These days, different organizations and businesses are faced with many decisions at different levels. Many of these organizations still depend on the personal intelligence and instincts of their managers to make the right decision at the right time. However, the speed of developments in the contemporary era and the explosion of information has surpassed the speed of our intelligence and instinct; Therefore, it is necessary to review decision-making processes and use data science and systemic approach to make decisions. In this article, we explain how to develop a data-driven mindset in the organization.
What is a data-driven mindset and what are its uses?
Data-driven thinking is an attitude that allows a person to analyze issues and phenomena based on available information and data. This analysis has several applications; For example, businesses can make better decisions using data science and data processing.
However, data-driven thinking is not limited to the application of data science to decision-making in businesses, but is a window through which phenomena can be analyzed. Hence, its functions will be observed in our daily and personal lives as well.
For example, in recent years, false news has become very popular on social networks. Someone with a data-driven mindset, instead of accepting the first thing he reads in that space, starts collecting data and researching it to confirm or disprove it. Also, if a claim is made, it tries to support that claim by gathering evidence.
How to cultivate a data-driven mindset?
A data-driven attitude cannot be achieved overnight. This achievement requires practicing, being critical, and accepting those mental errors that your mind may make when working with data. The following methods can be used to cultivate this attitude:
1. Habit of supporting decisions with the help of data
When you start your career as a data scientist and analyst, you need to get used to making decisions based on data. These decisions also include choosing the method of data analysis. However, getting used to making decisions based on data is not an easy task. Our brains make most evolutionary decisions based on apparent patterns.
To change to a data-driven mindset, it is necessary to abandon the habit of making unnecessary hypotheses and conjectures; For example, when working with data, instead of using random variables to analyze the data, select the most appropriate variables to do so by comparing the analysis models.
The most important thing when working with data is to let go of instinctual guessing and make assumptions based on observable data. To achieve this, you can write down the guesses you make while working with the data and then compare their validity with the available data.
2. Ability to see data as it is!
Apart from basing data on decision making, there is another important thing you need to do to cultivate a data-driven mindset: look at data as it is, not as you would like it to be. Our brains have a strong tendency to delete unwanted data and instead highlight desirable data.
In fact, our brains subconsciously tend to manipulate positive data and come up with all sorts of justifications for masking unwanted data and discrediting it. However, we must learn to understand the true meaning of data when we look at it, not the meaning that our minds tend to construct around it.
3. Understand the capabilities and limitations of working with data
Understanding what can be done with data is one of the primary tasks of a data analyst. But it is equally important to understand what can not be done with the data. The combination of the two makes our expectations of the data world come true.
The key to achieving a data-driven mindset is the ability to understand the capabilities and limitations of data. Ask yourself these questions to measure how well you know this:
- How is this data presented?
- What is input and output data?
- Can I formulate the problem in my mind?
- Is the issue in the field of data science?
Data-driven thinking capacities in business decision-making
Today we are witnessing a sharp increase in the amount of data available; Data obtained with the help of the capabilities of the digital world. But are businesses using the potential of this vast sea? Many businesses still seem to make their decisions not on the basis of data but on the basis of instincts, managers’ ambitions, or distorted or limited data.
These show that data-driven thinking has not yet taken root in these businesses and has not become part of their organizational culture. In order to use the maximum capacity of data, it is necessary to make changes in the decision-making structure of businesses so that the data-driven mindset fits between members.
Familiarity with different types of decisions in business
To use data in decision making, we first need to be familiar with the types of decisions that each organization or business makes during its lifetime. There are three types of decisions:
- Strategic decisions: Every organization during its life sometimes faces decisions that in the path of strategic planning can determine its fate. These decisions have profound implications for all organizations and members, and their frequency is very low. In other words, these decisions are few but fateful.
- Periodic decisions: These decisions occur more frequently and are taken to manage different parts of the organization. Some of these decisions can be automated by artificial intelligence processes and limit the presence of the human agent except when necessary; For example, reviewing inventory and providing a stable supply chain of raw materials are periodic decisions that occur periodically at different times in the factory or store.
- Daily and micro decisions: Decisions that the organization or its members make on a daily basis in the face of micro-issues; For example, offering a discount to a loyal customer or agreeing to an employee’s leave are examples of these decisions. One thing to keep in mind about these decisions: Although these decisions may seem small, their outcome in a large organization can have a long-term impact on the movement and life of the organization.
What effect does the data-driven mindset have on these three categories of decision-making?
As mentioned, the use of data and the development of data-driven culture in different parts of the organization improves and accelerates the decision-making process. The faster and better decisions are made in the organization, the better the organization will function as a system.
For example, in day-to-day decisions, having data-driven systems and algorithms to rank customers helps sellers design and deliver various discount packages. In this case, every decision no longer needs to be referred to higher levels and the speed of decision-making increases.
How to use data to make decisions?
Now that we are familiar with the types of decision making, how can we use the data in the decision making process? In this section, we introduce the data-driven decision-making process:
1. Determining the decision-making framework
Before receiving and reviewing the data, we need to determine the framework and level at which the decision is to be made. This reduces the likelihood of cognitive errors in decision making that result from data analysis.
2. Collecting data
We now find and collect the required data related to the framework. Data engineering plays an important role in this area and provides the required data from among the data sets.
3. Analysis and forecasting
Now that we have the necessary data in place, it is time to analyze the data. Based on the available data, we predict different models and design different decisions. Note that data are always subject to uncertainty and often look back; So it is better to define the decision interval.
Now it is time to make and implement the desired decision. Any decision must be properly communicated to other relevant groups.
Once a decision has been made, we must not forget it. Now we need to collect and analyze the positive and negative results and consequences of decisions based on feedback data to clarify the effectiveness of decisions.
6. Process modification and evolution
After a while we have made the various decisions and received the necessary feedback, we can discover the hidden influential aspects of the data-driven decision-making process and modify it if necessary; For example, reduce the role of human factors in the process or move part of the decision-making process to the lower layers of the organization, or vice versa.
How do we put data-driven thinking into our business?
But how can a data-driven culture and data-driven decision-making be embedded in different members and parts of the organization? To achieve this goal, it is necessary to create some structures and intellectual habits among the members of the organization.
- Determining the decision-making structure in the organizationFirst, we need to get an overview of which parts of our organization and by whom (humans or machines) the decision is made. Then think about how data-driven thinking can improve each segment.
- Expanding a data-driven culture: Data-driven mindset means making the ability to interpret data correctly in the organization a priority. The implementation of this priority should start from the highest levels of the organization and between key managers and leaders. The more the data-driven culture develops in the organization, the greater the possibility of delegating decisions to lower levels. In fact, the various components of the organization are mature enough to make decisions in their field of work.
- Upgrade related skills and abilities: Set standards to improve the skills of those parts of the organization that are facing decision-making. Facilitate the acquisition of these skills by providing learning tools. In general, you need three types of capabilities to develop a data-driven mindset: (1) data engineering, which enables you to collect data; (2) data mining science, which enables the analysis and classification of collected data; and (3) Decision-making intelligence that makes decisions using management skills and social sciences.
- Data transfer structureFor data to be used in the decision-making process, it must be reliable, relevant, reproducible, and provided by an official source; So you need to design a structure for data transfer between different parts. This structure includes databases, communication lines, and so on.
Every organization makes many decisions during its life, from the decisions that determine its destiny to the daily and partial decisions. At all these levels, data improves decision making. To use the capacity of this data, it is necessary to use a data-driven decision-making process in the organization. A prerequisite for using this process is to spread data-driven thinking among members of the organization.