2 minutes of reading

Have you ever asked yourself what data-informed decision making is? Nowadays, any and every company or educational institution generates data, even involuntarily. It is inevitable. Some make intelligent use of this data to optimize their processes, while others are not even aware of it.

The use of this data is extremely advantageous, especially when it comes to decision making and people management. Who has never heard of DDDM, or “data-driven decision making?” 

DDDM – Data-driven decision making

DDDM is a term that has gained a lot of popularity in recent years. It refers to a concept widely studied and, in a way, a term that has undergone transformations and changes in interpretation. DDDM suggests that every decision must be made based on data, and only on data. That is, the decision must ignore human opinion and / or influence, and it must be made based on insights extracted from data.

Data-driven decision is immune to human skepticism and simply eliminates the dogma “this is because I know” and defends the dogma “this is because the data says so,” both equally hasty and erroneous.

For this reason, another concept has been recently introduced in order to evolve DDDM, known as DIDM, or Data-informed decision making.

DIDM – Data-informed decision making

The latter has been defended by scholars and professionals from various fields, precisely because it recognizes the value of the insights acquired through data analysis while considering the importance of the human factor in this process as well.

Organizations that make data-informed decisions understand their limitations, while those that make data-driven decisions simply use the data as they are, without questioning it, trusting it 100%.

In order to better understand the difference between these two concepts, we can analyze automated systems, for example. Nowadays, driving a car is a highly automated activity. Many companies, such as Uber, have launched self-driving cars that do not require a driver to conduct the vehicle. Okay, so far so good. However, the driver is still very important in many situations. For example, when an external factor that has not been predicted by the machine comes into play. In this case, the presence of the driver would be essential to take over control of the vehicle and avoid a possible accident.

Finally, we need to stay informed and use all the resources and intelligence available to be more and more efficient and remain relevant in both the corporate and educational environments. We need to stay in control, and let the data bring us as many insights as possible. At least for now.