Businesses recognize that improving analytics capabilities can enhance forecasting of demand-and-supply-related variables. An intelligent architecture is presented to incorporate Big Data and Machine Learning for Business Forecasting. Defining the components of the architecture enables discussion of Data Ingestion and Integration, the first step. The Data Ingestion and Integration layer integrates data from sources within and outside the enterprise through batch and streaming pipelines; the extraction-transformation-loading process can be simplified through extraction-loading-transformation. Streaming processing of time-sensitive data minimizes latency. Internally developed data-stitching tools ensure continuity in data feeds. Quality controls for accessibility, accuracy, consistency, and credibility safeguard data fitness for use in forecasting models. Such quality standards are indispensable given the criticality and volume of data involved. Misalignment of demand and supply increases costs, reduces margins, diminishes customer satisfaction, and lowers sales. Pricing and promotional strategies are informed by demand forecasts. Forecasts of major revenue contributors guide budgeting and planning. Financial forecasting aids stakeholder communication, allocation of funds to support business growth, decision-making, and valuation. External factors such as market trends, government policies, rates of economic growth, and liquidity conditions shape financial forecasts. Quantifying uncertainty increases attention to potential adverse outcomes and supports the development of contingency strategies. Business forecasting delves into demand-supply dynamics and their impact on finances. A typical demand-supply-response chain links Demand → Pricing → Supply-Side → Financial Implications; Uncertainty and Scenario Analysis run across the chain.