6 key steps form a data-driven decision-making framework
A data-driven decision-making framework provides guidelines that any organization or individual can use. Improve decision-making on a professional and personal level.
A data-driven decision-making framework provides the logical progression organizations and individuals should follow to make the best professional and life choices that mitigate negative outcomes.
Humans often find it challenging to make decisions, especially when interpreting data and statistics. The National Highway Traffic Safety Administration conducted extensive research that attests wearing a seat belt can reduce serious crash-related injuries and deaths by approximately 50%. According to the Centers for Disease Control and Prevention, 9.3% of Americans still choose not to wear seat belts.
The gap between knowledge and action highlights a critical need to teach everyone a structured decision-making framework that uses data-driven insights to make more efficient business decisions and create a safer, more stable society.
A data-driven decision-making framework is a structured approach that systematically incorporates quantitative and qualitative data analysis to guide and inform decision-making. It ensures organizations and individuals make decisions using reliable evidence and thorough risk assessment.
Adopting a data-driven decision-making framework can lead to successful business, operational and societal outcomes while minimizing risks. Establishing a formalized data-driven decision-making process enables individuals and organizations to systematically evaluate potential drawbacks and continuously improve decision-making processes based on new data and insights.
The crucial aspect of a data-driven decision-making framework is basing decisions on facts supported by data rather than assumptions. Using facts reduces the chances of failure and increases the likelihood of success.
Embracing a data-driven decision-making framework
A data-driven decision-making framework is vital when data and AI influence every decision. In my book, AI & Data Literacy: Empowering Citizens of Data Science, I propose organizations and individuals use the following six-step data-driven decision-making framework. When applied effectively at both organizational and individual levels, the six stages can significantly enhance decision-making.
1. Identify and triage the decision
Prioritize decisions based on urgency. Clarify the decision hypothesis and define KPIs to measure its progress and success. For example, a company that is considering investing in a new product line could have a decision hypothesis that launching the product should capture a significant market share in the first year. KPIs might include market penetration, return on investment and customer acquisition costs.
2. Create a decision matrix
A decision matrix is a helpful tool to map out the options available, associated costs and benefits. It facilitates a structured comparison between potential decisions. For example, when deciding on software for enterprise resource planning, the matrix might compare different software options based on cost, compatibility with existing systems, scalability and vendor support.
3. Research and gather reliable data
Collect relevant and reliable data to inform the decision. Ensure each data source is trustworthy and vetted for its credibility. For example, if a hospital is considering buying new medical equipment, it should gather data on patient outcomes, equipment reliability reports and scientific studies on its effectiveness.
4. Create a cost-benefit assessment
Evaluate each option's direct and indirect costs and benefits listed in the decision matrix. A quantitative assessment is essential to make an informed and balanced decision. For example, when a local government assesses the cost-effectiveness of various public transportation projects, it might evaluate costs regarding financial outlays, environmental impact and social benefits.
5. Explore worst-case scenarios
Consider the potential adverse outcomes of each decision option to identify risk mitigation strategies. It is crucial to prepare for possible setbacks. For example, a technology firm can explore the worst-case scenarios of a new product launch, such as product failure, market rejection or unexpected competitive actions. Develop preemptive strategies to mitigate risks and address setbacks should any occur.
The lack of a data-driven decision-making framework often leads to suboptimal decisions based on intuition, incomplete data or bias.
6. Create an understandable presentation
The final step involves creating an easy-to-understand presentation and presenting the analyzed data in a clear format. Presentations ensure decision-makers make an informed decision based on the assessment. For example, an investment firm might use dashboards to present potential investment opportunities, displaying expected returns, risk levels and market conditions in an engaging and straightforward visual manner.
Risks of uninformed decision-making
The lack of a data-driven decision-making framework often leads to suboptimal decisions based on intuition, incomplete data or bias. Organizations that do not instill and enforce a data-driven decision-making framework for their important decisions can be less efficient, have lower profit margins and might damage their reputation:
Strategic failures. Organizations that do not use a data-driven framework might miss out on critical market insights. The outcome is strategic decisions that do not align with market realities or customer needs, potentially leading to financial losses. For example, a company might launch a new product without analyzing market demand data, leading to poor sales and unsold inventory because the product does not meet customer needs.
Operational inefficiencies. Without data to guide decisions, companies might operate based on outdated practices or inefficient processes, resulting in higher costs and lower productivity. For example, a company continues to use an old manufacturing process without analyzing operational data which suggests a newer technology could double the production speed and reduce waste.
Compliance and ethical breaches. Organizations that ignore data regarding regulatory compliance or ethical standards risk legal penalties and reputational damage. For example, a pharmaceutical company could ignore clinical trial data that suggests adverse effects of a drug and push it to market. It risks regulatory action, harm to its reputation and loss of consumer trust.
Loss of competitive advantage. Failing to analyze competitive data can leave organizations behind competitors who use analytics to innovate, improve services and capture market share. For example, a retail company ignoring consumer purchasing data might fail to stock trending products, resulting in lost sales to competitors who target trends through data analysis.
Organizations are not the only ones at risk. Individuals also need to follow a data-driven decision-making process every day or risk potential life-affecting consequences:
Poor financial decisions. Without a structured approach to analyzing data, individuals might make financial decisions based on gut feelings or peer influence rather than solid economic data, leading to inadequate investment choices or savings plans. For example, an individual may invest in a high-risk stock based on a friend's recommendation instead of thoroughly analyzing the stock's historical performance and market conditions.
Career missteps. Career decisions based solely on personal preference or immediate opportunities without considering long-term data can lead to regrettable choices. Use job market trends, industry growth projections or the historical success of paths to decide which direction to take. Choosing a career path without analyzing the future job market trends and sticking to declining industries can limit career growth and stability.
Health management risks. Individuals might make health decisions based on anecdotal evidence or incomplete information without relying on data-driven insights, leading to ineffective or harmful outcomes. For example, an individual might follow a trendy diet without considering nutritional data or consulting health data that accounts for personal health conditions such as diabetes or heart disease. It might lead to worsening health conditions or ineffective diet outcomes.
Educational misalignment. Decisions about education, notably higher education and professional training, made without analyzing relevant educational outcomes data can lead to misalignment with career goals and economic realities. For example, an individual might choose a major in college based purely on personal interest without analyzing job placement rates, average earnings or industry growth data for graduates in that field. They might encounter difficulties finding employment in their field or underemployment after graduating.
Bill Schmarzo is the former CIO of Hitachi Vantara, where he was recognized for groundbreaking work in data science and automated machine learning. He has also worked at Dell EMC as CTO and as vice president of analytics at Yahoo. He has also written several books on big data.
