D🚫N'T get it wrong. #ai without #data is possible❗With only 10% of your data, you can automate 90% of your business decisions in your organization.

💢Many #data and #analytics professionals and data scientists are wrongly under the impression that business decision automation requires tons and tons of data. But, only certain types of #ai #algorithms need extensive amounts of data. In fact, starting an #ai project to automate business decisions with data is lazy work. More likely, the results will be biased as you use the data to justify the already-made decisions rather than using data for decision-making.

🤔How is it possible to create #ai without #data? You have guessed it right! Use Decision-Centric Approach to:
– Select a business operation that you want to improve the decision-making process
– Create a list of all the business decisions that influence the operation
– Decompose the business decisions into smaller decision units
– Depict the dependencies and the relationship between the decision units
– Determine the type the type of decision unit (probabilistic vs deterministic)
– Identify the metrics for those decision units and required *data inputs*

🎯As the result of this work, you will create a set of hierarchical Decision Graphs that present a holistic view of business decisions. These decision graphs are used as the blueprint for business decisions in operation. By investigating the decision units and their metrics in relation to business KPIs, you can identify which one has the most positive impact on certain business decisions. Therefore, these decision graphs enable you to prioritize automation based on its impact on business operations.

💡Now that you have identified part of the Decision Graphs to automate, you can see that NOT, all of them require data; almost 10% of them will be using #machinelearning #ml models, so they require data for model training and validation. The rest of the decision units will use other techniques such as #rules, #optimization, #calculation, #math etc.

✨The additional benefit of this approach is for the decision units requiring #ml #machinelearning models, you can build and train “small models” as they have a very specific intent with limited scope in the Decision Graph. Therefore:
– the amount of required data for #ml #machinelearning is significantly reduced
– data selection and feature engineering are more straightforward
– the trained model will be more accurate

🚀With the Decision-Centric Approach® https://lnkd.in/gcWUy9eU , you will have a better chance of automating business decisions without tons of #data. Also, when using #ml #machinelearning for some parts of the Decision Graph, you can use your limited data to produce more accurate trained models with better scores.

#decisionintelligence

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Published May 3rd, 2024 at 07:30 am