Dialexa’s Data Science team conducted workshops during the discovery phase and shadowed key stakeholders to understand their processes and pain points. Applying our design thinking approach, the technology assessment allowed us to analyze their current platforms, technologies, and data sources used to uncover trends. Our assessment assessed types and efficacy of the models through a more sophisticated and complete analysis of the available data.
Margins were shrinking in some markets more aggressively than others. Accuracy was more critical than ever to their bottom line, yet their existing model–which contained lots of data–underperformed.
This underlies a common data problem across industries; having a lot of data isn’t helpful or informative. Instead, knowing what data is the correct data and understanding how it should be viewed matters tremendously.
Our teams engineered a new Day-Ahead and Real-Time (DART) forecasting model that dramatically improved forecasting accuracy and speed. The new model is 40x more efficient and runs in minutes. It is also fully automated and is five times more accurate than the previous model.
The team also built a new enterprise-level support platform to deploy this model and future models onto the client’s newly modernized tech stack. It previously took our client days to update their old model. In a time-sensitive business like energy, that equates to days of compromised profits. Our new model was able to update frequently to keep up with the volatile market.
This enterprise-wide, model pipeline process has set a standard for reliability that is now used across the client’s multiple data science teams.
Improved the model from a 25% margin of error to less than 5%, ensuring confidence in the model forecasts
Redeployment time was reduced from several weeks to less than one day, dramatically reducing technology overhead costs
Reduced the time to run the model from an hour to less than 1 minute and removed all manual intervention points
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