\n<\/p>\n
For instance, State Street now generates a continuous stream of inflation data for its clients, merging traditional indicators with alternative data sources like\u00a0<\/span><\/span>observed<\/span><\/span><\/span>\u00a0consumer spending and digital news. \u201cIf we look at central banks and interest rate projections, you can use this information set to address gaps when the central bank isn\u2019t speaking,\u201d sa<\/span>ys<\/span>\u00a0Clark. \u201cWhat is\u00a0<\/span>the rhetoric<\/span>\u00a0around a central bank? Our research shows that this can have implications for forecasting yields. More interestingly,\u00a0<\/span>you\u2019re<\/span>\u00a0getting breadth in perspective and incremental alpha outside of periods where conventional data sources are available.\u201d<\/span><\/span>\u00a0<\/span><\/span><\/p>\n Notably, Bloomberg offers\u00a0<\/span><\/span>alternative\u00a0<\/span>data\u00a0<\/span>solution<\/span>s<\/span><\/span>\u00a0to its clients via Bloomberg Terminal and data feeds, and\u00a0<\/span>thes<\/span>e<\/span>\u00a0include\u00a0<\/span><\/span>consumer transaction data analytics from Bloomberg Second Measure and foot traffic data analytics from Placer.ai,\u00a0<\/span>as well as\u00a0<\/span>Similarweb\u2019s<\/span>\u00a0web traffic data.<\/span><\/span>\u00a0<\/span><\/p>\n 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.<\/span>\u00a0<\/span><\/p>\n \u201cFrom a discretionary point of view,\u00a0we\u2019re\u00a0seeing discretionary managers able to look over a much broader breadth of names because\u00a0they\u2019ve\u00a0got the scalability to gain insight from that data.\u00a0They\u2019re\u00a0able to pick out things they never could before,\u201d says\u00a0Tushara Fernando, Head of Data and Machine Learning at the Man Group. \u201cFrom a systematic point of view, we\u2019re able to translate and quantize unstructured data into more structured data that we can use in our quant models.\u201d<\/span>\u00a0\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n Indeed, the proliferation of data and AI tools has pushed discretionary and systematic approaches towards convergence,\u00a0says\u00a0Systematica\u2019s\u00a0Dooms. \u201cDiscretionary managers get a lot of benefit from\u00a0GenAI tools in terms of adding code to their process, making it more systematic. Systematic investors get to use data traditionally in the human realm \u2013 unstructured data \u2013 and parse it into signals,\u201d he\u00a0explains, adding, \u201cUnder the hood, there\u2019s a lot of work to get that process right: how do you shape and architect the data to make it consumable by AI workflows?\u201d<\/span>\u00a0<\/span><\/p>\n Zooming in on agentic AI, experts point to\u00a0the technology\u2019s\u00a0early progress in\u00a0enabling\u00a0practical, tool-driven workflows.\u00a0Says\u00a0Dooms, \u201cTo me, agentic AI is not just about chain-of-thought and automation \u2013 that\u2019s table stakes. Your basic ChatGPT-style chatbot does planning and thinking.\u00a0It\u2019s\u00a0really about tool-calling: architecting processes where you can\u00a0identify\u00a0things you\u00a0were not able to\u00a0do before and can now\u00a0do\u00a0thanks to scalability and then presenting data so it can be used by an\u00a0LLM.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n State Street\u2019s Clark cites his own organization\u2019s internal analytics\u00a0capability\u00a0as a prime example of agentic AI\u2019s potential. \u201cWe\u2019ve got different data sources \u2013 our own research, structured and unstructured data \u2013 and we\u2019ve got agents querying tools to trigger further actions: generating investment insights for clients or internal stakeholders, triggering signals for capital markets settings, etc.,\u201d he explains, \u201cWe\u2019re not at the end state where that\u2019s fully implementable, but we\u2019re well into that pathway.\u201d<\/span>\u00a0<\/span><\/p>\n Implementation of\u00a0cutting-edge\u00a0data 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. \u201cIt\u2019s 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\u00a0or\u00a0no-code solutions so they can play with their own data and derive insight,\u201d\u00a0says\u00a0the Man Group\u2019s Tushar.\u00a0<\/span>\u00a0<\/span><\/p>\n \u201cWe 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.\u00a0That\u2019s\u00a0key to scaling up our contribution,\u201d\u00a0he adds.<\/span>\u00a0<\/span><\/p>\n State Street\u2019s Clark\u00a0agrees with this statement,\u00a0observing, \u201cI think the big innovation\u00a0is that data is now for everyone. The notion that\u00a0you\u2019re\u00a0a decision\u00a0maker\u00a0but someone else handles data and insight is dead. Building data literacy is the big innovation.\u201d\u00a0<\/span>\u00a0<\/span><\/p>\n Interested in more insights from Bloomberg Enterprise Tech & Data Summit 2025 in London, click here. Learn more about Bloomberg Enterprise Tech & Data solutions\u00a0here.\u00a0<\/span><\/p>\n Insights in this article are based on panels and fireside discussions at the Enterprise Tech & Data Summit held in London in November 2025<\/span>.<\/span><\/span>\u00a0<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":" For instance, State Street now generates a continuous stream of inflation data for its clients, merging traditional indicators with alternative data sources like\u00a0observed\u00a0consumer spending and digital news. \u201cIf we look at central banks and interest rate projections, you can use this information set to address gaps when the central bank isn\u2019t speaking,\u201d says\u00a0Clark. \u201cWhat is\u00a0the<\/p>\n","protected":false},"author":1,"featured_media":40572,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[1111,659,12239,3355,8238,4259],"class_list":{"0":"post-40571","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-business","8":"tag-data","9":"tag-find","10":"tag-insights","11":"tag-needed","12":"tag-opportunities","13":"tag-type"},"_links":{"self":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts\/40571","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=40571"}],"version-history":[{"count":0,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts\/40571\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/media\/40572"}],"wp:attachment":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=40571"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=40571"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=40571"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}How data drives discretionary v<\/span>ersus<\/span>\u00a0systematic processes<\/span><\/span>\u00a0<\/span><\/h2>\n<\/p>\n
The rise of agentic workflows<\/span><\/span>\u00a0<\/span><\/h2>\n<\/p>\n
Why democratizing data access is essential for scalable investment processes<\/span><\/span>\u00a0<\/span><\/h2>\n<\/p>\n