How to make decisions based on your data
What is data-driven decision-making?
Let’s start off with the definition: “Data-driven decision-making (or also DDDM) is the process of making organizational decisions based on actual data and not just intuition or observations.”
Data-driven decision-making can come in many different forms. For example, a company can:
- Collect satisfaction scores about customer satisfaction (CSAT).
- Launch products in a test market to see and understand how the product might perform in the market.
- Analyze the demographic data to create new business opportunities or determine new threats.
How data can be used in the decision-making process depends on different factors, for example your business goals and the type of data you have. If you are familiar with the concept “garbage in, garbage out” (or GIGO) you know that the quality of your data is the foundation of it all. With GIGO we refer to the fact that the quality of your output depends on the quality of your input.
The process of data-driven decision making
To transform your business towards more data-driven decision-making you’ll need to follow 6 steps:
1. Ask
It’s impossible to solve a problem if you don’t know what it is. So before you start collecting data, you should start by identifying the business questions you need answers in order to meet your business goals. By determining the exact questions you need to know to inform your strategy, you can streamline the data collection process and avoid wasting resources.
These are some things to consider:
- Define the problem you’re trying to solve
- Make sure you fully understand the stakeholder’s expectations
- Focus on the actual problem and avoid any distractions
- Collaborate with stakeholders and keep an open line of communication
- Take a step back and see the whole situation in context
2. Prepare
You will decide what data you need to collect in order to answer your questions and how to organize it so that it is useful. You might use your business task to decide:
- What metrics to measure
- Locate data in your database
- Create security measures to protect that data
To ensure that you’ll have the correct data you’ll need to ask yourself what problem you need to solve. Once you have it, you’ll need to think about where you can find the data to help you. You’ll probably need to extract data from different sources, such as different databases, feedback forms, or even social media.
Connecting different sources may sound easy, but finding common variables in each data set can be a very difficult problem. While it’s easy to settle for the immediate goal of using the data only for its current purpose, it’s wise to determine if this data can be used for more projects in the future. In this case, you should try to develop a strategy to present the data in a way that is accessible in other scenarios.
3. Process
Clean data is the best data! So before you start analyzing your data, you’ll need to clean it. Remember GIGO, the cleaner your data the better your output. You’ll need to get rid of any possible errors, inaccuracies, or inconsistencies. This might mean:
- Using functions to find incorrectly entered data
- Using functions to check for extra spaces
- Removing repeated entries
- Determining if your data is biased
A data analyst’s job consists surprisingly of 80% of data cleaning and organizing. This so-called “80/20 rule” shows the importance of clean and structured data before you can start analyzing. Tip: Before cleaning your dataset, create a duplicate. In this case, should something go wrong you ‘d always have a backup.
4. Analyze
After fully cleaning the data, you may start analyzing it! You will now begin to create models to test your data and provide answers to the business questions you had earlier in the process. Here you’ll combine data from multiple sources, perform calculations, and create tables with your results. Once you’ve analyzed your data you’ll need to present your findings. There are three different ways for this:
- Descriptive Information: Just the facts.
- Inferential Information: The facts, plus an interpretation of what those facts indicate in the context of a particular project.
- Predictive Information: An inference based upon facts and advice for further action based on your reasoning.
5. Visualize
Data visualization is an important step in the data analysis process. Not everyone is well informed with data and a table of numbers is essentially meaningless without a context. Therefore, data visualizations help us to represent the data in an easy and understandable way for everyone. It is important to be simple and to the point. A good rule of thumb is that anyone should be able to tell you what the graphic is about in less than 5 seconds. To help you with this you can use chart titles, color codes that everyone understands (green for good, red for bad), etc.
6. Act
The final step in data-driven decision-making is concluding. Ask yourself, “What new information did you learn after your analysis?”
It is important to be completely transparent about your findings so that the stakeholders can make the right decision. You can also give some recommendations yourself based on your results. The conclusion you draw will ultimately help your organization to make a better informed decision. It is also important to note that these findings can be useless if not presented effectively. Therefore, a data analyst should also master the art of storytelling and communicate with stakeholders as effectively as possible.
The conclusions drawn from your analysis will ultimately help your organization make decisions that are more informed and drive strategy moving forward.
To conclude, we see that the role of a Data analyst needs to imply skills in the art of data storytelling to communicate their findings with key stakeholders as effectively as possible after their analysis.
If you need help in analyzing your companies’ potential, don’t hesitate to contact HeadMind Partners who will support you in the best ways!
Sources:
Google Data Analytics Certification
https://online.hbs.edu/blog/post/data-driven-decision-makinghttps://www.techtarget.com/searchsoftwarequality/definition/garbage-in-garbage-out
https://www.techtarget.com/searchsoftwarequality/definition/garbage-in-garbage-out