Analyst Price-Target Analytics Dashboard
A specialized financial data company maintains the largest available collection of stock price targets from professional sell-side analysts — the roughly 250 firms that produce such research — going back 23 years, with detailed price-target histories cleaned, parsed, and enriched and made available through Google BigQuery. The data sits in a large, technical, flat table, while investment professionals need clear, decision-supporting insights, narratives, and visualizations, and the company wants practical use cases for agentic AI on top of it. Delivering SQL-driven analysis, a prototype dashboard, and concept-level AI use cases.
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Analyze a defined subset of the historical analyst price-target and rating dataset, develop clear business-facing insights and visualizations, and package the results into an executive presentation and use-case concepts relevant to institutional investors and wealth managers — showing how analyst price-target data can support investment decision-making.
Four phases over seven weeks: discovery and requirements; analysis design, with focused questions about analyst behavior and market outcomes — such as price-target changes versus subsequent returns, rating-change patterns, differences across sectors, and the timing and direction of updates; analytics development with iterative reviews, using SQL in BigQuery, simple statistical analysis, charts, and a prototype dashboard or report book in tools such as Tableau or Power BI; and testing, refinement, and handover. Excludes production software, trading systems, investment advice, and production AI agents.
A coherent business-facing analysis that explains the analytical question, shows the data logic and method, and presents findings, implications, and limitations in terms non-technical wealth management audiences can follow. Delivered with a data analysis notebook, SQL query examples, key charts and tables, a prototype-level dashboard or report book, an executive-style presentation, and a small set of concept-level agentic AI use cases — such as an assistant that answers a wealth manager's plain-English question about analyst track records without SQL.