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Why Agentic AI Is Shifting Attention Back to the Data Layer
The rapid improvement of large language models is changing the way financial institutions approach agentic AI. Model capability remains important, alongside the practical challenges of connecting AI to the right data, providing sufficient context, embedding agents into investment workflows and controlling what they can access and do. These issues featured prominently during a panel discussion…
Can Alternative Data Close the Credit Risk Visibility Gap?
Credit risk has traditionally been monitored through a familiar collection of fundamentals, ratings and market prices. But as private and public credit markets become more interconnected, investors are looking across a much wider range of data for signs that a borrower – or an entire sector – may be running into trouble. That search for…
Dakota Q&A: From Fund Raiser to Private Market Data Specialist
Two decades since its formation, Dakota has transformed its business from raising capital to providing private market participants with data and tech services. It’s history is one of nimble adaptation and being able to divine the growing needs of a booming yet often opaque corner of the financial sector. Company President Rob Robertson spoke to…
As AI Lowers the Bar for Alternative Data, Where Does the Edge Move Next?
Alternative data has spent much of the past decade moving from the margins of investment research towards the mainstream. Now, artificial intelligence is accelerating that process, making datasets easier to discover, evaluate, process and combine while allowing investment teams to tackle sources that would previously have required considerable engineering resources. But that creates a new…
Do Private Markets Have a Data Interoperability Problem?
Private markets generate large quantities of data around investors, funds and transactions. Yet much of that information still moves between asset managers, wealth platforms, custodians, administrators and other participants through PDFs, portals and other document-based processes. Information that already exists digitally can be embedded in a document, sent to another organisation, extracted and then entered…
Beyond the API: FactSet on Making Financial Data Ready for Agentic AI
The effectiveness of an AI agent ultimately depends on the quality, context and structure of the information it can access. For investment firms, that puts familiar challenges around data quality, provenance and integration firmly back on the agenda – while potentially changing what they expect from their data providers. “When you’re selling data, you’re not…
15 Software and Data Vendors Solving Valuation, Integration and Reporting in Alternative Investments
The secular shift towards private markets has transformed alternative asset allocations from an institutional tactical sleeve into a core portfolio driver. Yet behind this expansion lies a stubborn operational bottleneck: alternative asset data remains inherently unstructured, fragmented and delayed. Unlike public equities, where standardised data feeds integrate seamlessly into order management systems, private market data…
Alternative Data in Capital Markets Handbook 2026
Alternative data adoption in capital markets has moved into a more disciplined phase. Simply possessing a dataset is no longer a differentiator – the priority now is how data is sourced, synthesised and deployed, and how quickly granular, disaggregated signals can be turned into validated, decision-ready insight. The 2026 edition of the Alternative Data in…
Rethinking Alternative Data Infrastructure with Aerospike
Investment firms have never had access to more data. From corporate filings and social media to satellite imagery, transaction data and other alternative sources, the range of information that can potentially feed the investment process continues to expand. But as AI becomes embedded more deeply into research and investment workflows, the challenge is shifting. Simply…
Multiplying the Value of Unstructured Data
Financial institutions have spent years expanding the range of data available to investment, trading and risk teams. Increasingly, however, the challenge is not obtaining more data but making sense of what they already have. Unstructured data is estimated to account for 80-90% of the information generated withinfinancial institutions, encompassing everything from regulatory filings and earnings-call…