Data analysis is the process of collecting, cleaning, organizing, and analyzing data to discover insights that will influence business decisions. While this process might seem very straightforward, each step actually requires in-depth knowledge in order to make sure that you are able to obtain actionable insights. This means that you need to have a solid framework that you can use to analyze data across all industries, whether you’re analyzing patient claims in the healthcare industry, using user data to create personalized playlists in the music world, or building a tool to help farmers monitor the health of their crops in the agritech space.
This article will actually walk you through the 5 steps of data analysis, covering the basics of each step. This should leave you with a solid roadmap that you can use for the next time you need to analyze data!
Here are the steps we’ll take you through:
The first step in data analysis is to define the business problem that your stakeholder is trying to solve. This part is very important as it will give you an understanding of why you want to analyze any data and what data you should consider collecting. Once you are aware of the business problem, you should ask further questions to get a better idea of which goals you should set for this project. Some sample questions are:
Asking the right questions can have a huge effect on the turnout of your results, which is why business acumen is a very important skill to have. Business acumen ensures that you have enough industry knowledge to understand business problems in depth so that your analysis has a higher chance of success in relation to finding useful insights that stakeholders can use to make their next business decision.
For instance, let's say you work for a marketing analytics firm and they are having issues with increasing their conversion rate on Twitter. Instead of asking “Why aren't we able to increase the amount of customers we have?”, one could reframe the question to “How can we engage with our Twitter base to where they want to purchase our products?”. From there, you will be able to set a business goal (obtain a 5% increase in the Twitter conversion rate) and define which metrics and KPIs (Key Performance Indicators) that will be used to help you measure the progress of the stakeholder’s goal and to test your hypothesis (Twitter conversion rates are low because the content is boring). Once you have a solid list of refined KPIs, it’s time to figure out where that information is located.
After defining the business problem and creating a list of metrics and KPIs to track, you will need to identify the data sources that contain all of the relevant information that you need for analysis. This involves planning and strategizing on where the data is located and how you will need to extract it. For some, all of the data you need might be located inside of a data warehouse which can be extracted via a SQL query. For others, you might need to grab data across applications. For example, perhaps you will need to gather engagement rates from twitter, site conversion data from your google analytics instance, and transaction data from a 3rd party order tool.
Once you have extracted the data from the necessary data sources, it is time to cleanse it so that it will be ready for analysis. The purpose of this is to make sure that you have high quality data that is accurate and consistent. Even the tiniest of errors can have a huge impact on your analysis results. Data cleansing includes:
FYI: Data cleansing can take up to 60-90% of your time on the project. Remember that it’s the stuff that no one wants to do that makes the job so lucrative and important!
Whew, data cleansing is finally over and you can move on to the good stuff: finding insights! The analysis technique you use will differ, depending on your objective. Here are the 4 common types of analytical methods:
A data analyst might pull the data and put it in a data viz tool like Tableau, PowerBI, or Looker. Data Scientists might use prescriptive analytics and plug the data into some machine learning models to predict outcomes in relation to the business problem they are trying to solve.
Once you’re done with your analysis, you’ll create a writeup of your findings/predictions and get ready to share it with your stakeholders.
You’ve done the analysis technique of choice and documented your findings. Now it’s time for the most important part of the process: sharing what you have learned with the stakeholders for your project. This is easier said than done, as you need to make sure to communicate your insights with the stakeholders in a way that they can fully comprehend, otherwise they will not be convinced of why your analysis should have any influence over the next decision that they make.
On top of providing your insights, you might also be responsible for providing suggestions on the next steps that the stakeholders should take, as well as notify them of any gaps that the data might not have accounted for in your discovery. You want to be as clear as possible with your communication as you heavily influence how an organization moves forward!
I highly recommend using these essential steps the next time you need to perform data analysis. Overtime, you might realize that you need to customize these steps based on your own personal way of analyzing data or based on the data infrastructure/tools used within your organization. In case you aren’t able to have this article in front of you, you can use the infographic below as a general outline of the data analysis process for the next time you need it. Once you’re comfortable, this process will become natural to you to where you won’t need to think about which step should be performed next!
