Data-driven decision-making is defined as using facts to guide business strategy. A data analyst finds data, analyzes it, and uses it to uncover trends, patterns, and relationships. The results of this research may cause a business to change their portfolio of product and service offerings. They may branch out into a new way of distributing their products. Consider the move towards e-commerce. They may change their HR policies to improve employee retention. A retail store may adjust their hours and staffing levels to more closely coincide with the number of customers they expect to have in the store.

Analysts use data-driven decision-making and follow a step-by-step process. There are six steps to this process:

1. Ask questions and define the problem.
2. Prepare data by collecting and storing the information.
3. Process data by cleaning and checking the information.
4. Analyze data to find patterns, relationships, and trends.
5. Share data with your audience.
6. Act on the data and use the analysis results.

No matter how valuable data-driven decision-making is, data alone will never be as powerful as data combined with human experience, observation, and sometimes even intuition. Subject matter experts (SMEs) become critical in this process.

Four Steps

Models differ. Here is a four-step model that was mentioned in a Udemy course on statistics.

1. formulate a hypothesis
2. find the right test
3. execute the test
4. make a decision

Google’s PACE Workflow

The PACE workflow is similar to the above four steps but is more general.

1. Plan
2. Analyze
3. Construct
4. Execute