How Python turns data into business value across organisations

As the global economy continually adapts to the impact caused by the pandemic, supply chain disruptions, and an impending recession, the value of data is rising. Gathered from thousands of sources ranging from customer interactions to social media and everything in between, data can transform decision-making and empower businesses to weather market disruption. For organisations looking to make the most effective use of their data to optimise operations, it's crucial for analysts, data scientists, engineers, and developers to work together effectively. But, in many organisations, the demand for business insights can be so relentless that they have outgrown the capacity of in-house data teams.

For business leaders, Python should be seen as the 'equaliser'. Python is the most popular language for data science, used by 15.7 million developers worldwide. When adopted by businesses, it can be the key differentiator for advanced data analytics, offering data practitioners an open source framework that enables them to reach the cutting edge of data insights.

The growing popularity of Python

Python can be seen across many aspects of our lives, from the basis of the Netflix algorithm to the software that controls self-driving cars. As a general-purpose language, Python is designed to be used in a range of applications, including data science, software and web development, and automation. It's this versatility, along with its beginner-friendliness, that makes it accessible to everyone, allowing teams of machine learning (ML) and data engineers, and data scientists to collaborate with ease.

Python has a rich ecosystem of open source libraries that are often targeted for cyber attacks. That is the reason why it is important to proactively address how users access and interact with open source tooling in an organisation. Python is developed under an open source license, making it freely usable and distributable. There is a vast community of developers contributing to Python projects, making it easier for organisations to collaborate and achieve their goals. With its rich ecosystem of open source packages, businesses can leverage Python to accelerate projects without having to deal with the complexity of deploying third-party applications. This is why Python has become so popular in the data science field.

Bringing value to data science and ML

According to the latest Python Developers Survey, data analysis is now the single most popular usage for Python, cited by 51% of developers, with ML also among the top uses of the language, cited by 38%. Python provides data scientists with over 70,000 libraries that can be used in any given task. These libraries contain bundles of code, which can be used repeatedly in different programmes, making Python programming simpler and more convenient as data scientists will rarely have to start from scratch.

For businesses hoping to get to grips with ML for the first time, Python is a clear winner. It offers concise code, allowing developers to write reliable ML solutions faster. This means developers can place all of their efforts into solving an ML problem rather than focusing on the technical nuances of the language. It also has extensive libraries for ML purposes, meaning developers can turn to pre-existing code to solve tasks and reduce development time. It's platform-independent, allowing it to run on almost every operating system, which makes it perfect for organisations that don't want to be locked into a proprietary system. As a result, Python improves how cross-functional teams of data scientists, data engineers, and application developers can collaborate in taking ML models from experiments into production - which is one of the key challenges facing ML practitioners.

Python in practice

Across industries, Python is making a fundamental difference in how businesses operate, saving time, money, and better utilising their employees' skills. For example, in healthcare, the principal application of Python is based on ML and natural language processing (NLP) algorithms. Such applications include image diagnostics, NLP of medical documents, and the prediction of diseases using human genetics.

These applications are essential to the likes of the healthcare sector, as they process and analyse the data into understandable, meaningful, and reliable information that can allow conditions to be treated before they become dangerous. The industry widely recognises the importance of Python, having set up the NHS Python Community. Led by enthusiasts and advocates of practice, the community champions the use of the Python programming language and open code in the NHS and healthcare sector.

Elsewhere, in the utility sector, Python is being adopted to open up new applications to help customers save money and energy. Take EDF as an example - the energy giant moved away from legacy systems in order to have a more unified view of its data. A crucial aspect of this involved utilising Python to enable data scientists to bring ML models into production. By taking an integrated approach, the company is able to better understand the requirements of its customers and develop new products via ML techniques. As a result, EDF can better support financially vulnerable customers, setting up strategies if they start to face difficulties and predicting them before they happen.

For most scenarios, whether it's analytics, ML or app development, Python is not the only language being used. Rather it's often paired with SQL, Java and other languages used by different teams. Integrating Python into data platforms provides organisations with a unique way to create their own applications to derive business value from their data across teams and programming language boundaries. Doing so in a streamlined single cloud service removes much of the expense and complexity traditionally associated with building and managing data-intensive applications, catering to different programming language preferences from different teams. Using a cloud data platform — along with the languages that developers are already comfortable with — offers a simpler, faster way to derive business insights from data.

The future lies in Python

The pressure on business leaders to rapidly turn their ever-expanding store of data into insights and business value is only going to increase in the coming years. Analysts, data scientists, data engineers, and developers can expand their levels of collaboration with the flexibility of working on the same data and develop interactive applications that can turn insights into actions — all with Python. Organisations cannot afford to ignore Python's unique mixture of flexibility, performance, and speed in order to operationalise the power of ML insights and better meet customer needs.