Cost of delaying ONE Sharpe-enhancing dataset
Illustrative annual P&L drag (USD millions) from slow time-to-production vs accelerated with Kamba Analyst, by firm size (conservative, per-dataset scenario).
Firm size Annual drag without Kamba Analyst Annual drag with Kamba Analyst
~$1B AUM

≈ $0.25M / year

Most of the first-year Sharpe uplift is forfeited as the dataset spends ~9 months in backlog, manual reviews, and handoffs.

≈ $0.08M / year

Agentic Smart Search, standardized DQRs, and orchestrated backtests cut time-to-production to ~3 months and improve usable sizing.

~$10B AUM

≈ $2.5M / year

Larger books magnify the P&L impact when high-Sharpe datasets stay in “interesting later” instead of reaching production quickly.

≈ $0.8M / year

Workflow automation and reusable DQR / backtest templates shorten cycle times and increase the share of datasets that actually get sized and deployed.

>$10B AUM
(e.g. ~$30B)

≈ $7.5M / year

Enterprise-scale portfolios pay a meaningful “status quo tax” when Sharpe-accretive datasets remain trapped in fragmented, manual workflows.

≈ $2.4M / year

Centralized Smart Search, DQRs, and backtests turn structural backlog into a repeatable, Sharpe-uplift pipeline as datasets move from idea to production in a single, governed flow.

Approximate expected annual drag from wasted team capacity (slow, manual reviews and handoffs), underused vendor spend (paid-for data that never reaches production), and missed or delayed alpha when a single dataset that could improve a core strategy’s Sharpe ratio is not sized or deployed in time.

Illustrative modelling assumptions (aligned with the Kamba 2025 State of Data white paper): one high-quality dataset can, if fully exploited, support strategies with ≈ +0.2 incremental Sharpe on a 20% sleeve of the book (≈ 0.04% incremental return on total AUM per year); the status quo workflow captures ~36% of that potential (≈9-month time-to-production, partial sizing), while an AI-native, Kamba-style workflow captures ~80% (≈3-month time-to-production and higher effective utilization). Orange values show the residual annual drag vs full potential under the status quo; blue values show residual drag after workflow modernization.

All figures are ChatGPT-calculated, illustrative numbers for one Sharpe-enhancing dataset per firm size. Directional only — not forecasts, guarantees, or investment advice.