ReportWire

Tag: Data Integration

  • What type of data is needed to find opportunities | Insights | Bloomberg Professional Services

    [ad_1]

    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. 

    [ad_2]

    Bloomberg

    Source link

  • Where enterprise data is headed in 2026 | Insights | Bloomberg Professional Services

    [ad_1]

    The rise of interoperable and multi-cloud data infrastructure 

    The move to the cloud has been one of the key trends in in the financial services space over the last decade, but companies are now finding that optimal solutions require integration of multiple cloud platforms, says Neill Clark, Managing Director and Head of State Street Associates EMEA. “Five years ago, I would have said the key is to get all of your data on a cloud – ideally a single cloud – and once you’ve done that, you’ve got a set of cloud-native tools and you’re good to go,” says Clark.  

    “Now the notion of a multi-cloud strategy plus on-prem makes more sense. The reality is everyone ended up multi-cloud by default because they couldn’t get to one cloud. Now it’s the right choice to make, because new tools are coming out all the time and computing costs vary.” 

    Colette Garcia, Global Head of Enterprise Data Real Time Content at Bloomberg, confirms that this approach aligns with how firms are striving to balance flexibility and precision in their data strategies. “That resonates with us – getting you the data wherever you need it, cloud-agnostic, on-prem-agnostic. It’s the quality of the data and being able to deliver it wherever you need it,” she says. 

    Integration of AI into investment and research workflows 

    Another growing trend experts point to is the integration of agentic AI into core areas of business, including investment research and portfolio management. The goal is not to eliminate human insight and judgment, but to enhance it through automated data retrieval. 

    According to Grégoire Dooms, Head of Data Research & Development at Systematica, AI enriches the information sets that analysts and portfolio managers can incorporate into their decision-making. “AI has completely lowered the bar for access and scale of processing unstructured data – text data,” says Dooms. “It is enabling us to build feature-extraction pipelines that are sector-specific or asset-specific and scale them tremendously.”  

    State Street’s Clark adds that broader data plus natural language processing tools have significantly enhanced his organization’s research product. “We’ve seen significant metric improvements: 60% better measurement of some macro criteria, 20–30% better prediction outcomes in certain use cases,” he comments. “It’s a meaningful revision in how you can access unstructured data.”

    Dawn of a new era for explainable and real-time data access 

    AI capabilities have revolutionized the relationship between financial services organizations and their clients, allowing firms to share data and insights with clients in real-time.  “We’re experimenting with open-architecture data sharing with our clients—with no barriers at all,” said State Street’s Clark.  

    “You’ve still got to assemble the control around it, with real-time access to data at a moment of your choosing, in a format of your choosing, integrated and delivered in a way that you can integrate with your other data sets. That feels like the way we’ll be exchanging information with our clients in the future, somehow.”  

    Notably, as AI’s capabilities grow, companies have to continually upgrade the guardrails that ensure regulatory compliance and ethical practices, turning strong data strategy and governance into a competitive advantage. And in a fast-approaching future where AI-enabled models may make investment decisions without human input, the need for these controls becomes even more critical. 

    Keeping up with a fast-changing environment 

    Technological capabilities are changing so fast that it’s hard to predict what AI will look like in a month, let alone a year or five years out. However, experts agree that technology already spreading into many aspects of the financial services industry would only become more ubiquitous.  

    “It’s not really about who’s going to use AI and who’s not, and who’s going to get left behind,” says Bloomberg’s McManus. The question really becomes: Who’s going to use it in the most intelligent way? Who’s going to be thoughtful, measured, and really understand how to derive real value from the AI they develop. 

    Explore how Bloomberg is using AI to deliver actionable insights that empower you to move faster, work smarter and achieve better results 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.    

    [ad_2]

    Bloomberg

    Source link

  • How companies integrate private market data at scale? | Insights | Bloomberg Professional Services

    [ad_1]

    What’s driving the push to integrate private market data with public market workflows? How are investors and asset managers addressing persistent data frictions to create a unified view across portfolios? 

    This episode of Market Dialogues features Leila Sadiq, Global Head of Enterprise Data Content at Bloomberg, in discussion with Todd Hirsch, Head of Private Capital at Point72, Mark Neely, Director of Alternative Investments at GenTrust, and Avi Turetsky, Partner and Head of the Quantitative Research Group at Ares, on how private markets are evolving toward greater transparency, valuation consistency, and data connectivity. 

    Source: Enterprise Data & Tech Summit, October 16, 2025, New York

    The Market Dialogues podcast series provides access to curated, thought-provoking discussions from Bloomberg global events. It offers in-depth insights from experts on key trends and themes driving the markets today and beyond. 

    Discover more conversations in the Market Dialogues series here. 

    Featured insights from this episode of Market Dialogues: 

    On technology changing valuation transparency

    Todd Hirsch: Technology has enabled us to have much greater frequency of inputs for valuations. Whether they’re public or private… I think you can have a better sense of how often you want to mark your positions and how frequently you need to adjust those marks based on the inputs you have.

    When new information comes in, it’s important that it gets incorporated. So if a building sells down the street and you now know there’s a new comparable, you can incorporate that in real time. The frequency of adjustments on the private side today is much better than it has ever been, and transparency for investors continues to improve as that frequency increases, and the supporting data and analytics become stronger.* 

    On the data gap between public and private companies

    Avi Turetsky: We know that public markets show meaningfully higher vol[atility] than private market NAVs. We know the valuations are different: public market valuations represent marginal trades, while private market valuations are appraisal-based. We have a pretty good sense of the relationship between the two.  

    But whether privately owned companies are actually more stable than publicly traded companies, if you’re looking at revenue, EBITDA, or cash flows, as far as I know, no one knows the answer to that question…The prices are more stable… But whether the revenues are more stable to EBTIDA, no one knows because no one’s been able to get the data on large enough scale to my best knowledge. 

    On managing allocation across private and public assets 

    Mark Neely: I focus a lot of my time on the allocation model for clients, and it’s always challenging because we’re constantly comparing public and private markets and they don’t really compare. You look at private equity over a trailing two-year return, and it doesn’t track public equities over the last 10 years  

    Clients will say, “The public markets are really strong, I want to go into private,” and I’ll say, “Okay, but let’s look at what the EBITDA multiples are that private markets are purchasing at. What are things being marked at?” That transparency, or lack of it, makes it very challenging to take advantage of dislocations or to reallocate capital between private and public markets, or up and down the capital stack in direct lending, Broadly Syndicated Loans (BSLs), and term loans.” 

    *Quotations have been edited for brevity and clarity.

    [ad_2]

    Bloomberg

    Source link