Phases of Data Analysis Process
Data Analytics involves mainly Six important phases that are
1) Ask
The first phase of the data analysis process is asking the right questions. It’s impossible to solve a problem if you don’t know what it is. The analyst must be able to ask different questions to find the right solution. The analyst has to find the root cause of the problem in order to fully understand the problem.
· Ask effective question
· 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
The second phase is to prepare the data. Preparing your data means collecting or using the data relevant to the problem you are trying to solve. The data has collected from various sources like internal, external, third-party sources. The common sources from where the data is collected are Interviews, Surveys, Feedback, Questionnaires, Forms.
· Choose data sources
· Locate data in your database
· Understand how data is generated and collected
· Make sure data is unbiased and credible
· Create security measures to protect that data
3) Process
The third phase is to process the data. After the data is collected from multiple sources, it is time to clean the data. If data is free from misspellings, redundancies, and irrelevance it considers as clean data. You have to clean the data so that the data is consistent and will not affect the credibility of the analysis.
- Removing repeated entries
- Checking as much as possible for bias in the data
- Maintain data integrity
- Verify and report a cleaning result
4) Analyze
The fourth phase is to analyze the data. The primary goal in this phase is to find the relationships, trends, and patterns that will help you solve your business problem more accurately. In this phase we perform calculations, combine data from multiple sources. The tools used for performing calculations are Excel or SQL, Programming languages like Python or R.
· Use tools to format and transform data
· Sort and filter data
· Identify patterns and draw conclusions
· Make data-driven decisions
5) Share
The fifth phase is to share your data findings. The data now transformed has to be made into a visual (chart, graph). The reason for making data visualizations is that there might be people, mostly stakeholders that are non-technical. Visualizations are made for a simple understanding of complex data. Tableau, Power BI, Excel, Looker are some tools that are used to make visualization. Sharing the insight with the team members and stakeholders will help in making better decisions.
· Bring data to life
· Create effective visuals
· Make more informed decisions
· Lead to stronger outcomes
· Successfully communicate your findings
6) Act
The sixth phase of the data is to act. In this phase, we will use everything we have learned from our analysis and act upon it. You will provide recommendations to the stakeholder on how to solve the business problem and help them make a good decision.
· Make decisions
· Solve problems
· Revealing gaps and opportunities
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