AI + Alt Data in Investment Management

We’ve been taking a look at how AI is helping to operationalise the use of alternative data in investment management.

Our research shows the next frontier will be about more than finding new datasets – it will mean integrating them into production, side-by-side with traditional data, without reducing compliance or accuracy.

Today’s state – Machine Learning at the core

Over the past decade, ML has professionalised alternative data:

🟢 NLP cleans and structures vast news, filings, and sentiment feeds.

🟢 Computer vision extracts features from satellite imagery.

🟢 Anomaly detection flags patterns in IoT sensor data.

These techniques have made large-scale alternative data operationally viable – but they remain concentrated in specialist quant teams with heavy engineering pipelines.

The production reality

The real challenge lies in operationalising alternative data.

That means:

➤  Integrating it with traditional datasets.

➤  Mapping entities to master records and aligning timestamps to “as-of” truth.

➤  Embedding compliance restrictions into data pipelines.

➤  Building reproducible feature stores and governance for modelling.

These production steps determine whether an alternative dataset becomes a repeatable, high-value signal or remains an interesting one-off.

The next phase – Language Models + Agentic AI

☑️ Specialised language models enable user interaction with alt-data pipelines – defining collection processes, querying signals, and explaining results without code
☑️ Agentic AI orchestrates multiple agents across end-to-end workflows – sourcing → cleansing → feature engineering → backtesting → delivery

The Benefits:

This combination of AI and robust production workflows lowers barriers to entry and significantly compresses time-to-insight.

It moves alternative data from being a niche resource to a cross-firm capability.

📍 Our Market Map

To put the space in context, we’ve mapped six major categories of alternative data in investment management – covering use cases, data gathering techniques, analytical approaches, and the types of firms most likely to apply them.

See the image for details.