This paper develops a nowcasting model to produce timely estimates of quarterly GDP growth for Kenya. Nowcasting combines official monthly indicators with digital transaction data. Exploiting strong comovement of macroeconomic time series, a few latent factors summarize aggregate dynamics and enhance forecasts. The model is updated with each data release, decomposing revisions into predictable and news components. Results demonstrate robust performance of the nowcasting model in data-constrained environments and show that nowcasting is applicable to low-income countries.