A data-driven decision-making framework provides a logical progression for organizations and individuals to follow to make the best professional and life choices that mitigate negative outcomes.
Humans often find it difficult to make decisions, especially when interpreting data and statistics. The National Highway Traffic Safety Administration has conducted extensive research that proves that wearing seat belts can reduce serious injuries and deaths in crashes by about 50%. According to the Centers for Disease Control and Prevention, 9.3% of Americans still choose not to wear seat belts.
The knowledge-action gap highlights the importance of educating everyone on a structured decision-making framework to leverage data-driven insights to make more efficient business decisions and build safer, more stable societies.
A data-driven decision-making framework is a structured approach that systematically incorporates quantitative and qualitative data analysis to guide and inform decisions, enabling organizations and individuals to make decisions using reliable evidence and thorough risk assessments.
Adopting a data-driven decision-making framework can help drive successful business, operational, and societal outcomes while minimizing risk. Establishing a formalized data-driven decision-making process allows individuals and organizations to systematically evaluate potential shortcomings and continually improve their decision-making processes based on new data and insights.
A key aspect of a data-driven decision-making framework is making decisions based on facts backed by data, not assumptions. Using facts reduces the chances of failure and increases the chances of success.
Adopting a data-driven decision-making framework
If data and AI are going to influence every decision, then a data-driven decision-making framework is essential. AI and Data Literacy: Empowering Citizens in Data ScienceWe suggest that organizations and individuals use the following six-step data-driven decision-making framework: When applied effectively at both the organizational and individual levels, the six steps can significantly enhance decision-making.
1. Identify and prioritize decisions
Prioritize decisions based on urgency. Identify your decision hypotheses and define KPIs to measure progress and success. For example, a company considering investing in a new product line might have a decision hypothesis that the product launch will 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 tool that helps you map out available options, their associated costs and benefits, so you can compare potential decisions in a structured way. For example, if you’re deciding on software for enterprise resource planning, you can use a matrix to compare different software options based on cost, compatibility with existing systems, scalability, and vendor support.
3. Research and collect reliable data
Collect relevant, reliable data to inform your decisions. Ensure that each data source is trustworthy and has verified its reliability. For example, if a hospital is considering purchasing a new medical device, it should collect data on patient outcomes, reliability reports of the device, and scientific studies on its effectiveness.
4. Prepare a cost-effectiveness evaluation
Evaluate the direct and indirect costs and benefits of each option outlined in the decision matrix. Quantitative evaluation is essential to make informed and balanced decisions. For example, if a local government is evaluating the cost-effectiveness of various public transport projects, they might evaluate the costs in terms of financial expenditure, environmental impacts and social benefits.
5. Consider the worst-case scenario
To identify risk mitigation strategies, consider the potential negative impact of each decision option. It is crucial to prepare for possible failures. For example, a technology company can consider worst-case scenarios for a new product launch, including product failure, market rejection, and unexpected competitive actions. Develop preventative strategies to mitigate risks and address failures if they occur.
The lack of a data-driven decision-making framework often leads to suboptimal decisions based on intuition, incomplete data, or bias.
6. Create easy-to-understand presentations
The final step is to create an easy-to-understand presentation and present the analyzed data in a clear format. A presentation allows decision makers to make an informed decision based on the evaluation. For example, investment companies use dashboards to present potential investment opportunities, displaying expected returns, risk levels, and market conditions in an attractive and easy-to-understand visual way.
The risk of uninformed decision-making
Lack of a data-driven decision-making framework often leads to suboptimal decisions based on intuition, incomplete data, or bias. Organizations that do not adopt and enforce a data-driven decision-making framework for key decisions risk reduced efficiency, reduced profit margins, and damaged reputations.
Strategic failure. Organizations that do not use a data-driven framework may miss out on important market insights. This may result in strategic decisions that are not aligned with market realities and customer needs, leading to financial losses. For example, if a company launches a new product without analyzing market demand data, it may experience poor sales and unsold inventory because the product does not meet customer needs.
Operational inefficiencies. Without data to guide decisions, companies may operate based on outdated practices and inefficient processes, resulting in increased costs and reduced productivity. For example, companies continue to use old manufacturing processes without analyzing operational data that suggests new technology could double production speeds and reduce waste.
Compliance and ethical violations. Organizations that ignore data related to regulatory compliance or ethical standards risk legal penalties and reputational damage. For example, if a pharmaceutical company ignores clinical trial data suggesting side effects for a drug and puts it on the market, it risks regulatory action, reputational damage, and loss of consumer trust.
Loss of competitive advantage. Failing to analyze competitive data can cause you to fall behind competitors who are using analytics to innovate, improve services, and gain market share. For example, a retailer that ignores consumer purchasing data may not stock trending products and will lose sales to competitors who are targeting trends through data analytics.
It’s not just organizations that are at risk – individuals too must follow data-driven decision-making processes every day or risk impacting their lives.
Poor financial decisions. Without a structured approach to data analysis, individuals may make financial decisions based on intuition or peer influence rather than hard economic data, which may lead to poor investment choices and savings plans. For example, an individual may invest in high-risk stocks based on a friend’s recommendation rather than thoroughly analyzing the stock’s past performance and market conditions.
Career failure. Making career decisions based solely on personal preference or immediate opportunity without considering long-term data can lead to choices you regret. Let job market trends, industry growth forecasts, or past career successes help you decide which direction to go. Choosing a career path without analyzing future job market trends and sticking to a declining industry can limit your career growth and stability.
Health care risks. Individuals may make health decisions based on anecdotal evidence or incomplete information rather than relying on data-driven insights, leading to ineffective or harmful outcomes. For example, individuals may follow fad diets without considering nutritional data or consulting health data that takes into account personal health conditions such as diabetes or heart disease. This may lead to poor health and ineffective dieting outcomes.
Educational discrepancies. Decisions about education, especially postsecondary and professional education, made without analyzing relevant educational outcomes data can lead to misalignment with career goals and economic realities. For example, someone may choose a college major based entirely on personal interest without analyzing graduate employment rates, average earnings, or industry growth data in that field, which can lead to difficulties finding employment in that field after graduation or to being underemployed.
Bill Schmarzo is the former CIO of Hitachi Vantara and known for his groundbreaking work in data science and automated machine learning, as well as former CTO at Dell EMC and VP of Analytics at Yahoo, and is the author of several books on big data.
