Data driven decision making (DDDM) is a process that involves collecting data based on measurable goals or KPIs, analyzing patterns and facts from these insights, and utilizing them to develop strategies and activities that benefit the business in a number of areas. Fundamentally, data driven decision making means working towards key business goals by leveraging verified, analyzed data rather than merely shooting in the dark.
This process of Data Driven Decisions it's based in the DIKW model. This model consists basically in transforming the raw data into something that can be useful for decision making. This raw data it's transformed to information, then into knowledge and wisdom, to finally be traduced into actions and/or well-defined business strategies.
Get insights of market trends, improve the transaction rate or implement a trading bot to handle daily operations.
Collect, clean and consolidate all the data sparsed out trough all the institutions. Get clear information to take precise targeted actions.
Improve the R&D lead time and succes rate. Drive the innovation by the data. Customize your own digital reaserch environment
Improve Quality Assesment, reducing costs and automating repetitive tasks. Optimize the overall quality metrics.
Discover new routes, hubs or connections for your distribution network. Improve customer statisfaction by reducing ditribution lead times.
Recommend better products to your customer, or predict which new product will be more profitable. Obtain customized reports.
Get detailed predictions of consumer habits, obtain better results of your marketing campaigns by targeting more specific customers.
Obtain clear predictions of win/loose rate for matches of different sports. Simplify the game design integrating AI from the core.
Gather and process data from power ditribution terminals, set up on-site data collectors. Analyze the complete ditribution netowrk to anticipate potencial flaws.