Data-driven decision-making (DDDM) is a familiar term within the business sector. We all understand its significance and how it can benefit businesses.
However, data-driven decision-making is not just about incorporating data into the decision-making process. There are other factors that determine the efficiency of our data-driven decision-making.
Unfortunately, if it's not efficient enough, it may not be as beneficial to businesses.
Sertis would like to invite you to explore what data-driven decision-making truly means and what constitutes effective data-driven decision-making. Let's dive right in.
What is data-driven decision-making?
Data-driven decision-making (DDDM) is the practice of utilizing data, which includes facts, metrics, and insights from analytics, to inform and guide decision-making in a business context. This approach ensures that decisions are aligned with the organization's goals.
In essence, data-driven decision-making involves making informed decisions based on relevant data rather than relying solely on assumptions, experience, or predictions. This approach enables more realistic, well-informed, and context-specific decisions that ultimately serve the best interests of the business.
Why is data-driven decision-making important?
Making decisions without data is akin to entering a maze without a map, relying on gut feelings and hope to find the exit. On the other hand, incorporating data into decisions is like navigating a maze equipped with a map, allowing us to reach our destination quickly.
In the world of business, data-driven decision-making relies on filtered and analyzed data, with predictions based on real-time, collected facts. This data serves as the guiding map that leads our business toward its goals.
According to MIT Sloan, organizations that make data-driven decisions and operations achieve 5% higher efficiency and 6% higher profits than their competitors.
According to PwC, organizations that utilize data-driven decision-making experience a 4% increase in revenue compared to those that do not.
According to Analytics Insight, organizations that base their decisions on data can make choices 2.5 times faster than their counterparts.
What makes good data-driven decision-making?
Quality data: The data used for analysis and decision-making must be accurate, relevant to the goals, and unbiased. As data is an important resource for making decisions, using incorrect data can lead to wrong decisions.
Efficient and accurate analysis: Another critical aspect, as important as data quality, involves the use of effective analytical methods. It's essential to choose methods that are suitable for the complexity of the data, whether they are statistical approaches or AI models. Using inappropriate or overly simplistic methods can impede the extraction of valuable insights.
Data literacy: Ensure that relevant personnel have an understanding of the data's nature, can use data appropriately, and interpret it effectively for data-driven decision-making.
Aligned understanding of goals: It is crucial to ensure that all team members have a shared and consistent understanding of the organization's goals and direction. Misunderstandings can lead to confusion in decision-making, making it challenging to achieve success.
Quick decision-making: Data-driven decision-making should facilitate faster processes compared to traditional decision-making. It should use real-time data to adapt to market changes and make timely adjustments.
Continuous learning and feedback: It is essential to maintain a feedback loop to continuously measure and evaluate decision-making processes. This ongoing evaluation allows room for improvements in subsequent decisions, ultimately aiming for maximum efficiency.
Ethical and legal compliance: Data-driven decision-making must adhere to ethical and legal standards, including respecting data privacy, complying with the law, and preventing data misuse.
Expertise: Those involved in the decision-making process should be experts with strong knowledge, understanding, and experience in their field to ensure confidence in decision outcomes.
Examples of good and bad data-driven decision-making
Examples of good decisions
A business conducts market research and employs AI models to understand customer preferences and problems in each segment before developing new products.
An airline analyzes the performance and maintenance of each airplane part, using experts to make decisions on predictive maintenance timelines, reducing costs and schedule disruptions.
Examples of bad decisions
An automotive company launched a new model based solely on current sales records, without predicting future trends and changing preferences. The analytics method used was unsuitable for their data, resulting in inaccurate results and an inability to achieve their target sales.
Schools evaluate teachers solely based on students' average test scores, neglecting other critical factors such as teachers' experiences and student engagement in the learning process. Additionally, the evaluators lack sufficient experience in the education field, hindering the school's ability to achieve the desired teaching improvements.
Do you have a clear understanding of what good data-driven decision-making looks like? If you're interested in assessing whether your organization truly embraces data-driven practices, explore our '10 Checklists to Determine if Your Company is Data-Driven'.
Sertis is an AI and data solutions provider. We offer consultancy and solution development services to assist organizations in becoming fully data-driven. We are here to partner with you, taking your business towards a successful future and helping you unlock the value of your data.
Learn more about solutions from Sertis: https://www.sertiscorp.com/th/solutions