How Data Science and AI are Killing Software Engineering Best Practices (And Why We Need to Fight Back!)

Fra Gadaleta
Amethix Technologies
6 min readFeb 21, 2023

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In recent years, the field of data science and artificial intelligence has experienced an explosive growth, with a corresponding surge in the development of data-driven products and services. While these advancements have transformed the landscape of business and technology, there is a growing concern that the fundamentals of software engineering are being overlooked.

Many people see data-driven products as being fundamentally different from traditional software products. While it’s true that they require a different set of skills and tools, data products are still software at their core, and they need to follow the best practices of software engineering in order to be successful.

One of the most concerning trends in this field is the tendency to abandon tried-and-true software engineering practices in favor of newer, more data-centric approaches. Agile development, for example, has become something of a buzzword in the world of data science and AI, but many experts argue that it’s not an ideal methodology for these types of products.

Agile development is often seen as a quick-and-dirty approach to software development that emphasizes speed and flexibility over process and structure. While this may work well for some types of software products, it’s not necessarily the best approach for data products that require careful planning and execution. In fact, some argue that Agile development can be counterproductive for data products, as it can lead to a lack of clarity around project goals, poor communication between team members, and a lack of focus on critical details.

So, what are the best practices of software engineering that we need to be paying more attention to in the world of data science and AI?

Project planning

First and foremost, we need to focus on the importance of proper project planning and requirements gathering. This means taking the time to fully understand the needs of the user, as well as the technical constraints and opportunities of the project. Without this foundation, it’s impossible to build a successful data product that meets the needs of both the user and the organization.

Testing and quality assurance

Another key principle of software engineering that we need to emphasize is the importance of testing and quality assurance. In the world of data science and AI, this means putting in place rigorous testing protocols that ensure the accuracy and reliability of the data being used. This is especially important in fields such as healthcare, where inaccurate data could have serious consequences for patients. Not to mention automotive as more and more control is delegated to automatic systems doomed to become autonomous.

Security and privacy

We also need to pay close attention to issues of security and privacy when it comes to data products. As data becomes an increasingly important asset for organizations, it’s critical that we take steps to protect it from unauthorized access and misuse. This means building secure systems, implementing proper access controls, and adhering to industry best practices around data privacy.

Communication and collaboration

Finally, we need to focus on the importance of communication and collaboration between team members. Data products are often complex projects that require input from multiple stakeholders, including data scientists, software engineers, product managers, and business analysts. By emphasizing the importance of open communication and collaboration, we can ensure that everyone is working together towards the same goals, and that the final product is something that truly meets the needs of the user.

All the principles above, in fact, come from software engineering, way before data science and AI became buzzwords.

As the rush towards artificial intelligence continues to accelerate, many companies are racing to jump on the AI bandwagon in an effort to stay competitive. However, it’s becoming increasingly clear that simply having an AI strategy is not enough. The companies that will survive and thrive in this new landscape are the ones that not only grow organically on a topic that is unstable and experimental by definition, but also apply core software engineering principles to their workflows.

At the heart of this approach is the understanding that AI is not a magic bullet that can be applied to any problem and automatically solve it. Instead, AI is a tool that must be carefully integrated into existing workflows and processes in order to be effective. This requires a deep understanding of software engineering principles such as project planning, requirements gathering, testing, and collaboration.

it’s becoming increasingly clear that simply having an AI strategy is not enough

Companies that apply these principles to their AI workflows are able to avoid many of the pitfalls that plague less disciplined approaches. By carefully planning out their projects, for example, they can ensure that they are solving the right problem and that their AI models are meeting the needs of their users. Similarly, by implementing rigorous testing and quality assurance protocols, they can ensure the accuracy and reliability of their AI models.

In the end, it’s clear that the rush towards artificial intelligence is not a sprint, but a marathon.

In the rapidly evolving world of AI, it’s easy to get caught up in flashy claims of revolutionary products that often fall short of expectations. But the real key to long-term success lies in striking the perfect balance between innovation and disciplined software engineering. Companies that master this art are poised to make the greatest impact and reap the greatest rewards.

In the rapidly evolving world of AI, it’s easy to get caught up in flashy claims of revolutionary products that often fall short of expectations.

By taking an organic approach to growth and staying true to the core principles of software engineering, these companies can create AI products and services that genuinely meet the needs of their users, rather than simply hyping up the latest buzzwords. And with a deep understanding of the underlying technology, they’ll be able to drive their businesses forward with unparalleled insight and effectiveness.

In short, while other companies may be chasing after the latest AI fads, those that focus on solid engineering principles and a user-centered approach are the ones that will truly shine in the long run.

Imagine a construction company that has spent decades building bridges and skyscrapers, perfecting their techniques and becoming masters of their craft. However, as technology advances and the world changes, they find themselves facing a new challenge — building skyscrapers over bridges, or space elevators or underwater cities or any other sci-fi equivalent.

Such a new task is definitely innovative and exciting, but also fraught with risks and uncertainties. The company must apply their traditional construction principles to this new challenge, even though the context has changed. They must carefully consider the unique characteristics of the new project, such as the weight and stress distribution, the impact of wind and weather, and the materials and technologies needed to achieve the desired outcome.

Despite the novelty of the task, the company cannot abandon their traditional construction principles. They must still adhere to the same standards of quality and safety that they have always followed. They must still plan their projects carefully, gather and analyze data, and collaborate closely with other stakeholders to ensure a successful outcome.

In this way, the construction company can leverage their existing expertise and experience to tackle this new challenge, while also adapting to the changing landscape of the industry. By combining the old and the new, they can build even more impressive structures that push the boundaries of what is possible.
Why shall we not apply this same approach to the world of AI?

By focusing on careful planning, rigorous testing, security and privacy, and open communication, we can build successful data products that meet the needs of both the user and the organization.

Are you ready to take your data-driven products to the next level? At Amethix, we’re experts in developing innovative data solutions that adhere to the best practices of software engineering. Visit our website amethix.com to learn more and discover how we can help your business thrive in the age of AI and data science.

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Fra Gadaleta
Amethix Technologies

🏢 Founder of Amethix 🌟 Building software wizardry and 🦀 Rust-powered wonders 🎧 Host of the mind-bending podcast https://datascienceathome.com