1. Data-driven decision making
Previously, we talked about why getting from data to insights is valuable. Now, we will discuss how to get the most value out of data through data-driven decision making.
2. Why data-driven decisions matter
Data-driven decision making can be defined as follows: "It is the process of using data to make an informed decision about a specific problem and acting upon it."
It can be used for optimizing performance, gaining a better understanding, protecting against risks, or determining the best course of action.
3. Misconceptions about data-driven decision making
The importance of data-driven decision making is the topic of many articles with headlines such as these. Unfortunately, this has also led to some misconceptions:
Data-driven decision making is not just for large organizations, you can start small.
It is not just the responsibility of the data team; in fact, by including people with different backgrounds, the data-driven process will be enriched.
It is not the only or always the best option. Data-driven decision making is a strategy, with its own advantages and disadvantages.
4. Data-driven process
No matter what type of data-driven project you undertake, there are 5 main steps that underpin every data-driven process.
The first -and very important- step is defining a problem statement, which will guide the rest of the process. The second step is to collect the necessary data. The third step is to perform data analysis.
The last two steps are about communicating the results, and of course taking action on our newly gained insights while reflecting back on the process.
5. Problem statement
Effective data-driven decision making is driven by a well-crafted problem statement. Because of its importance, we'll go over this in more detail.
Simply put, the problem statement answers the question: 'What is the problem you want to solve?'
It acts as a guide to which data to collect, which analysis technique is appropriate, how the results can be used. It can refer to an issue that needs to be addressed, impact that needs to be made, or a question that needs answering.
Typical problem categories are as follows: describing the state of an organization or process, diagnosing causes, detecting anomalies or predicting events.
6. How to define a problem
There are three guiding questions that can be used to craft a problem statement: 'What is the current situation?' 'What do we need to know?'' and 'Where do we want to be?'
A good problem statement has the following characteristics: clearly defined - be specific; actionable - specific action should be possible based on the eventual answer; and realistic - possible to solve with the available resources.
It can also help to use a specific question to start from.
7. Example: customer satisfaction
For example, imagine you are part of a customer service team and need to advice management on customer satisfaction.
The starting question could be: "How can we improve customer satisfaction?"
From there, the problem statement can be defined as follows: "Customer satisfaction is currently under our target. We need to know the main reasons for a negative score so we can take measures to improve customer satisfaction."
8. Example: student performance
For the next example, you are working for a university and are tasked with monitoring student performance.
Your starting question could be: "Why are average math scores of students in decline for the last three years?"
Starting from that we can craft the following problem statement: "The last three years math scores of students have declined. We need to know what the main causes are of this decline so we can take measures to counter it."
9. Let's practice!
Over to you!