1. Data in decision making
Becoming data-driven is the ultimate goal for many individuals and organizations. Understanding the role of data in decision making is essential for becoming better data practitioners.
2. What is decision-making
People make decisions everyday. For adults, it is estimated that they face about 35000 decisions each and every day. What to eat, what to wear, how to spend your free time, what major they should pursue, where should they live. Minor and major choices mixed in together each with its own set of consequences and expectations.
Decision making is simply the process we all undergo to make the right choices at the right time. For many decisions, employing data can help make a tough choice clearer.
3. From data to decision
Data-driven decision making is a five-step process, each step revealing more to ultimately drive a well informed decision. The process begins with asking a question, gathering the correct data, preparing the data, conducting analysis, and finally making the right decision.
It is important to note that this process is repeating in nature. As we make decisions, the results of those decisions can fuel future decision making.
Let's explore each step in detail.
4. Asking the right question
The journey of a data-driven process starts with identifying the question you are looking to answer. This may sound easy, but it is the hardest part of the entire data-driven decision making process.
A good question, will make it clear what you are trying to answer and prevent you from creeping into other areas. Taking a little extra time to clearly define your question will ensure your success through the rest of the process.
5. Collecting data
With your focused question in mind, the search begins for the right data to answer this question. Data can often live in multiple locations and in multiple forms so being deliberate with where you source data is important.
Thinking ahead to your analysis can also payoff greatly. For example, if you are deciding between two different versions of your data, one in its raw form and one summarized by month, knowing which is most valuable for your analysis can reduce the amount of cleaning and prep you need to do in the next step.
6. Preparing data
Preparing data can mean many things. In some instances it is converting messy or low quality data into higher quality data through skillful manipulations. In others it is simply arranging the data into whatever expected format you need it in to enable your analysis.
Many types of analysis have very specific requirements for how data should be arranged and aligned. A cleaned dataset will be ready for analysis without any additional effort or outstanding concerns. In some cases the data preparation phase can be the most cumbersome taking up to 80% of the overall time for the entire decision making process.
7. Analyzing data
Analyzing data is next. This step is critical because it is what transforms our data into something we can make decisions with.
Data analysis tools like Python, R, Tableau, Power BI, Excel, Google Sheets allow us to perform many different kinds of analysis to find insights from data. The technical detail of these tools is outside the scope of this course. I recommend checking other DataCamp courses for a thorough understanding of these tools and techniques.
8. Making decisions
The final step is ultimately interpreting the results and making a decision. Armed with our analysis we will be able to balance the outcome with our knowledge of broader subject matter to arrive at a data-driven decision. It is vital to recognize that the outcome of the analysis is only part of being data-driven, our personal experiences and knowledge also help drive the decision making and blending these two together we can arrive at a decision that is much more powerful than making it with gut feeling alone. This whole process is also iterative in nature. As you make decisions and observe their outcome you are better equipped to make changes and improvements to your decision making process.
9. Let's practice!
Let's do some practice to become more data-driven.