How LaaS (Lending as a Service) Is Revolutionizing Fintech

When you are going to create a new web solution from scratch or optimize an existing one, one of the key indicators of top high quality will most likely be a high response rate (to user actions) and SEO-friendliness. 

Unfortunately, client-side rendering, which is done by default in many modern web frameworks and libraries, can become an antagonist for developers pursuing these two goals. In this case, it makes sense to consider the possibility of implementing server-side rendering. Below, we will explain to you what it is, what its features are, with the help of which software tools it can be implemented, and also for which projects it is best suited. 

Definition of Server-Side Rendering

Generally speaking, server-side rendering (SSR), as is probably clear from the name, occurs on the backend side. First, the browser sends a request from the client side to the server, after which the SSR server returns an HTML page with all the necessary meta tags, styles, markup, and other attributes. Then, in the browser, the rendering itself happens, the results of which immediately become visible to the end user.

Why is all this necessary if you can use the default option, client-side rendering (CSR)? – you may ask. In fact, everything is simple: search engine crawlers do not recognize the SEO text contained on the pages (or the page, if it is the only one) of the CSR solution. Thus, if the CEO occupies a significant part of the promotion strategy of your project, you can achieve better results only by implementing SSR. Let us add that projects with sophisticated business logic may “suffer” from CSR in the context of performance since the increased load in the form of several synchronous requests will lead to delays in the interface’s response to user actions. And this is exactly the case where server-side rendering can also become a win-win solution.

Currently, SSR technology is actively used in such world-famous solutions as Airbnb, Upwork, YouTube, Netflix, Uber, etc.

What Are the Benefits of Server-Side Rendering?

Now, based on the above, let's look at the key benefits of SSR.

• SEO and social media friendliness. The server side render approach ensures improved SEO ranking through the correct indexing of pages – now, search robots can recognize SEO text and other attributes important for good ranking. First of all, this is due to the fact that now, search robots do not need to read SSR JavaScript code. As for friendliness for social platforms, it is explained by the ability to display colorful previews when sharing SSR-rendered pages – all due to the correct recognition of meta tags. 
• Better app/website performance. SSR rendering provides a faster initial page load as the JS to HTML conversion is done on the backend. Thus, users see refreshed content faster than with CSR, in particular when it comes to dynamically updated pages. In the long term, this can ensure a reduced bounce rate for websites and web applications. 
• Lower load on the user's device and better user experience (UX). Due to the fact that user requests are now processed on the server side, the user device will experience minimal load. All that remains for it is to interpret the HTML code returned by the server.

What Are the Risks of Server-Side Rendering?

To ensure the objectivity of our review, let's also analyze the disadvantages of server-side rendering.

• Higher TTFB. TTFB or time to the first byte is one of the highest priority indicators of good (or, vice versa, insufficient) performance of web pages. This parameter indicates the time it takes for the browser to receive the first byte of page data processed on the server side. Typically, compared to CSR, the TTFB value is higher because instead of returning a file with links to JS, the server spends some time converting JS to HTML code.
• Limits on the number of requests simultaneously processed on the server side. Due to the increased load on the server, the number of requests processed synchronously will be less than in the case of client-side rendering. Thus, the server throughput will be reduced.
• Need to wait for all the HTML code to load. While the page is loading HTML code under SSR, the user will not be able to perform any new actions on it.

If we add to the above disadvantages a fairly high entry threshold into SSR, as well as an increase in the budget of such projects (due to frequent requests to the server), it becomes clear that this approach is not suitable for everyone.

Server-Side Rendering Frameworks and Tools

As for the server side rendering frameworks and libraries that can be used to process client-side requests on the backend, these include React, Next.js, Nuxt.js, Angular (v7 and newer), and Svelte/Sapper. They use one of the most universal server-side templating languages – JavaScript.

Below we propose to consider server-side rendering React concepts only since React is one of the main tools that we use in web development.

Server-Side Rendering vs Client-Side Rendering

Server-side rendering is not a one-size-fits-all solution since there are situations where its alternative, client-side rendering, is the best choice. In particular, if the content on web pages is updated dynamically – that is, it requires the rendering of some components only (i.e., those with whom the user interacted) while the whole page doesn’t need to be updated, CSR is better suited since part of the content with which the user did not interact will be already loaded.

However, considering that during initial initialization, the content is not displayed until the page is fully loaded into the browser (this can take 2 or 3 seconds, which is critical for a modern consumer of Internet content), when implementing CSR, the site may have poor SEO regardless from the professionalism of SEO specialists (note that this is not a default situation, because with the right approach, lightweight projects with CSR are still ranked well). And this is where the SSR React approach can come to the rescue as usually, React server side components are well-recognized by search crawlers. Thus, by resorting to it, you will be able to ensure enhanced content visibility for search engines.

Server-Side Rendering: SEO vs. Performance

As you can already understand, server-side rendering is capable of providing the best SEO performance for solutions that really need it. In particular, thanks to this approach, search engines will not need to interpret JavaScript. At the same time, if you decide to render in React applying CSR by default, for the React server side rendering implementation, you will have to use additional tools to indicate metadata (for example, React Helmet).

As for performance, in the case of high-load projects, with the server side React rendering, this indicator will be better than with CSR since the SSR website or application will not be limited by the resources of the user device and browser. Also, the user device itself will be less loaded since its only task in the context of updating content will be its output (without rendering).

Conclusion

To sum up, we would like to emphasize that with the correct use of JavaScript frameworks for SSR, you can solve the problem of poor ranking of single-page applications (SPAs) as well as content-heavy websites where SEO and bootstrap performance are critical. On the other hand, CSR is suitable for software with dynamically updated content, that is, content that should change without completely refreshing the page.

However, you should not limit yourself to just these two rendering approaches. In particular, there are also hybrid rendering approaches that combine the best characteristics of SSR and CSR. For example, you can consider the static site generation (SSG) vs SSR couple – perhaps the first option will be the best choice for your project.

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In the world of business and analytics, data plays a crucial role in making important decisions. Visualization tools such as charts, graphs, and dashboards are used to understand data better and make informed decisions. 

What types of dashboards and charts exist

Charts and dashboards are graphical representations of data that can be used to show trends, compare data, or show relative proportions. Some of the most common types of charts include:

• Line charts - are used to show changes in the values of a variable over time. They are often used to show trends, such as changes in sales over time.

• Bar charts - are used to compare the values of a variable between several categories. For example, you can use bar charts to compare sales of different products in your company.

• Pie charts - used to show relative shares. For example, a pie chart might show what percentage of sales were made in a particular region.

Dashboards are sets of graphs, charts, and tables that show key performance indicators of a company. They allow you to visualize data and provide a broader view than individual charts. It can be used to monitor performance, track goals, and plan business strategies.

Many dashboards include financial, sales, marketing, operations, and others. Each type is designed to display a company's specific performance metrics.

For example, a financial dashboard might contain information about financial metrics such as profits, losses, budgets, and cash flows. A sales dashboard might show the number of sales, average checks, conversion rates, etc. A marketing dashboard can include information about traffic, conversions, cost of customer engagement, etc. An operational dashboard can display data on production capacity, resource efficiency, latency levels, etc.

Regardless of the type of chart or dashboard used, the key is to present the data intelligently. Data visualization should be clear and easy to interpret to facilitate decision-making. Therefore, when creating charts and dashboards, you need to consider the audience, purpose, and context in which these tools will be used.

The functions they perform

On the technical side, charts and dashboards are tools for visualizing data and presenting information in a clear and user-friendly format.

Charts are usually created using specialized libraries and software tools that allow you to generate graphical elements such as charts, graphs, pie charts, etc. Chart data can come from various sources, including databases, Excel files, APIs, etc.

On the other hand, dashboards are usually created using specialized software tools that allow you to combine data from various sources and display it in a user-friendly format. Dashboards can include multiple widgets such as graphs, tables, text boxes, etc., which can be customized and linked to quickly display data.

Regarding function, charts and dashboards can perform different tasks depending on business needs. They can be used to monitor performance, track sales, manage resources, predict trends, etc. In addition, they can be configured to automatically alert users of potential problems and delays in processes, allowing you to react quickly and correct the situation.

What problems do charts and dashboards solve

Charts and dashboards are powerful tools that help businesses solve many problems. Here are some of them:

‍Resource Management: Dashboards and charts can help businesses manage their resources, such as money, time, people, etc. That allows a company to use its resources more efficiently and increase productivity.

Planning and Strategy: They can help businesses develop plans and strategies based on data analysis. Also, their use can help identify trends, predict future events, and make fact-based decisions.

Identifying potential problems: They can help businesses identify potential problems and risks. That allows the company to take steps to prevent them or reduce their possible impact.

Improve communication: Dashboards and charts can help improve communication within a business by allowing employees to share information quickly and efficiently. They can also help enterprises to understand customer needs and expectations better.

Workflow automation with advanced charts and dashboards

In today's dynamic business environment, workflow automation is becoming increasingly necessary for businesses of all sizes. By automating repetitive tasks and streamlining business processes, companies can save time, reduce costs and increase efficiency. It's also a convenient way to display and process collected data.

The first step in automating a workflow is identifying which one can be automated. The workflow should be well-defined, with clear inputs and outputs. Defining the metrics that need to be tracked is also important. Metrics such as time-to-completion, task completion rate, and error rate can help businesses measure the effectiveness of the workflow.

The next step is to choose the right tools for automating the workflow. There are several advanced charts and dashboard applications available in the market, each with its own features and capabilities. Businesses should choose a tool that meets their specific needs and is easy to use. Some popular advanced chart and dashboard applications include Tableau, Power BI, and Google Data Studio.

When it comes to developing custom charts for a finished product or business idea from scratch, it's important to understand what requirements they must meet. Selecting appropriate chart types and configuring data visualizations are important to ensure that the insights generated by the dashboards are easy to understand and act upon. For example:

• Ease of use
• Compatibility with existing systems
• Cost
• Customization options
• Support and training
• Which metrics are most important?
• Which chart types will best represent the data?
• What colors and visualizations will be used?
• What level of detail is required?

The next step is to connect data sources to the advanced charts and dashboards. This stage can involve integrating existing systems or importing data from spreadsheets or databases. Once the data sources are connected, the application can start to generate real-time insights into the workflow. It is important to ensure that the data is accurate and up-to-date.

The dashboards should be monitored in real-time to ensure the workflow runs smoothly. The metrics generated by the dashboards can help businesses make informed decisions and take corrective action if necessary. Real-time monitoring can also help identify issues or bottlenecks in the workflow, allowing them to make changes quickly.

To ensure the success of workflow automation with advanced charts and dashboard applications, businesses should follow some best practices. These include regularly reviewing the workflow and its metrics to identify areas for improvement, ensuring that the dashboards are easy to understand and act upon, and involving stakeholders in the automation process.

Summary

Automating workflows with advanced charts and dashboard applications can help businesses save time, reduce costs, and improve efficiency. By following the steps outlined in this guide and adopting best practices, businesses can make the most of workflow automation and gain valuable insights into their data. By automating repetitive tasks and streamlining business processes, they can focus on critical tasks, improve productivity, and stay ahead of the competition.

If you have ever used ChatGPT to find answers to your text queries or DALL-E to generate images, you are already familiar with generative AI. In a nutshell, this type of artificial intelligence is capable of creating unique text, images, and other media as a response to a user query, using training datasets. Despite the advancement of such solutions, they are not without their drawbacks. In particular, one of the key problems with using such solutions is, unsurprisingly, the privacy of user data, which we will actually discuss below.

What Are Some Challenges of Generative AI

So, what are the challenges of generative AI? Let's find out right now.

• Ethics of generative AI models. The ethics of models used by generative artificial intelligence means, first of all, the absence of bias in their responses, as well as ensuring their objectivity and transparency. In practice, providing the first two properties can be difficult – mainly because a service provider developing an AI model usually has limited data samples. As for transparency, it also indirectly depends on the size of the data samples – the more limited and narrowly specialized they are, the greater the risk that the generated solution to the user problem will not consider generally accepted provisions and axioms.
• Using someone else's intellectual property. Generative AI always uses some sample of data for training. This data, in turn, may be protected by copyright – therefore, there may be a difficulty in transferring ownership of the generated content. Actually, this highlights a larger issue that questions the integrity of the use of AI, especially for commercial purposes. If we add to this the blurring of the geographical boundaries of laws related to the generative AI applications, it becomes clear that the solution to this problem must be taken at the international level.
• Potential harm from generated responses. Some organizations in the healthcare, financial, legal, and other sectors, using generative AI, expose their consumers to certain risks – the fact is that the answers it produces may contain potentially dangerous recommendations. Moreover, due to the possible differences in the context of user queries, identifying those answers that can cause harm to users can be quite difficult. That is why, along with the careful development of generative AI models, it is also important for these organizations to pay attention to the implementation of other mechanisms that would set delimitations for the generated answers.
• Compliance with generally accepted user data privacy policies. Finally, if you plan to gradually expand the geographic reach of your AI solution, you will need to ensure that it meets the user data security and privacy standards in the region where the specific user is located. Specifically, these may include the EU General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act (EU AI Act), the California Consumer Privacy Act (CCPA), and so on. But that’s not all: currently, due to the popularization of AI in various fields, a lot of new legislative provisions are being developed, such as the UK’s AI and Data Protection Risk Toolkit, the NIST AI Risk Management Framework, China’s Generative AI Measures, India’s Ethical Guidelines for AI in Healthcare and Biomedical Research, and so on. That’s why long-term fixing user privacy issues in generative AI solutions may be quite challenging.

Solutions for Data Security in Generative AI

In this section, we propose to understand how you can overcome the above-described obstacles in AI data security or, at least, mitigate the consequences of encountering them.

• Creating ethical AI solutions. Data for training models is collected from people who may introduce their own biases. Moreover, AI itself can create biases according to its own algorithms for interpreting the data used for training. Thus, to ensure the transparency of the answers produced by a solution based on generative AI, it is important to additionally implement algorithms for comparing this data with third-party reliable sources of information. 
• Introduction of comprehensive data security AI mechanisms. Any data considered to be someone else's intellectual property and used by generative AI for learning may cause harm or loss to its owners who provide it. To prevent this, you should take care in advance of non-disclosure of your users' information and, as an option, provide them with clear and understandable notifications about for what purpose, by whom, and how their data may be used by your software in the future.
• Eliminating harm from generated responses. To ensure that the responses made by your AI-powered solution are safe for end users, you need to pre-define the rules and parameters that the AI ​​must follow when generating them, as well as implement checking and verification mechanisms to make the generated content safe. And, of course, do not forget to introduce an AI and data security system for monitoring your solution’s operation, which would allow you to fix situations with potentially harmful responses – this will give you a direction for further optimizing your training model. 
• Compliance with AI regulations. The first thing to start with is to ensure reliable user data access control and compliance with the most strict encryption and privacy standards. However, due to the constant discovery of new vulnerabilities even in the most reliable generative AI security mechanisms, you will need to conduct regular checks and audits of the operation of your AI driver solution. Finally, you will need to train the team working on your project on generally accepted rules for using AI and data for its training.

Conclusion

Now, being aware of the most common generative AI security risks, you can start working on your project, keeping it with all necessary policies and standards in time. Also, if you are looking for a service provider to delegate secure AI development without unnecessary doubts, feel free to contact us.