The 8 Stages of Data-Informed Decision-Making

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Organizations are increasingly recognizing the pivotal role of data in shaping their strategies and driving success. As part of the broader data intelligence umbrella, empowering organizations to leverage data to its fullest potential requires a systematic and structured approach to data-informed decision-making. The data-informed decision-making process encompasses several interconnected stages, each with its specific objectives and actions. Understanding and effectively navigating through these eight stages enables organizations to make informed decisions that drive success, foster adaptability, and create a culture of continuous improvement. Below are high-level details about each stage, and at the end is a link to download a checklist that you can use throughout each stage to make data-informed decisions.

Ask & Define

The Ask & Define stage is where the groundwork is laid by clearly defining the problem or decision that needs to be addressed. This stage consists of three essential steps: defining the decision, framing the decision, and modeling the decision.

Examples:

• Launching a New Product: A company is considering launching a new product and needs to define whether the market needs this product, who the target audience is, and what features it should have.
• Employee Retention Strategy: An organization is concerned about a high turnover rate and wants to define the core reasons behind it and the possible ways to improve employee retention.

Common Pitfalls:

• Lack of Clarity in Problem Definition: Often, teams rush into data collection and analysis without a clear understanding of what problem they are trying to solve.
• Too Many Cooks in the Kitchen: Sometimes, a decision involves multiple departments or stakeholders, each with their priorities and perspectives, leading to a diluted or convoluted problem definition.
• Scope Creep: The problem or decision to be addressed keeps expanding, leading to an unmanageable amount of work or analysis.
• Ignoring Historical Context: Teams sometimes define problems without understanding their historical context, which could lead to redundant efforts or the same mistakes being made.

Capture & Acquire