding the quota results in the request being denied, protecting the API from being overwhelmed by too many requests.
  • Throttling – throttling limits the rate at which a client can make requests. If a client exceeds the rate limit, subsequent requests are either delayed or rejected. Throttling is useful for managing the load on the server and maintaining performance.
  • IP whitelisting – restricting access to APIs based on IP addresses ensures that only trusted clients can make requests. This method can effectively prevent unauthorized access and reduce the risk of abuse from unknown sources.

Additional security measures for API

Beyond authentication, authorization, data encryption, and rate limiting, several other security practices can enhance API security.

  • Input validation – validating and sanitizing all inputs to the API helps prevent common attacks such as SQL injection and cross-site scripting (XSS).
  • Logging and monitoring – implementing robust logging and monitoring mechanisms allows for the detection and investigation of suspicious activities. Monitoring API usage patterns can help identify and mitigate potential security threats.
  • Security audits and penetration testing – regular security audits and penetration testing can uncover vulnerabilities in the API. These proactive measures enable developers to address security flaws before they can be exploited by malicious actors.
  • Versioning and deprecation – managing API versions ensures that outdated and potentially insecure versions are phased out systematically. Deprecation policies guide users to migrate to newer, more secure versions of the API.

Modern APIs

Over the years, the term “API” has often referred to any kind of generic interface for connecting to an application. Recently, however, modern APIs have acquired some characteristics that make them extremely valuable and useful:

  • Modern APIs follow standards (typically HTTP and REST) that are developer-friendly, easily accessible, and widely understood.
  • They are treated as products rather than code. They are intended to be consumed by a specific audience (e.g. mobile developers), documented, and versioned so that users can have certain expectations about their maintenance and lifecycle.
  • Because they are much more standardized, they have a much stronger discipline for security and governance, as well as being monitored and managed for performance and scope.
  • Like any other software produced, modern APIs have their own software development life cycle (SDLC) that includes design, testing, build, maintenance, and versioning.
  • Modern APIs are also well-documented for consumption and versioning.

Future trends in APIs

The future of APIs promises to be dynamic and transformative, driven by advancements in AI, machine learning, and IoT. As technology evolves, APIs are expected to play an even more crucial role in enabling seamless integration, enhancing functionalities, and fostering innovation. Here are some key trends to watch:

AI and machine learning integration

  • Enhanced capabilities – AI and machine learning will empower APIs to offer more advanced functionalities. APIs will be able to provide predictive analytics, natural language processing, and personalized user experiences, making applications smarter and more responsive to user needs.
  • Automated API development – AI tools will streamline the API development process by automating code generation, testing, and documentation. This will reduce development time and ensure higher quality and consistency in API creation.
  • Adaptive APIs – machine learning algorithms will enable APIs to adapt to changing user behaviors and environmental conditions in real-time, improving performance and user satisfaction.

IoT impact on API development

  • Interoperability – the proliferation of IoT devices will necessitate APIs that can facilitate communication between a vast array of devices and platforms. APIs will need to be more versatile to handle diverse data formats and protocols.
  • Security enhancements – with the increase in IoT devices, security will be paramount. APIs will incorporate more robust security measures, including advanced encryption, tokenization, and anomaly detection to protect sensitive data.
  • Real-time data processing – APIs will be designed to handle real-time data streams from IoT devices, enabling instantaneous data analysis and decision-making. This will be crucial for applications in smart homes, healthcare, and autonomous vehicles.

API Monetization and Ecosystems

  • API marketplaces – the growth of API marketplaces will make it easier for developers to discover, access, and integrate APIs. These platforms will offer APIs as services, enabling businesses to monetize their digital assets effectively.
  • Subscription models – APIs will increasingly adopt subscription-based models, providing tiered access to different levels of service. This will allow companies to generate steady revenue streams while offering scalable solutions to users.
  • Partnerships and collaborations – APIs will foster more partnerships and collaborations between companies, allowing them to leverage each other’s strengths and create more comprehensive solutions.

Developer experience and tooling

  • Improved documentation – the emphasis on developer experience will lead to more comprehensive and user-friendly API documentation. Interactive documentation with real-time testing capabilities will become standard.
  • Unified development environments – integrated development environments (IDEs) will offer built-in support for API development, testing, and deployment. This will streamline the workflow for developers and reduce the time required to bring APIs to market.
  • API analytics and monitoring – advanced analytics and monitoring tools will provide deeper insights into API usage, performance, and security. This will enable proactive maintenance and optimization of APIs.

Consent Management Platform (CMP) – what is it?

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In a digital world where data protection and privacy are becoming increasingly important, the need for proper management of user consent is increasingly discussed. A Consent Management Platform (CMP), or consent management platform, is a tool that helps websites and online services manage and document users’ consents for the collection and processing of their data.

The importance of CMP is becoming increasingly relevant, primarily due to the strict requirements of the European GDPR and other local data protection laws.

What are CMPs?

CMPs are an essential tool for any organization that operates online and processes users’ personal data. They not only provide the necessary legal protection but also help build trust and transparency in the digital environment. As the digital environment continues to be dynamic and regulation is constantly evolving, it is important that organisations keep their consent management systems up-to-date and compliant with the latest legal requirements.

With the growing emphasis on data protection and increasing demands for transparency from users, the role of CMPs can be expected to continue to grow in the Czech Republic. Regulations will evolve and technology will improve, leading to a continuous evolution of consent management standards and practices.

A brief introduction to the legislative/legal framework of privacy protection

In every EU country, every company/individual who processes personal data must comply with the GDPR rules (GDRP – sets out the rules for the protection of personal data). CMPs are key to meeting these rules because they allow users to choose what cookies and other tracking technologies can be used during their visit to the site.

How do CMPs work?

A CMP typically integrates an interactive interface on a web page that is displayed to users on their first visit. This interface offers users the ability to choose which cookies and tracking technologies can be activated. All choices are recorded and stored as evidence that the website respects the user’s consent.

CMP is actually such an automated cookie consent that takes care of regular cookie scanning, often also serving as an automatic cookie policy generator in multiple languages to ensure consent management for compliance with international privacy laws such as GDPR, ePrivacy, LGPD, CCPA, and PDPA.

Legal requirements for CMP

  • Compliance with GDPR and local laws – CMP must be designed to meet the requirements of the General Data Protection Regulation (GDPR) and other relevant data protection laws in your country. This includes ensuring transparency and providing information to users about what data is being collected, for what purpose, and how long it will be retained.
  • Right to access and rectification (right to revoke users’ approval) – CMPs should allow users to easily access their personal data and request its rectification or deletion.
  • Documentation of consent – The CMP must effectively document all consents to demonstrate when and how consent was given or withdrawn. These records must be retained for as long as necessary to comply with legal requirements. Withdrawing consent must be as “complex” as giving it (i.e. you can’t hide it behind more and more layers; CookieBot does it nicely, for example, where you have to click on a floating icon even after consent has been given, and you can click on it at any time to change the consent). Next, you need to have elaborated terms and conditions – in a separate document, which you often find on other sites such as “Privacy Policy” or “Privacy Statement”. This document should detail how your organization collects, stores, processes, and protects users’ personal data. It should also inform users of their rights related to data protection, such as the right to access, rectify, erase, and restrict the processing of their personal data. The document should be easily accessible to users, usually located on the website in a visible section, often at the bottom of the website (in the ‘footer’) or on the contact page. It is important that this document is up-to-date and reflects all applicable legislation, including the GDPR if you do business with or process EU citizens’ data. Again, often this document (at least in relation to cookie processing) can be automatically generated for you by some CMPs and can be maintained automatically (for example, which services use cookie processing on your site).

Benefits of using a CMP

  • Ensuring legal compliance – CMP helps businesses comply with GDPR and other data protection regulations.
  • Improving transparency – users have a clear view of what data is collected and how it is used.
  • Increasing trust – transparent and fair treatment of user data increases user trust in the brand.
  • Customizing the user experience – CMP allows users to control which types of cookies are active, which can improve their online experience.

Important CMP features for website owners

  • Consent Management UI – An intuitive and easy-to-use interface that allows users to easily manage their cookie and tracking preferences. This includes the ability to grant, deny or change consent for different categories of cookies according to the applicable legal framework (ability to revoke consent at the individual cookie level, ability to revoke consent at any time, failure to file a consent revocation). For more complex projects, you will also appreciate the ability to copy settings of individual domains between each other, manage access/roles and other advanced features – for example, you can choose for which legislation you need a given CMP for a given site and how it should behave – ideally you should be able to customize everything, as well as individual languages/translation options, document generation).
  • Easy integration – the CMP should be designed to allow easy integration with existing systems and infrastructure on the site. This includes compatibility with different web platforms and technologies, minimizing the technical requirements and time required for deployment.
  • Integration with analytics and advertising tools – The CMP should be compatible with commonly used analytics and advertising tools to ensure that any data collection using these tools is consistent with user consent.
  • Adaptability and extensibility – Given the ever-changing legal standards and technological advances, the CMP should be designed to be easily upgradable and extensible, allowing for the addition of new features or integration with new services. This is generally provided by the CMP provider itself, who you pay for the solution (but the ultimate responsibility is yours and the most you can do is to seek legal redress from the CMP provider – on the other hand, it should be said that the largest CMP providers generally have the following
  • Automation and reporting – automated reports and alerts that inform site owners of the status of consents, missing consents or the need to renew them. These tools help maintain compliance with minimal effort.
  • Multi-language support – Given the global nature of the Internet and the diversity of user languages, a CMP should be able to support a multilingual environment, allowing proper communication with users in their preferred language.
  • Data security and protection – ensuring that all data collected is securely stored and protected from unauthorized access or data leakage. This includes the use of encryption and other security protocols.
  • Auditability and compliance reports – providing tools to generate auditable reports that can be used in inspections or audits to demonstrate compliance.
  • GDPR compliance and compliance testing – an important feature of CMP is the ability to perform compliance tests to verify that consent settings on a site comply with GDPR and other legal standards. These tests help identify and address potential issues before they become subject to scrutiny by regulators. Some tools have checklists or some semi-automatic guidelines/checklists for new users, who are not very aware of the current legislation.
  • Fully customizable cookie bar to your design – The CMP should offer customization options that allow it to be seamlessly integrated into the site design. This includes adapting colors, fonts, layout, and other visual elements to ensure that the consent management interface feels natural and does not discourage users. Last but not least, this is where maximum customizability of the bar plays a big role (as standard settings often don’t ensure that you get the most cookies/consents collected from users). Often you need to customize the look and feel (such as overflow across the entire site, where you can’t continue working with the site without clicking some form of consent, the ability to un-click all consents with one click and have this as a highly visible option).
  • Reporting and analysis of user behavior – the analytics tools integrated into CMP provide valuable insights into how users interact with the consent interface. This information can help optimize the design and content of notifications to achieve higher consent rates. Pretty much an essential feature if your CMP consent numbers have started to drop dramatically, as it will directly impact both analytics tools (how much you measure/see in Google Analytics) and ad campaigns (how many cookies you include in each audience based on activity from the site).
  • A/B testing for optimal cookie banners/CMP banners – using A/B testing allows you to experiment with different versions of the alert text to see which wording is most effective in gaining user consent. This contributes to a better understanding of user preferences and increases the effectiveness of consent management.
  • Versioning – once you change settings/design, it’s definitely nice to be able to restore previous settings with one click. Alternatively, have the ability to preview and test/preview changes before publishing them to the live site.
  • Integrated site cookie scanner/scan cookies – the cookie scanning feature allows CMP to automatically identify and categorize cookies used on the site. This facilitates consent management and ensures that users have accurate information about what cookies are being used and for what purposes.
  • Multi-language viewing – Support for multilingual content is also important – this is particularly important for global sites and brands. CMPs should be able to display consent information in multiple languages (so that users can understand it all), which helps achieve better understanding and compliance with international users.
  • Pricing/CMP price and billing schema – probably the main selection criteria, but certainly not the only one. Many CMPs are indeed cheaper and suitable for one domain (smaller project), but again for global sites, they are completely unsuitable – what I encounter most often – CMPs do not have legislation/interfaces sorted out for other countries – for example outside the EU, they cannot translate the CMP bar into the language, you need, they don’t have an intuitive interface for managing dozens of sites, or they are too expensive for larger sites (charging by subpages), or each of these functionalities means an extra charge that you didn’t count on when choosing a CMP.
  • CMP speed and size – both the speed of loading the CMP and its size – may not be as much of a concern for us today, but some CMPs have several hundred kb to load, while other solutions can do the same in units or tens of kb.
  • Certified CMP vendor for Google – when managing Google Consent, for example, again quite an important point for publishers – if you have a non-Google certified solution, your site will appear in all Google services as if it doesn’t have integration to Google Consent v2 – so somewhere you will get another window popping up from Google just for Google Consent v2/Google Consent 2.0.

DMA (Digital Marketers Act)

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The Digital Markets Act (DMA) is a key European Union regulation aimed at curbing monopolistic behavior and ensuring fair competition between digital platforms. The Act represents a major breakthrough in the regulation of large technology companies and affects many aspects of digital marketing, user privacy, and competition.

For marketers, the DMA has major implications as it affects the ways in which companies can use digital tools and platforms for their activities (or the data from them). In what follows, we look at the key aspects of DMA and their implications for marketing strategies.

The purpose of DMA

The DMA was created to counter several specific problems that have arisen as a result of the dominance of large technology giants, known as gatekeepers. These companies control essential digital platforms, which allows them to influence markets, restrict innovation, and prevent fair competition (and thus cannot restrict innovation and prevent new competitors from entering the market because it is threatening to them).

The DMA seeks to regulate these potential negative impacts and practices and establish clear rules of the game.

When was the Digital Markets Act (DMA) created?

The Digital Markets Act (DMA) was formally adopted by the European Parliament and the Council of the EU and came into force on 1 November 2022. The rules came into force on 2 May 2023. This act is a key step in the regulation of large digital platforms and ensures a fairer competitive environment in the EU digital market.

What are gatekeepers?

The DMA defines gatekeepers as large online platforms that have a significant impact on the internal market, act as a key interface for users, and have the ability to lock in businesses and consumers. To be classified as a gateway, a platform must meet specific criteria such as exceeding €6.5 billion in revenue over the last three years or being a dominant platform with more than 45 million monthly active users in the EU and 10,000 annual active business users (all three conditions must be met simultaneously, meeting only one or two conditions is not sufficient for a platform to be classified as a gateway).

Further changes have subsequently come into force, increasing the potential fine from the original €6.5 billion to €8 billion – this change came into force on 2 November 2023 following the adoption of the final DMA by the European Parliament and the Council of the EU.

The Digital Market Act (DMA) applies to all companies that supply digital services in the EU, whether they are based in the EU or not. This includes:

  • Online platforms – these are platforms that connect users with third-party products and services such as e-commerce platforms, social networks, and search engines. Examples of online platforms include Amazon, Facebook, and Google.
  • Large online intermediaries – are online platforms with a turnover of at least €8 billion per year and at least 45 million monthly active users in the EU. Examples of large online intermediaries include Amazon, Apple, Facebook, Google and Microsoft.
  • Product/price comparison sites – are websites that allow users to compare prices and features of products and services. Examples of comparison sites include Google Shopping, but also services such as Skyscanner.
  • Large online advertising providers – these are companies that sell advertising on online platforms. Examples of large online advertising providers include Google (with its Google Ads) Meta (Meta/Facebook Ads), X, Bing, etc.
    Obligations for gatekeepers

The DMA imposes a number of obligations on gatekeepers to ensure that their platforms remain open and fair. These obligations include:

Ensuring interoperability – gateways must enable interoperability of their services with other services, allowing users to move more easily between different platforms.

Prohibiting data misuse – gateways must not misuse data collected from different sources to create unfair advantages. The DMA prohibits certain conduct that could harm competitors or consumers, such as processing data collected from competitors or preventing users from switching platforms or services. It prohibits them from favoring their own services or products. These obligations are designed to ensure that other businesses can compete fairly and innovate in an already very complex competitive environment. In short, Google and other big players must be able to demonstrate that they can actually use the data sent from your websites.

Algorithm transparency – companies must disclose how their algorithms rank and recommend content to ensure the process is fair and transparent. These policies also define how these companies can collect and use data, which also affects how marketing campaigns can be targeted and measured.

Data protection, user privacy, and implications for the entire digital ecosystem

The DMA places great emphasis on data protection and user privacy, an area that is particularly sensitive for marketers. The legal framework that the DMA creates requires marketers to be much more careful about how they collect and use user data. This has major implications for personalization and ad targeting, which has hitherto been seen as key to effective digital marketing.

This will require marketers to experiment more with new forms of digital marketing that respect privacy and comply with regulations, while still remaining effective.

The changes required by the DMA may require marketers to redesign existing practices, tools, and technologies. The new tools should be able to effectively manage user consent, and properly process data according to the new rules while enabling effective marketing campaigns and results. As a result, marketers (or the owners of the companies they work for) will need to consider further investment in technologies that are able to dynamically adapt to changes in this legislation. Adapting to these changes requires an understanding of the new rules, flexibility in approaches, and a willingness to innovate – and to do so quite quickly.

While the DMA only directly affects the European Union, its impact can be global. Large platforms that operate globally can embrace change at a global level to avoid the need to implement different systems for different markets. This means that marketers outside the EU should also be aware of the DMA and prepare for possible changes.

And what will be likely the real impact?

Leaving aside the fact that the environment will be more transparent and the user can have more say and influence over what data is collected about them there will be a whiplash on the big companies that benefit most from this (Google, Facebook, etc.), ultimately it will probably have an impact on the price of the services themselves that small or medium-sized businesses provide. Because all of these adjustments (within the website, in internal/external documents, in campaigns, and ultimately because of the negative impact on campaign results, the need to use consultants/legal advice/consulting firms or if a smaller business manages these activities themselves = still costing them time and therefore money) will need to be reflected in the end prices. So, in short, the consumer/end user will pay for it again.

Because the rules themselves, even though they are being modified and amended over time, are already becoming completely opaque to the average business person and in some cases, from my point of view, are just creating more and more obstacles and making marketing/overhead more expensive for companies. Over time, this will all become so opaque due to the various currencies, amendments, and additions that the average business person will get lost in them and not be able to keep track of them. In addition, the individual rules are already sometimes so contradictory that even the various legislative branches often do not have set uniform procedures, and so everything is dealt with in a wait-and-see style in another exemplary case.

Google Consent Mode 2.0 – what you have to do?

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If you use cookies on your websites and collect data through Google Analytics, Google Ads, or Search Ads 360, you will be affected by the upcoming change. In response to European regulations regarding user privacy and personal data protection, Google is introducing a new Consent Mode 2.0. If you have no idea what this is about and more importantly, what it entails, don’t despair. Today you’ll (hopefully) find out. So let’s get to it.

But first, let’s start with some general chit-chat that the more experienced among you may possibly skip – namely – why there is actually a need to manage user consent via cookies, and when and where this need arose.

Managing consent in digital marketing: The importance of the Digital Marketplace Act in Europe for brands and organizations

Consent management has become a key aspect of digital marketing for brands and organizations, particularly with the advent of the Digital Market Act (DMA) in Europe.

Under the European Union’s General Data Protection Regulation (GDPR), website owners or business/company operators are responsible for how they collect, process, and store (secure) the personal data collected from users. However, a new EU regulation, the Digital Marketers Act (DMA), shifts these responsibilities to large technology companies, which have been tasked by the European Commission with promoting fair competition and protecting user privacy. These organizations are thus subject to the Digital Market Act and must obtain explicit consent to collect and use European citizens’ personal data for operations such as advertising or research.

Failure to comply with the obligations under the Digital Market Act can lead to substantial fines, which can reach up to 10% of a company’s worldwide annual revenue. In the case of repeated infringements, this amount can rise to 20%. This is where Google Consent Mode v2/Consent Mode 2.0 becomes particularly useful to avoid such situations.

At present, this legislation only applies to regions of the European Economic Area (EEA). However, it is essential to keep a close eye on further guidance from other countries on this topic, as they too may eventually adopt similar restrictions.

What is Google’s consent regime?

Google’s consent mode is a mechanism used in Google’s tools – in particular, we are talking about Google Analytics 4 (GA4)/Google Tag Manager/Google Ads, which allows businesses to transmit consent signals from the CMP cookie consent banner to ensure user choice is respected. Essentially, this consent mode allows you to determine whether visitors to your website have consented or opted out of sharing their personal information for advertising, measurement, and personalization purposes.

If a user consents, Google may use this mechanism to obtain detailed analytics and other information about the user from cookies. Conversely, if the user does not consent, Google will restrict the use of cookies and identifiers to suit the user’s preferences.

Note: Google Consent Mode is NOT a stand-alone solution for managing user consent or cookie compliance. It does NOT replace the need for a Consent Management Platform (CMP) or cookie consent banner/widget, which is responsible for obtaining and managing user consent on the website. Instead, Consent Mode acts as an additional feature that works with the CMP to ensure that Google tags and scripts work in accordance with users’ consent preferences.

Google Consent Mode V1 or Google Consent Mode V2: What’s the difference between them?

Google Consent V1

In its beta version (v1), Google Consent Mode collected consent data based on two parameters:

  • ad_storage – this signal relates specifically to marketing targeting cookies and whether or not to store them on the server.
  • analytics_storage – this signal activates or deactivates the storage of cookies related to analytics, statistics, and performance (duration of visits, number of visitors, number of page views, etc.).

These parameters affect the behavior of Google Analytics scripts when loaded on a website. For example, if the ad_storage parameter is set to denied, Google will not store any data about the user’s ad.

The first version of Google Consent was introduced in 2020 to enable data collection for Google Analytics and Google Ads while complying with European data protection laws (GDPR). Google Consent Mode V2 is an updated version to accommodate the new Digital Markets Act, which takes effect in March 2024.

Google Consent V2

Version 2 goes further and controls the use of this data in Google’s advertising products through two new consent signals dedicated to audience building and remarketing:

  • ad_user_data – this signal relates to permission to send and share user data with Google for advertising and remarketing purposes using site-to-site identifiers (impacting campaign effectiveness and Smart Bidding).
  • ad_personalization – this signal relates to permission to personalize ads based on user data (an essential feature for building audience lists!). In layman’s terms, this enables personalized advertising.

If the user does not grant these two new parameters, Google will not be able to create specific audiences and serve personalized ads in the European Economic Area (EEA) region.

If the user refuses consent, Google Ads and GA4 tags will run in an anonymized form (without personal data). It is important to ensure that no personal data of the user is sent to the system. For this purpose, it is necessary to have two very similar tags ready, which are activated depending on the consent given. If consent is refused, a tag is triggered that does not include user data, transaction ID, and other sensitive information.

Google Consent Mode V2 thus offers two basic settings:

  • Basic Consent Mode – all features are activated only after consent has been granted for cookies.
  • Advanced Consent Mode – services work even if consent is denied, allowing for background data collection.

Overview: Consent mode parameters

Consent Type Description
ad_storage Enables storage (such as cookies) related to advertising.
ad_user_data Sets consent for sending user data related to advertising to Google.
ad_personalization Sets consent for personalized advertising.
analytics_storage Enables storage (such as cookies) related to analytics e.g. visit duration.

In addition to the consent mode parameters, there are the following privacy parameters:

Storage Type Description
functionality_storage Enables storage that supports the functionality of the website or app e.g. language settings.
personalization_storage Enables storage related to personalization e.g. video recommendations
security_storage Enables storage related to security such as authentication functionality, fraud prevention, and other user protection.

See more

The functioning of both modes is shown in the image below (or see Google’s official help on the behavior of the new tags under Google Consent V2):

In practice, the basic implementation means that unless you get consent from the visitor to use cookies, analytics, and advertising scripts are completely blocked.

On the other hand – the advanced implementation allows running these scripts in “anonymous” mode, even without cookie consent. This allows Google Analytics, for example, to use indirect data to fill in missing information due to lack of cookie consents, while Google Ads relies on anonymous data using machine learning to better tune conversion models and set up automated bidding strategies more effectively.

Regardless of the method chosen, two new parameters – ad_user_data and ad_personalization – need to be sent via the Consent Mode API to ensure these processes work properly.

As of March 6, 2024, you will need to have Consent Mode V2 implemented in either the “basic” or “advanced” version. There are several important aspects you should consider when doing this:

If you do not implement V2, there will be limitations in data collection for remarketing purposes and conversion attribution in Google Ads.

  • The Basic version – will not collect data in the background without user consent and there will be no data modeling.
  • The Advanced version – will continue to model missing data as before.

Relationship between consent rates and modelled conversions

Users who refuse to consent to cookies are likely to have significantly lower conversion rates compared to those who consent to their use (according to official additional help from Google on Google Ads, their extensive analysis has shown that conversion rates vary depending on consent to use cookies). Users who provide consent are two to five times more likely to convert than those who don’t. This difference is influenced by a number of factors such as the overall consent rate, industry specifics, or the type of conversion goal.

The example above shows how increases/decreases in consent rates do not correspond to increases/decreases in conversion rates because users who do not consent convert less frequently. In this case, the advertiser has a 50% consent rate but only a 19% decrease in conversions (12 out of 62) and an 18% increase in conversion rate from conversion modeling.

 

Consent mode 2.0 – what needs to be done?

As of March 1, 2024, every site will be required to implement an updated version of Consent Mode V2 in their cookie bar.

What does this mean?

This is part of a new EU regulation called the Digital Marketers Act, which places new requirements on large analytics platforms. This regulation introduces new technical specifications for cookie bars, primarily adjustments to the technical parameters for tracking data.

This change will mainly affect platforms from Google, such as Google Ads, DV360, and SA360. Within the consent panel, users will be able to manage their preferences directly in their Google account. Although the changes are described as “minor,” they have a significant impact on the way personalized ads and remarketing are executed.

What about the cookie bar? Do you need to change it?

The cookie bar doesn’t change in appearance. If the cookie banner is discreet, most users will probably ignore it anyway and won’t want to interact with it in any way. You can increase the likelihood of user consent by using clear and friendly text, using psychological elements, and grabbing attention. But there’s nothing more to change from a design perspective (unless you’re using some CMP and not a custom solution that perhaps no longer reflects recent changes to the background of cookie management itself).

When applying the legal changes it is advisable to do the following (this is a very brief summary, the whole issue is much more complex and I cover more below):

  1. Incorporate Google’s new cookies (ad_user_data, ad_personalization) into your cookie inventory.
  2. Add to your cookie documentation, which will now include information about Google and the tools you use.
  3. Mentioning the use of emails and phone numbers for tracking and advertising should be part of your data processing documentation.
  4. Pay attention to updating your consent bar to match the new requirements (this is essentially handled for you by your CMP if you don’t have a custom solution).

What is the difference between the cookie bar and Consent Mode?

A cookie bar is a visual tool on a web page that appears to inform users about the use of cookies and request their consent. It allows users to approve or reject the use of different types of cookies, including those for marketing or analytical purposes. This tool is a direct interface between the website and the user, aimed at ensuring transparency and compliance with legal requirements such as GDPR.

Google Consent Mode is specifically designed to work with Google services and how they process data, while cookie bars are more general tools that can affect a wide range of technologies and third parties used on websites. Google Consent Mode is a tool designed to optimize the way data is collected and used, based on the consent you have already given. Consent Mode therefore relies on the fact that consent has already been obtained (for example, via a cookie bar).

Google Consent Mode is therefore repeatedly not a stand-alone solution for managing user consent or cookie compliance. It does not replace the need for a Consent Management Platform (CMP) or a cookie consent banner/widget, which is responsible for obtaining and managing user consent on a website. Instead, Consent Mode acts as an additional feature that works with the CMP to ensure that Google tags and scripts work in accordance with users’ consent preferences.

On the other hand, Google Consent Mode is a tool that allows websites to customize how Google tools will use cookies and collect data based on user consent preferences. Thus, this mode sends user consent information directly to the Google services that are used on the site, based on the user’s decision made through the cookie bar.

Impact of Consent Mode 2.0 on the European Union and the rest of the world

It is clear that within the EU countries covered by the digital marketing rules, the implementation of Consent Mode V2 will be mandatory. Google should control this obligation based on the IP address of users. Outside the EU, there will be no obligation to implement the cookie bar or Consent Mode V2 at this time, but as I wrote above – that doesn’t mean that some companies won’t implement the same strict rules on cookie management, even if the law doesn’t explicitly forbid them to do so.

Google partner CMP is becoming a necessity

There are already certified consent management platforms (CMPs) from Google, such as the popular CookieBot/CookieFirst/Usercentrics/OneTrust and others. However, there is speculation that in the future, a certified CMP solution will be a necessity to meet regulatory requirements. The current list of CMP-supported partners can be found here.

End of third-party cookie support at the end of 2024

The changes in digital marketing don’t end in March, however. There will be other important and quite fundamental changes over the coming summer and fall:

The use of third-party cookies will be discontinued (ending in the autumn). Autumn 2024 will bring the end of support for third-party cookies, which means that user’s personal data, which is not directly linked to their identity, will now be crucial for conversion measurement and audience tracking. This measure is expected to lead to less effective marketing campaigns and a general reduction in the accuracy of remarketing targeting.

Users’ personal data will become the only identifiers for conversion measurement and audience tracking.

A reduction in the performance of marketing campaigns and generally less accurate remarketing targeting is expected. So, this is probably where those companies that have spent years building their brand and don’t have their entire business built on paid campaigns alone will win. It is recommended to consider strategies for obtaining user consent to register on the site, as ownership of email addresses with permission to use in marketing campaigns will become increasingly important.

Other alternatives to cookies in the future

Google has been experimenting with alternatives to cookies such as FloC, FloX, TurtleDove, and Fenced Frames for about a year. These technologies are based on aggregating user data, monitoring the pages visited, and then grouping the data into specific categories.

It is simplistic to say that unless there is some dramatic change, campaign targeting is likely to get worse as a result of increasingly complex data collection and a more complex legislative framework, ads/campaigns will get more expensive and the constant continuous integration of changing rules will also cost something.

However, there are other methods to strengthen data quality for analytics and marketing purposes in the context of the limited use of cookies and the coming end of third-party cookie support.

How to optimize data quality for digital marketing because of Google Consent Mode 2.0?

The key to successful behavioral modeling is an effective consent mode and sufficient data for AI. Google recognizes users who have not consented to the use of cookies and uses AI to estimate missing information for GA4.

The quality of the modeling increases with the amount of data collected from users who have given their consent. That is, the more data you have (the more consented traffic you get), the better the quality of this modeling.

However, there are alternative approaches that can help improve the quality of data for analytics and marketing platforms, especially in the context of the phasing out of cookies and the upcoming end of support for third-party cookies.

  • Adjusting the cookie bar – optimizing the cookie bar is key to increasing the level of user consent to cookie collection, which has a direct impact on the quality of data collection. The design and user-friendliness (UX) of the cookie bar play a critical role in the user’s decision-making process of whether or not to consent. What to consider when improving it? The cookie bar should be visually appealing and easy to navigate. For example, highlighting the consent button in green, while keeping the other options less prominent, can significantly increase the percentage of users who consent (but here some legal opinions differ on whether this point can be used – the visual design should be intuitive and not mislead users or lead them to unwanted consent). The text on the cookie bar should be clear and concise so that users understand exactly what they are consenting to. It is important that the text appears trustworthy and informs how the data will be used. A properly worded explanation can reassure users that their data will be processed responsibly and transparently. It is crucial to raise users’ awareness of the importance and benefits of providing consent so that data is lost as little as possible and analytical processes can be as accurate as possible. After all, we discussed this part above, that it is possible to personalize the bar and better explain its purpose (if someone had written that by giving consent you allow not to make services more expensive, everyone would probably agree on the spot :-)). Including animations or interactive elements can increase user engagement. For example, an animation that responds to cursor movement or triggers when the page loads can grab the user’s attention and increase the chance of consent. Users should be able to easily change their consent decision, which increases their confidence in the management of their data. The option to withdraw consent should be as accessible as the option to grant consent. Regular A/B testing of different versions of the cookie bar can help identify which design elements and what wording of text most effectively lead to consent. The data from these tests will allow the bar to be further optimized and improved. Implementing these features and continuously improving them can significantly contribute to a higher percentage of users giving consent to cookie collection, which is essential for effective online marketing and analytics. However, we are talking about – the entity in question needs to have the time and space for such forms of testing (for smaller companies/clients, this is basically pure utopia).
  • Behavioral modeling in GA4/Google Analytics – implementing a cookie bar that does not involve actively obtaining user consent can lead to significant data loss – specifically, it can be a 30% to 60% drop in analytics data from those users who have not consented to the use of cookies. By implementing an effective consent regime, we can save some of this data by using advanced behavioral modeling techniques in systems such as Google Analytics. This modeling allows us to simulate the likely behavior of users who have not provided consent and therefore allows us to maintain a degree of accuracy in our analytics reports. In this process, it is important to carefully set the cookie bar parameters to ensure maximum transparency and clarity of choice for the user. This can include clear and concise wording that informs users how and why their data is being used and offers simple options for granting or withholding consent.

  • Facebook Conversion API (CAPI for short) – the Facebook Conversion API (CAPI) is an advanced integration for digital marketing that provides two key benefits that directly impact the effectiveness and accuracy of digital advertising campaigns on Facebook. CAPI allows event data to be transferred directly from the advertiser’s server to Facebook’s server, resulting in higher quality and more reliable data. This minimizes data loss caused by blocking cookies in users’ browsers. As a result, the effectiveness of Facebook campaigns is improved as optimization is based on more accurate and complete information. According to recent studies, Facebook reports that implementing CAPI reduces acquisition costs by 13% and increases purchase event tracking by 19%. With the impending end of third-party cookie support, particularly in Chrome, CAPI offers a sustainable solution to continue profiling and data collection across domains. This is done through server-side measurement, which enables effective ad targeting and detailed reporting of campaign results without the need to rely on traditional cookies. In addition, CAPI provides flexibility in the transmission of different types of events, including those that take place on websites, in mobile apps, in offline interactions, and even in communications via apps like WhatsApp and Messenger. This includes the ability to transmit conversions from e-commerce transactions, in-app interactions, and other important signals relevant to marketing purposes. With CAPI, advertisers have full control over what data is shared with Facebook.
  • Enhanced Conversions/Enhanced Conversions – Enhanced Conversions, also known as Enhanced Conversions, is a key tool for increasing the accuracy of conversion measurement in digital marketing campaigns, especially Google Ads. This technology allows for better matching of conversion actions to real users by comparing hashed data collected from advertisers’ conversion pages with data from logged-in users in Google. A key aspect of successfully implementing Enhanced Conversions is obtaining users’ consent to use their data for this purpose. Users should be clearly informed about what data is being collected and how it will be used, and they must actively consent to the sharing of this information with third parties. This is achieved by effectively deploying a cookie bar that allows users to grant or deny this consent. Enhanced conversions can be implemented in several ways that vary according to technical requirements and advertiser preferences:
    • Using Google Tag (gtag.js), which is a direct integration into the website code.
    • Using Google Tag Manager, which allows tag management without the need to intervene in the page code.
    • The Google Ads API, is suitable for advanced users who want to automate and scale processes through the API.

All of these methods require careful configuration and testing to ensure that data is collected and processed correctly, resulting in the benefits of more effective and targeted ad campaigns. With a better understanding of users’ paths to purchase and their conversion behavior, marketing strategies can be optimized and ROI maximized.

How to evaluate data more effectively with Google Consent Mode 2.0?

Possible avenues that will be used to evaluate campaigns in the future may include:

  • Attribution – evaluation is done through Google Analytics 4 (GA4), which is a simple and affordable solution suitable for routine evaluation of ad campaign effectiveness, such as very simple click campaigns. It’s important to note that while clicks can provide useful data, additional, deeper metrics are needed to comprehensively analyze campaign effectiveness and understand user behavior (as any PPCer who does more than just brand campaigns will probably tell you, just evaluating clicks is certainly not enough). But for smaller companies, it may be enough for a while. It’s important to mention here that there will be a significant loss of data in GA4 – specifically, there may be a 30-60% drop in analytics data from those users who did not consent to the use of cookies, i.e. you’ll be missing a pretty important sample of data about users who were on the site but for some (unknown to you) reason did not consent and so you won’t learn much about them.
  • Incrementality (causality test) – for brand campaigns, you can use Google Casual Impact, which allows for detailed analysis. Optimal results can be achieved through geo-split testing, which involves selecting regions where the campaign will run or not. Choosing the appropriate region can be difficult and challenging to implement, but it’s effective for evaluating campaigns before and after launch. The advantage of incrementality is low cost.
  • Marketing Mix Modeling (MMM) – can be done by an experienced data analyst or with tools like lifesight.io. This is suitable for companies with a wide range of offline and online advertising activities and for tracking long-term trends. External factors such as GDP trends, inflation, interest rates, weather, or seasonality can also be included in the modeling, although the assessment can be complex. This model, on the other hand, is quite unusable for smaller companies as it requires quite a bit of time and money in addition to expert knowledge.

Other useful resources:

AI news OSN and global resolution on AI

AI news: OSN and global resolution on AI

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OSN has just unanimously adopted the first global resolution on artificial intelligence. While this step symbolizes significant progress towards a coordinated global approach to the regulation and development of AI, we must not overlook its non-binding nature and potential shortcomings.

The resolution calls on states to focus on the protection of human rights and personal data while monitoring the risks associated with AI. Although it is supported by more than 120 countries, including China and the US, its actual impact remains uncertain, mainly due to its non-binding nature.

Although the initiative counts on the support of all 193 UN member states and emphasizes unity in the approach to AI governance, there is a significant lack of concrete measures to guarantee its safe development and use. U.S. Ambassador to the U.N. Linda Thomas-Greenfield’s statement that “together we will choose to govern AI rather than have it govern us” sounds encouraging, but requires more than words – we need action.

Concerns about the potential misuse of AI, including disruption of democratic processes, fraud, or massive job losses, are ever present. The resolution offers a framework, but without binding rules or sanctions for those who do not respect the standards, its effectiveness remains in question.

Europe is emerging as a leader in AI regulation, while in the U.S., political polarization is hindering legislative progress. Although the White House is taking steps to reduce the risks associated with AI, a global response to the challenges posed by AI requires more than just US leadership.

This resolution is a step in the right direction, but its success will depend on the ability to transform words into binding actions that ensure AI serves society ethically and safely. It’s time to move the discussion out of UN meeting rooms and into concrete, binding action.

Machine learning – how does it actually work?

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Lately, the term “machine learning” has been on everyone’s lips. But what’s the real scoop on how it works?

In a nutshell, it’s all about teaching computers to recognize patterns in data and make decisions based on them. Instead of manually writing out programs, algorithms are trained on heaps of data so they can “learn” to perform specific tasks on their own.

There’s a whole toolbox of machine-learning techniques out there. Let’s dive into a few of them in this article.

Regression – predicting numerical values

Regression models come into play when we want to predict a continuous numerical value based on input data. For instance, guessing a property’s price from its size, location, and so on. The poster child for regression techniques is linear regression, which fits a line through data points to minimize prediction error.

Classification – sorting into classes

Unlike regression, classification models aim to sort objects into classes or categories. Think of distinguishing between a cat and a dog in a photo or predicting whether a customer will stick with a company or jump ship based on their behavior. Common classification techniques include decision trees, neural networks, and support vector machines.

Clustering – finding similar points

Clustering algorithms seek out points in a large dataset that are similar to each other and group them into clusters or clusters. This technique is handy for things like customer segmentation for targeted marketing or organizing a vast number of documents into thematically similar groups.

Dimensionality reduction – simplifying data

We often encounter data brimming with attributes, which complicates processing and analysis. Dimensionality reduction techniques strive to reduce the number of attributes while retaining as much information as possible. For example, the principal component analysis method can replace a large set of original attributes with a smaller number of new variables.

Ensemble methods – strength in numbers

Instead of relying on a single model, we create multiple models and let them vote. For example, rather than using one decision tree, we might use a bunch of trees, each trained on different data slices. We then combine the results, say, by voting or averaging. This approach often leads to more accurate outcomes.

Neural networks – brain inspiration

Neural networks mimic the brain’s structure, consisting of simple computational nodes connected by links. By adjusting the weights of these links appropriately, the network can be trained to solve complex problems. In recent years, a special type of neural network, known as deep learning, has made significant strides in tasks like image recognition or speech processing.

The backbone of modern machine learning, especially deep learning, is the use of advanced high-tech computers, often referred to as high-performance computing (HPC) systems. These systems are equipped with powerful GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) designed specifically to handle the massive computational demands of training complex neural networks. HPC systems are crucial for processing large datasets, running simulations, and speeding up the training process, which can otherwise take an impractical amount of time.

The cost of these advanced computing systems can be substantial. Typically, funding comes from a mix of sources depending on the setting. In academic and research institutions, funding may come from government grants, research endowments, and partnerships with industry leaders. For private companies, especially those in the tech sector, investment in HPC infrastructure is often viewed as essential for staying competitive, with costs being a part of the company’s research and development (R&D) budget. Additionally, cloud computing platforms now offer access to HPC resources on a pay-as-you-go basis, making advanced computing power accessible to startups and smaller companies without the need for upfront investment in physical hardware.

The rise of cloud-based machine learning platforms has democratized access to advanced computing resources. Companies like Google, Amazon, and Microsoft offer machine learning services that run on their cloud infrastructures, allowing users to pay only for the computing resources they consume. This model has lowered the barrier to entry for innovative machine learning projects, enabling a broader range of companies and researchers to explore complex models without the need for significant capital investment in hardware.

Transfer learning – knowledge transfer

Instead of training a model from scratch, we can leverage knowledge gained from solving a similar problem. For instance, a neural network trained to recognize faces can be “retrained” to identify dogs and cats. Transferring learned features to a new domain often speeds up training and improves results.

With the advent of technologies like GPT-3, machine learning is not just about recognizing patterns or making predictions anymore. It’s also about understanding and generating human-like text, opening up new avenues in natural language processing and beyond. This leap forward has transformed how we interact with technology, making it more intuitive and human-centered.

We’ve seen a few examples of how machine learning works. The key is to find the technique that best matches the type of problem at hand and to train models on quality data. While the principles might seem straightforward, successfully deploying machine learning in practice is quite challenging. Yet, diving into this field is well worth it, as it opens up fascinating possibilities for data analysis and practical applications.