Do you still discover it challenging to make efficient use of data? Applying a set of best practices for data science to each project will ensure constant improvements and get the best outcome from collected data. However, like any scientific approach, data analysis requires to follow step – by – step process. Each step needs specific skills and knows – how. To extract valuable insight from it is crucial to understand the complete process.
Hence, we explore significant steps of the data analysis process for your better understanding. In this post, we will define the goal, collect data and bring out the analysis. When you get complete information, then you have a much better understanding of the data analytics career.
Phases of Data Analysis Process
We elaborate on each step and how data analytics professionals can achieve it using a technology stack.
1. Ask Relevant Questions
First step in the data analysis process is to ask relevant questions. The data analyst is provided a question and business task. So, the data analysts must comprehend the task and fulfill stakeholder expectations for the solution. Data analysts must ask various questions to find the correct answer to the issue. For any challenge, the data analyst must discover the root cause of the problem and avoid any distractions.
Therefore, initiating with a clear objective in mind is a significant step in the data analysis process. However, determining the business issues, you must resolve and setting a common goal will be easier.
2. Source and Collect the Data
Second step in data analysis is to source and collect data. However, the purpose is to find relevant data to solve the problem or provide supporting analytical solutions to the stated objective. This step requires reviewing existing data sources and discovering if it is necessary to collect new data. It includes any tasks to get data in – hand, like querying databases, scraping data, and submitting a request to other departments.
Also, it is essential to know the sources from where the data is extracted; it is calculated data or only recorded data. The timing of data is necessary.
3. Process and Clean the Data
After collecting and combing data from various sources, it is time to roll up your sleeves and dive into it. Here you are required to spend time polishing the data, which ensures it is in top –shape. In data cleaning process it converts raw data into useful ones. It includes removing incorrect data and evaluating its incompleteness and inconsistencies. Data cleaning is crucial in data analysis because data accuracy depends on the data quality.
You might clean out spaces between letters and symbols. Although these may seem minor issues, they can severely impact your results. Even the most minor inaccuracies could cause you to lose important information. This is because raw data can be challenging to use in its current form. It will often have flaws, such as missing values.
4. Data Visualization
We need to visualize the data after creating or building the data sets. This will allow us to make our Hypotheses or Insights and help us explore and evaluate the data. Tableau/SaaS is a data visualization tool enabling you to visualize large data columns from structured and unstructured databases quickly. It also makes finding insights and meaningful patterns in the data accessible.
5. Statical Analysis
Most critical aspect of data analysis is summarizing the data and explaining how it relates to the real world. Static analysis can also identify patterns or trends in predictive analytics. This helps to make business decisions and determines the statistical significance of the data set.
6. Deploy Models
After selecting, creating, and turning models, the next step is the deployment model. The model deployment aims to produce outputs that lead to a decision or action. Usually, model prediction and other variables are inputs to an optimization issue. However, the solution to this problem is to produce raw data converted into finished data by experts.
Here are a few examples of the decisions that look after evaluating and translating model outputs:
· Change in policy
· Increase in price
· Launch the promotion
· Alteration in mixture
7. Evaluate and Validate
Monitoring and validation is the final step in data analysis. It is essential to check the results after making decisions and allow a brief time. You can monitor and validate results in many ways. For example, you can create summary reports or charts showing actual versus targets, average sales, or revenue over time.
It is essential to ensure that results meet expectations. Reexamine assumptions and look for any errors in data feeds. Also, be sure to check for unexpected changes in data attributes. Check to see if there was an unexpected change in the market.
After the initial system has been tested, monitoring the procedure to identify changes in the model’s performance or source data is essential. As time passes, a model can become less efficient and require updating with new training data.
You can apply the seven data analysis steps to new and existing use cases. These steps are intended to be implemented, automated as much as possible, and continuously improved. Focus on data analysis and understanding the process to get the best out of it.