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Data Platforms: The driving force behind AI innovation

By Line Vodby Rasmussen, Principal Consultant and Cedric Wacker, Senior Consultant in Devoteam

When I speak to clients and business leaders at events, I repeatedly encounter distinct themes and objectives, shaped by the rapid advancements in artificial intelligence (AI) over the past few years. These often involve questions like how to use data to drive business innovation, leverage advanced machine learning to predict future growth, implement chatbots, or even create a custom version of ChatGPT. If you’ve found yourself asking any of these questions, this article is for you.

TL;DR: This article explains why you need a data platform as a foundation for your AI projects. 

Setting the scene

Just as skyscrapers need strong foundations to be stable and enduring, data platforms provide the infrastructure, tools, and capabilities to harness the power of data. Offering scalable compute and storage, they form the bedrock for AI projects, enabling us to collect, store, and analyse vast amounts of information efficiently.

Additionally, they provide the necessary compute power to train and run AI models. This synergy between data platforms and AI is transforming industries and pushing the boundaries of what’s possible in technology.

The core of AI: Data quality and data availability

At the heart of any AI system lies data. The successes of OpenAI and other AI leaders were driven not just by algorithmic innovation, but by scaling existing machine learning methods with vast amounts of data and compute power. The quality, diversity, and volume of data directly impact the performance of AI models – essentially, the quality of AI output is a function of the quality of data inputs. 

Modern data platforms enable us to efficiently collect, store, transform, and manage large quantities of data, handling various types of data, including structured, semi-structured, and unstructured data, making them versatile and comprehensive.

These platforms bring advanced data capabilities to both enterprises and SMEs, allowing continuous integration of datasets, which is crucial for training robust AI models that generalise well and perform accurately in real-world scenarios.

Additionally, modern data platforms support real-time data ingestion and processing, essential for deploying AI models that require immediate insights and actions. Whether it’s real-time fraud detection in financial services, dynamic pricing in e-commerce, or predictive maintenance in manufacturing, the ability to process and analyse data in real-time is a game-changer. 

Data platforms provide the necessary infrastructure to support these real-time AI applications, ensuring timely and accurate decision-making.

Enhanced data processing and management

Building on this foundation, data platforms provide sophisticated tools for data processing and management, essential for preparing data for AI applications. They offer capabilities such as data cleansing, transformation, and enrichment, ensuring that the data fed into AI models is of high quality. 

Advanced features like real-time data processing and automation further enhance the efficiency of data workflows. By streamlining these processes, data platforms reduce the time and effort required to prepare data, enabling both enterprises and SMEs to focus more on building and refining AI models. 

This efficient handling and preparation of data are critical to developing AI systems that deliver accurate and reliable results.

Scalability and flexibility

One of the standout features of modern data platforms is their scalability. As our data and AI projects grow in scope and complexity, the need for scalable data infrastructure becomes paramount. Data platforms can scale both horizontally and vertically, accommodating increasing data volumes and processing demands. 

Horizontal scaling involves adding more machines to handle data and processing tasks, while vertical scaling enhances the capacity of existing machines. This scalability is crucial considering the vast amounts of data that need to be integrated, transformed, and managed, as highlighted in the previous paragraph. 

Moreover, given that machine learning and AI are compute-intensive, scalable data platforms ensure that our AI systems can handle large-scale data inputs and continue to perform optimally as we expand our AI initiatives. This flexibility and robustness are essential for developing and maintaining efficient, high-performing AI solutions.

Integration with advanced analytics and machine learning tools

Data platforms are not just repositories for storing data; they are integrated ecosystems that support advanced analytics and machine learning (ML). Many data platforms come with built-in ML libraries and frameworks, enabling seamless integration between data storage, processing, and model training. 

This integration simplifies the workflow for data scientists, allowing them to train, validate, and deploy AI models within the same environment. Having worked as a data scientist in less mature times, I can attest to the immense value of having all these capabilities in one place without needing to move data across various systems.

Additionally, data platforms often provide support for popular AI and ML tools, such as feature stores, experiment tracking, and model registries. This fosters a collaborative and flexible development environment, adhering to MLOps best practices. 

By streamlining the end-to-end process of model development and deployment, these platforms enhance efficiency and productivity, enabling data scientists and engineers to focus on innovation and improving model performance.

Fostering innovation and growth

The combination of robust data platforms and AI drives innovation across industries. By unlocking the potential of data, we can uncover new business opportunities, optimise operations, and enhance customer experiences. 

Data platforms empower us to experiment with AI, iterate quickly, and launch innovative solutions faster. This agility and innovation are critical for staying competitive in an increasingly data-driven world.

Summing up

Data platforms are the unsung heroes powering the AI revolution. Their ability to manage, process, and scale data effectively, along with providing the necessary compute, tools, and frameworks, lays the groundwork for successful AI applications. 

As AI continues to evolve, the importance of robust data platforms will only grow, making them indispensable for any organisation looking to harness the full potential of AI. Embracing advanced data platforms is not just a technological upgrade; it is a strategic move that positions us at the forefront of innovation and growth in the AI era.

Ready to embark on your own AI journey with a modern data platform? If you have any comments or questions please reach out to us by using the contact form below.