WHAT IoT AND BIG DATA TRENDS WILL UNDERPIN DIGITAL TRANSFORMATION IN 2024

From machine learning automation to real-time analytics and enhanced data governance, how organizations extract and apply insights from exponentially growing information streams continues advancing rapidly. It makes sense to think about leading trends set to shape big data and analytics in 2024, which we do below.

ML Automates Analytics

Automated machine learning (AutoML) will eliminate much manual tuning of models, greatly expanding access to advanced analytics techniques for non-specialists. With algorithms building algorithms, AutoML promises to boost productivity and find complex data correlations that humans alone cannot easily detect.

IoT Adds Context

Integration of real world inputs via sensors on smart products, appliances and vehicles will layer additional signal onto customer and operational data models. It promises to enhance accuracy of demand forecasting, predictive maintenance and other analytics applications reliant on environmental context.

Streaming Analytics Accelerate

As 5G reduced network latency permits, more organizations will apply complex analytics models to data streams in real-time rather than in batches after lengthy processing delays. That enables more agile business planning, inventory management and personalized customer interactions.

Data Mesh Architecture

Data mesh principles aiming to improve data quality, security and accessibility using domain-oriented decentralization will gain more converts as data networks scale in complexity across departments and partners. It represents a new paradigm beyond monolithic data lake architecture.

Advanced Visualization

Data visualization capabilities will grow more robust allowing faster interpretation of analytics from various perspectives. Increased use of augmented analytics overlays and conversational interfaces via chatbots aims to democratize access to custom insights for employees without coding skills or deep statistical knowledge.

Enhanced Governance

Application of ML algorithms will help discover, catalog, track and protect sensitive structured and unstructured data across systems while also monitoring usage. Progress bolsters data security amidst growing regulatory concerns, though intended bias remains an area of focus around some automated approaches.