Forecasting
Generate more accurate forecasts and predictions
“Forecasting is very difficult, especially when it involves the future.”
– Yogi Berra
The Challenge:
Traditionally, forecasting tools were developed to deliver broad-scoped forecasts to support macro business decisions. It is commonly assumed that time-series forecasting models use only fresh, reliable streaming data. Many business leaders would be disappointed to discover how much latency undermines forecasting models due to limitations in the ability to store, access, and process the vast troves of data needed for accurate predictions. Low-latency, real-time data is more important than ever with the increasing velocity and granularity of data and the parallel demand to move towards real time.
A Better Way:
Historically, annual forecasts with quarterly updates were sufficient for business needs, but that doesn’t come close to cutting it in the Intelligence Era. Continuously and accurately predicting future needs is the gold standard of forecasting. To forecast in real-time on the scale needed to remain relevant in today’s data-driven world, one must have access to true real-time data that is both fresh and instantly available.
Introducing FeatureBase
The Real-Time Database for Any Scale
FeatureBase is designed to deliver secure, fast, continuous access to all your data in a machine-native format. The first and most crucial step in leveraging big data for predictive forecasting is ensuring all of the data is accessible and ready for analysis.
Rather than the conventional approach of moving, copying, and preaggregating data, FeatureBase extracts signals from each of the underlying data sources and stores them in a centralized access point, making data immediately accessible, actionable, and reusable. FeatureBase maintains up-to-the-millisecond data updates with no time-consuming, costly preaggregation necessary.
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Contact us today to learn more about how Molecula FeatureBase can transform your business through eliminating preaggregation and unlocking real-time value.