For instance, State Street now generates a continuous stream of inflation data for its clients, merging traditional indicators with alternative data sources like observed consumer spending and digital news. “If we look at central banks and interest rate projections, you can use this information set to address gaps when the central bank isn’t speaking,” says Clark. “What is the rhetoric around a central bank? Our research shows that this can have implications for forecasting yields. More interestingly, you’re getting breadth in perspective and incremental alpha outside of periods where conventional data sources are available.”
Notably, Bloomberg offers alternative data solutions to its clients via Bloomberg Terminal and data feeds, and these include consumer transaction data analytics from Bloomberg Second Measure and foot traffic data analytics from Placer.ai, as well as Similarweb’s web traffic data.
How data drives discretionary versus systematic processes
Discretionary managers now analyze a broader universe of securities due to scalable data infrastructure, while quant teams increasingly translate unstructured data into structured signals for models. These trends support the convergence of systematic and discretionary styles, driven by AI enhanced research workflows and improved data engineering practices.
“From a discretionary point of view, we’re seeing discretionary managers able to look over a much broader breadth of names because they’ve got the scalability to gain insight from that data. They’re able to pick out things they never could before,” says Tushara Fernando, Head of Data and Machine Learning at the Man Group. “From a systematic point of view, we’re able to translate and quantize unstructured data into more structured data that we can use in our quant models.”
Indeed, the proliferation of data and AI tools has pushed discretionary and systematic approaches towards convergence, says Systematica’s Dooms. “Discretionary managers get a lot of benefit from GenAI tools in terms of adding code to their process, making it more systematic. Systematic investors get to use data traditionally in the human realm – unstructured data – and parse it into signals,” he explains, adding, “Under the hood, there’s a lot of work to get that process right: how do you shape and architect the data to make it consumable by AI workflows?”
The rise of agentic workflows
Zooming in on agentic AI, experts point to the technology’s early progress in enabling practical, tool-driven workflows. Says Dooms, “To me, agentic AI is not just about chain-of-thought and automation – that’s table stakes. Your basic ChatGPT-style chatbot does planning and thinking. It’s really about tool-calling: architecting processes where you can identify things you were not able to do before and can now do thanks to scalability and then presenting data so it can be used by an LLM.”
State Street’s Clark cites his own organization’s internal analytics capability as a prime example of agentic AI’s potential. “We’ve got different data sources – our own research, structured and unstructured data – and we’ve got agents querying tools to trigger further actions: generating investment insights for clients or internal stakeholders, triggering signals for capital markets settings, etc.,” he explains, “We’re not at the end state where that’s fully implementable, but we’re well into that pathway.”
Why democratizing data access is essential for scalable investment processes
Implementation of cutting-edge data and AI tools still requires human input. Indeed, making the same data available to everyone from entry-level employees to the C-Suite is one key to success. “It’s incredibly important for us to provide an infrastructure for traders, junior traders, desk-side analysts to have access to all the data we have in a seamless fashion, and to provide them with low-code or no-code solutions so they can play with their own data and derive insight,” says the Man Group’s Tushar.
“We want to give them a platform to do their own testing and back testing, analyze their flows and profitability, and tell us how to be more proactive, having done some of the work themselves. That’s key to scaling up our contribution,” he adds.
State Street’s Clark agrees with this statement, observing, “I think the big innovation is that data is now for everyone. The notion that you’re a decision maker but someone else handles data and insight is dead. Building data literacy is the big innovation.”
Interested in more insights from Bloomberg Enterprise Tech & Data Summit 2025 in London, click here. Learn more about Bloomberg Enterprise Tech & Data solutions here.
Insights in this article are based on panels and fireside discussions at the Enterprise Tech & Data Summit held in London in November 2025.
