Data Driven Decision Making

Data-driven decision making is a currently underutilized practice that can more effectively add value to business and development programs. By simply understanding data, NGOs, private companies, and government agencies can make better-informed decisions than in the past.


When it comes to decision making, gut instincts and human biases are deeply rooted. These choices are based on the idea of simply knowing when something is right or wrong. As a result, when a decision fails, a significant amount of time is spent determining the root cause and resolving why the decision did not produce the desired results. This results in a loss of money and productivity for the organization.

While intuition can provide a hunch or spark that leads you down a particular path, data allows you to verify, understand, and quantify your findings. It should be noted, however, that having data-generated insights is not always sufficient. Essentially, leaders and heads of governmental institutions must understand the problem before dispatching data science troops to try to solve it.

Data scientists can dig up masses of information but it’s worth nothing unless they are led by someone who understands the setting in which they are working — a leader with industry experience. Also, goals need to be clearly set before data scientists can theorize ways to reach them.

Despite the fact that the scarcity of data in informal areas is a major challenge, for this practice to be effective, many organizations, government agencies, and consulting firms must devise novel methods for collecting and sharing representative data. Some data collection methods include user digital footprints, surveys, launching a new service in a test market, and then analyzing shifts in demographic data.

To summarize, using data scientists alone in decision making is not advantageous; however, using data scientists in conjunction with good leaders is. Leaders can make decisions with much greater confidence in the outcome by utilizing the power of data.