How artificial intelligence is reshaping data analytics in 2025

Picture this: It’s 2025, and you’re launching a new data project. Instead of spending hours manually cleaning datasets, building reports, or sifting through raw numbers to identify trends, AI seamlessly takes over the heavy lifting for you. Not only does it crunch numbers—it also detects inconsistencies, highlights emerging customer patterns, and even suggests the most effective visualisations tailored to your specific challenges. 

As a result, data-driven tools are no longer just software—they have become intelligent collaborators. They swiftly process large amounts of datasets at unparalleled speed, automate tedious tasks, and uncover insights that might otherwise go unnoticed. Whether you’re optimising performance, forecasting trends, or refining predictive models, automation enables analysts to focus on solving complex business problems while simultaneously delivering strategic value. 

 Imagine having a digital assistant that: 

• Cleans up messy data automatically—no more endless hours spent sifting through spreadsheets! These tools can instantly identify errors, fill in missing values, and organise data so it’s structured and ready for analysis. 

• Predicts what you’ll need before you even realise it—by analysing past trends, it provides accurate forecasts across various business functions. 

• Helps you understand what the data means—by highlighting key takeaways. 

• Creates personalised reports and dashboards. 

• Provides clear, actionable recommendations, helping you make smarter business decisions and achieve better outcomes. 

Rapid data processing 

AI-powered tools now cleanse, merge, and validate data in a fraction of the time it once took. Analysts who previously spent hours collecting unstructured data and checking for inconsistencies can now rely on automation for precise, real-time processing. 

Beyond just accelerating data preparation, AI enhances workflows in multiple ways. It assists with library syntax across different platforms, allowing analysts to streamline their processes effortlessly. Tools like Trifacta, DataRobot, and AWS Glue DataBrew make these improvements possible. Consequently, professionals can move beyond repetitive tasks and shift their focus toward higher-value analytical work. 

Luca Fossati, Global Head Coach of Data & Analytics at FDM Group, highlights this shift: 

“AI has always been intimately connected with data & analytics, and the inter-dependencies between them continue to expand. Data engineering is one key component of data & analytics, which is focused on extracting, cleaning, aggregating and preparing data. Many of the applications that use the polished data are predictive analytics applications, which leverage generative AI and other more traditional machine learning models to make predictions.  

A more recent trend has been to incorporate AI in the data pipeline itself, when processing the data. For instance, when cleaning raw data, one may decide to include an AI agent that can spot and correct anomalies. Other agents could be used to filter and transform the data, etc. This is giving rise to so-called agentic workflows.  

Additionally, the prompts involved in those tasks could be enhanced by the use vector databases and retrieval augmented generation (RAG). Furthermore, there are orchestration tools that can manage a variety of LLMs (both open-source and not) to be used within the same pipeline, for different purposes. Finally, the development of the pipeline itself can be greatly sped up, by the use of code generation tools.” 

Predictive analytics 

Traditional predictive analytics has long relied on historical data to forecast future trends. However, AI takes this approach a step further by identifying cause-and-effect relationships. This capability enables analysts to generate more accurate predictions while simultaneously gaining a competitive edge. 

Automation-driven predictive analytics is about more than just forecasting. It actively helps businesses anticipate market changes, optimise operations, and proactively refine their strategies. Companies that leverage these models can spot opportunities before competitors, thereby improving their decision-making and overall performance. 

In a fast-changing business environment, the ability to anticipate trends is invaluable. AI-driven predictive models empower organisations to adapt quickly, ensuring they remain resilient and forward-thinking. 

FDM Consultant Norbert Csecs from our Change and Transformation Practice shares: 

“The majority of the world, including the British government is betting on AI continuing to improve quickly. This dynamic technological environment encourages everyone to rethink their processes and build a capability to harness the power of AI in their organisation. Even more exciting is the opportunity the global tech community has in shaping the usage of neural computing for the common good. It’s a brave new world, an incredibly exciting time to be alive!” 

Code automation & effortless debugging 

Coding is a fundamental skill for data analysts, yet it can be time-consuming. AI-powered tools now streamline coding and debugging, making technical tasks far more efficient. These advancements simplify complex processes such as visualising datasets, generating insights, and building machine-learning models. 

Instead of manually writing and troubleshooting code, AI-driven assistants generate, refine, and debug it instantly. Anaconda Assistant, Jupyter AI, and GitHub Copilot enhance efficiency in programming environments. Jupyter AI enables Python users to create entire code blocks with minimal effort, while Microsoft 365 Copilot supports Excel users by automating formula generation and macros. 

By reducing the time spent on coding and debugging, AI allows analysts to shift their focus. Rather than being tied up in technical challenges, they can concentrate on leveraging data for strategic decision-making. 

Smarter, more insightful reporting 

New AI-driven platforms make uncovering deep business insights easier than ever before. By rapidly analysing datasets, they identify hidden trends and deliver meaningful interpretations. Solutions like Tableau GPT allow users to ask direct questions and receive instant, data-backed responses. 

FDM Senior Delivery Consultant David Harvey shares: 

“In 2025 we will see a further advancement and adoption of self-service Analytics Tools & Reports. These Reports are now simply created by an end-user writing their requirements into a dialogue box for the application to then create a series of Dashboards and Insights which it calculates are aligned to the user’s requirement. These can allow users to create dashboards in minutes instead of days. That said there is still a need for business intelligence (BI) developers to create reports.” 

Synthetic data 

The ability to generate synthetic data is revolutionising analytics. By 2030, Gartner predicts that most machine-learning models will rely on synthetic datasets. This shift is particularly valuable for industries that require large, diverse datasets but face strict privacy regulations. 

Synthetic data offers several advantages. It protects privacy by eliminating risks associated with using real personal data. Additionally, it provides an unlimited supply of training data, making model development more scalable. Moreover, it helps mitigate bias by generating balanced datasets, reducing errors in AI models. 

Many companies are now turning to free tools like ChatGPT to generate synthetic training data. These tools allow data scientists to experiment with and refine models without relying on sensitive datasets. Industries such as healthcare, finance, and cybersecurity are already leveraging synthetic data to drive innovation. 

Data quality 

AI goes beyond generating synthetic data—it also improves overall data quality. By automating data imputation, AI can fill in missing values and detect anomalies in real-time. This reduces errors, ensuring that organisations work with clean, reliable datasets. 

Managing data quality is becoming more critical as businesses collect increasing volumes of information. Automated tools help by integrating data from multiple sources, creating a unified and accurate view. This enhances operational efficiency and enables organisations to make better-informed decisions. 

With high-quality data, businesses can avoid costly mistakes and improve the accuracy of their analytics. AI-driven solutions ensure that every decision is based on trustworthy information, strengthening overall performance. 

Conclusion 

Automation is reshaping data analytics, introducing new levels of efficiency, accuracy, and insight. From streamlining workflows and automating code to enhancing reporting and generating synthetic data, AI-driven tools continue to evolve. 

Professionals who embrace automation gain a significant advantage. They enhance their analytical capabilities, improve efficiency, and stay ahead of industry trends. Businesses that integrate AI-driven solutions position themselves for long-term success. The ability to process, analyse, and act on data faster than ever before gives organisations a decisive edge. 

The time to adopt these technologies is now. Those who lead the transformation will drive real business impact, unlocking new levels of innovation and performance. AI is not just changing data analytics—it’s redefining how businesses operate and compete.  

How FDM can support your data journey 

At FDM Group, we recognise the challenges businesses face in sourcing skilled professionals and the growing need for hands-on, industry-relevant training. Our Data & Analytics Practice is designed to bridge the skills gap by equipping individuals with the knowledge and experience they need to excel. Whether you’re a graduate looking to enter the field, a career returner, or an experienced professional aiming to upskill, our careers programmes provide the essential coachingand support to help you succeed.   

Read more about our other Practices to find the right career path for you.