How AI is Shaping the Future of Data Platforms & Infrastructure in 2024
AI is here. There’s no denying that.
AI-related startups secured $12.2 billion in funding from VC firms in 1166 deals. These are just the numbers for Q1 2024. It’s evident that all facets of Industries and consumers are moving towards AI backed solutions. Be it healthcare, logistics, Supply Chain or food tech, every industry is embracing AI and for all the right reasons. Data marketplaces are no exception to this. Interestingly enough, these developments are helping data stewards, CXOs and companies alike.
If you’re someone working in data-governance field or anyway related to Data management, this article is for you. We’ve tried to understand how AI is changing the data landscape and how you can take advantage of the same.
AI & Data Management Practices
Gone are the days when Python scripts were at the centre of Automating tasks. All the major data management tasks from Data Quality to Metadata organisation and integration across systems is possible because of AI services. A remarkable 75% of enterprises now utilise AI to operationalise these processes, increasing their throughput in return.
Furthermore, AI has been a great help as a data interpreter. Finding key insights in unorganised data has moved a lot from just using Excel sheets and human analysts. With the global AI market projected to reach a massive $305.9 billion valuation by year's end, these technologies are transforming industries through their ability to extract profound understanding.
Data Infrastructures in the Age of AI
Traditional data infrastructure like data warehouses, data lakes, and ETL pipelines were built primarily for humans to access, combine, and analyze structured data. But the rise of AI is ushering in new data infrastructure requirements such as handling larger data volumes. Training modern AI models, especially deep learning models, requires vast amounts of data - often billions or trillions of data points. Traditional data stores can struggle with ingesting, storing, and providing access to data at this unprecedented scale. New distributed storage and processing technologies are emerging to meet these big data demands.
As a result, Cloud Platforms lik AWS, GCP and Azure will see a shift towards fine-tuning the model towards this AI data revolution. Offerings like data lakehouses provide cost-effective storage of all data types combined with advanced data management and analytics. Cloud data platforms tap into virtually unlimited compute resources to power intensive AI workloads like model training, tuning and deployment.
AI in terms of Data infrastructure platforms will bring in second order effects. Historically, data was siloed across disparate warehouses, lakes, streams and pipelines for different use cases. AI platforms are enabling a unified data operations approach that converges all data workloads onto a common, integrated platform environment. This allows AI, analytics, data science and data engineering teams to collaborate on a standardized data foundation.
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Role of AI enabled Data Pipelines:
None of this is possible without a robust data pipeline.? A key value of AI platforms is their ability to accelerate data-to-insights by automating data pipelines for data integration, transformation, feature engineering and model deployment into production applications. With automated data operations driven by AI and machine learning, the entire data lifecycle can be streamlined and continuously optimized.
Retrieval-Augmented Generation (RAG) boosting the efficiency
A key development in AI-driven data infrastructure is retrieval-augmented generation (RAG). RAG combines retrieval-based and generative models to improve the quality and relevance of AI-generated insights. It pulls specific, relevant information from large datasets and then generates clear, contextually accurate responses. This boosts the efficiency and accuracy of data processing.
RAG can fetch the most pertinent data from extensive repositories and turn it into actionable insights. This is especially useful for data management tasks, where accurate and relevant information is crucial. By extracting meaningful patterns from unstructured data or improving data quality processes, RAG helps data teams achieve higher precision and relevance in their analyses.
Responsible AI Enablement - Conclusion
In essence, AI platforms are becoming advanced data platforms – completely reshaping data requirements and management paradigms. To unlock the full potential of AI, organizations will need to modernize their data infrastructure and operations around AI's unique and exponentially growing demands. Emerging AI-centric data platforms and services are revolutionizing how enterprises build their data foundations to successfully become AI-driven.
The need of the hour is to incorporate robust & responsible AI capabilities to enable trustworthy, ethical and unbiased AI deployments.
The most senior people at Incept get together and discuss the best and leading practices to make Data Governance successful. Then the Blog folks write the article and share it with you.