Is There a Specific Process for Data Analysis?<\/h2>\n\n\n\n![\"data](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2023\/03\/data-analysis-process.png\")
<\/figure>\n\n\n\nThere is indeed a specific process for data analysis. Suppose you are looking to create the best recipe for pizza dough. You could frame your problem as a lack of knowledge\u2014not having a sufficient pizza dough recipe. <\/p>\n\n\n\n
What data could help you solve this problem? One way would be to comb through the plethora of online recipes available. You could then sort this data, filtering out recipes with low reviews or comments noting flaws in the recipe. Then, once you\u2019ve collated the best recipes, you can begin to analyze them. What are the commonalities that emerge? Maybe you find that the best recipe depends on the style of pizza you want to make and that it\u2019s best to group certain recipes together. The data analysis process won\u2019t create the perfect pizza dough recipe for you, but it can get you headed in the right direction. <\/p>\n\n\n\n
The Data Analysis Process<\/h2>\n\n\n\n
Let\u2019s take a more in-depth look into the data science process:<\/p>\n\n\n\n
Establish the Purpose of the Process<\/h3>\n\n\n\n
This is arguably the most critical step, as it can set you up for success. The purpose is often defined as a business question or problem statement related to your organization\u2019s goals. Examples include:<\/p>\n\n\n\n
\n- Would customers respond positively to the launch of X product?<\/li>\n\n\n\n
- What are some ways to reduce employee attrition?<\/li>\n\n\n\n
- Will incorporating AI tools reduce production costs?<\/li>\n<\/ul>\n\n\n\n
Data Collection<\/h3>\n\n\n\n
Once you\u2019ve defined the problem, then you can start collecting data. Broadly speaking, there are three different categories of data, and the ones you use will depend on the nature of your problem. Most data analysis problems require a combination of the three. <\/p>\n\n\n\n
First-party data is data that your own organization generates. Oftentimes, this is data about previous customer interactions that can be used to make accurate predictions about your customers\u2019 behavior in the future. <\/p>\n\n\n\n
You could also use second-party data\u2014data that\u2019s generated by external sources, but is about your company specifically. This can include what customers are saying on social media platforms or review websites.<\/p>\n\n\n\n
Third-party data comes from groups like think tanks and government sources and is more concerned with the nature of your customer base, rather than a specific interaction that a customer has had with your company. <\/p>\n\n\n\n
Data Cleaning<\/h3>\n\n\n\n
Not all the data you collect will be useful or accurate, and you\u2019ll need to discard the data points that are irrelevant, duplicated, inconsistent, or outdated.<\/p>\n\n\n\n
This is called data cleaning<\/a>. When combining multiple sources of data, you\u2019ll likely wind up with duplicates and outliers. And when you\u2019re dealing with millions of data points, as is often the case with data analysis, you can\u2019t comb through each piece of data on your own to find the duplicates or outliers. Data analysts<\/a> estimate that the time spent cleaning data consumes about 70-90% of the data analysis process. <\/p>\n\n\n\nAt this stage, you can also do an exploratory analysis, which is an initial and cursory data analysis. Exploratory data analysis<\/a> will also assist with identifying other data points you may need.<\/p>\n\n\n\nData Processing<\/h3>\n\n\n\n
Once you have all the relevant data, you can begin to process it. This entails organizing the data, sorting the data into relevant categories, and labeling them for easy organization. Now the data is prepped for analysis.<\/p>\n\n\n\n
Data Analysis<\/h3>\n\n\n\n
Data analysis can be done in numerous ways. One way is to use algorithms and mathematical models to manipulate data variables, which helps extract relevant information and valuable insights that tie into the problem defined in the first step. <\/p>\n\n\n\n
Types of Data Analysis<\/h4>\n\n\n\n
Let\u2019s look at the various data analysis techniques<\/a>, which can be used in combination, depending on your problem.<\/p>\n\n\n\n![\"Types](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2023\/03\/types-of-data-analysis.png\")
<\/figure>\n\n\n\nDescriptive Analysis<\/h5>\n\n\n\n
As the name suggests, descriptive analysis describes or summarizes the data and its characteristics. It doesn\u2019t go beyond explaining what has happened. You use this type of data analysis to deliver a narrative of what has occurred. Descriptive statistics and analysis present scattered data into digestible pointers. You can also do a part of this at the stage of exploratory data analysis.<\/p>\n\n\n\n
Diagnostic Analysis<\/h5>\n\n\n\n
With diagnostic analysis, you begin to focus on the \u201cwhy,\u201d and diagnose why something is occurring. At this stage, you are not looking for solutions or predictions. The goal is to understand the factors that are contributing to the problem. You use this technique when you want to go into issue identification mode.<\/p>\n\n\n\n
Predictive Analysis<\/h5>\n\n\n\n
Here\u2019s where you start generating forecasts based on your data. Data analysts perform predictive analysis<\/a> when they want to establish a situation in the future. This prediction helps stakeholders gauge business performance.<\/p>\n\n\n\nPrescriptive Analysis<\/h5>\n\n\n\n
This kind of analysis brings together all of these data analysis techniques to offer recommendations. These form the basis of data-driven decisions.<\/p>\n\n\n\n
Inferential Analysis<\/h5>\n\n\n\n
With this technique, you derive conclusions based on the data you have collected and analyzed, such as, \u201clack of employee training is a cause of employee attrition\u201d or \u201cemployee attrition affects customer satisfaction.\u201d<\/p>\n\n\n\n\n![\"YouTube](\"https:\/\/img.youtube.com\/vi\/0Xp3bnMt-TQ\/sddefault.jpg\")
<\/figure>\n\n\n\nThere is indeed a specific process for data analysis. Suppose you are looking to create the best recipe for pizza dough. You could frame your problem as a lack of knowledge\u2014not having a sufficient pizza dough recipe. <\/p>\n\n\n\n
What data could help you solve this problem? One way would be to comb through the plethora of online recipes available. You could then sort this data, filtering out recipes with low reviews or comments noting flaws in the recipe. Then, once you\u2019ve collated the best recipes, you can begin to analyze them. What are the commonalities that emerge? Maybe you find that the best recipe depends on the style of pizza you want to make and that it\u2019s best to group certain recipes together. The data analysis process won\u2019t create the perfect pizza dough recipe for you, but it can get you headed in the right direction. <\/p>\n\n\n\n
The Data Analysis Process<\/h2>\n\n\n\n
Let\u2019s take a more in-depth look into the data science process:<\/p>\n\n\n\n
Establish the Purpose of the Process<\/h3>\n\n\n\n
This is arguably the most critical step, as it can set you up for success. The purpose is often defined as a business question or problem statement related to your organization\u2019s goals. Examples include:<\/p>\n\n\n\n
- \n
- Would customers respond positively to the launch of X product?<\/li>\n\n\n\n
- What are some ways to reduce employee attrition?<\/li>\n\n\n\n
- Will incorporating AI tools reduce production costs?<\/li>\n<\/ul>\n\n\n\n
Data Collection<\/h3>\n\n\n\n
Once you\u2019ve defined the problem, then you can start collecting data. Broadly speaking, there are three different categories of data, and the ones you use will depend on the nature of your problem. Most data analysis problems require a combination of the three. <\/p>\n\n\n\n
First-party data is data that your own organization generates. Oftentimes, this is data about previous customer interactions that can be used to make accurate predictions about your customers\u2019 behavior in the future. <\/p>\n\n\n\n
You could also use second-party data\u2014data that\u2019s generated by external sources, but is about your company specifically. This can include what customers are saying on social media platforms or review websites.<\/p>\n\n\n\n
Third-party data comes from groups like think tanks and government sources and is more concerned with the nature of your customer base, rather than a specific interaction that a customer has had with your company. <\/p>\n\n\n\n
Data Cleaning<\/h3>\n\n\n\n
Not all the data you collect will be useful or accurate, and you\u2019ll need to discard the data points that are irrelevant, duplicated, inconsistent, or outdated.<\/p>\n\n\n\n
This is called data cleaning<\/a>. When combining multiple sources of data, you\u2019ll likely wind up with duplicates and outliers. And when you\u2019re dealing with millions of data points, as is often the case with data analysis, you can\u2019t comb through each piece of data on your own to find the duplicates or outliers. Data analysts<\/a> estimate that the time spent cleaning data consumes about 70-90% of the data analysis process. <\/p>\n\n\n\n
At this stage, you can also do an exploratory analysis, which is an initial and cursory data analysis. Exploratory data analysis<\/a> will also assist with identifying other data points you may need.<\/p>\n\n\n\n
Data Processing<\/h3>\n\n\n\n
Once you have all the relevant data, you can begin to process it. This entails organizing the data, sorting the data into relevant categories, and labeling them for easy organization. Now the data is prepped for analysis.<\/p>\n\n\n\n
Data Analysis<\/h3>\n\n\n\n
Data analysis can be done in numerous ways. One way is to use algorithms and mathematical models to manipulate data variables, which helps extract relevant information and valuable insights that tie into the problem defined in the first step. <\/p>\n\n\n\n
Types of Data Analysis<\/h4>\n\n\n\n
Let\u2019s look at the various data analysis techniques<\/a>, which can be used in combination, depending on your problem.<\/p>\n\n\n\n
<\/figure>\n\n\n\nDescriptive Analysis<\/h5>\n\n\n\n
As the name suggests, descriptive analysis describes or summarizes the data and its characteristics. It doesn\u2019t go beyond explaining what has happened. You use this type of data analysis to deliver a narrative of what has occurred. Descriptive statistics and analysis present scattered data into digestible pointers. You can also do a part of this at the stage of exploratory data analysis.<\/p>\n\n\n\n
Diagnostic Analysis<\/h5>\n\n\n\n
With diagnostic analysis, you begin to focus on the \u201cwhy,\u201d and diagnose why something is occurring. At this stage, you are not looking for solutions or predictions. The goal is to understand the factors that are contributing to the problem. You use this technique when you want to go into issue identification mode.<\/p>\n\n\n\n
Predictive Analysis<\/h5>\n\n\n\n
Here\u2019s where you start generating forecasts based on your data. Data analysts perform predictive analysis<\/a> when they want to establish a situation in the future. This prediction helps stakeholders gauge business performance.<\/p>\n\n\n\n
Prescriptive Analysis<\/h5>\n\n\n\n
This kind of analysis brings together all of these data analysis techniques to offer recommendations. These form the basis of data-driven decisions.<\/p>\n\n\n\n
Inferential Analysis<\/h5>\n\n\n\n
With this technique, you derive conclusions based on the data you have collected and analyzed, such as, \u201clack of employee training is a cause of employee attrition\u201d or \u201cemployee attrition affects customer satisfaction.\u201d<\/p>\n\n\n\n
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