THE THREE STAGES OF DATA ANALYSI S:
The Three Stages of Data Analysis
Jul 14, 2022
These steps and many others fall into three stages of the data analysis process: Raw data, Information, Knowledge.
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These steps and many others fall into three stages of the data analysis process: Raw data, Information, Knowledge.
Want to learn Data Science? Check out the data science course in Hyderabad.
Visit my Profile for More Information
<a href="https://www.learnbay.co/data-science-course/data-science-courses-in-hyderabad/">Data science course in Hyderabad </a>.
Transcript
KNOWLEDGE
The business environment is more competitive than ever, particularly online. Adding value through data analysis to better understand customers and change strategy for quick success is the only way to differentiate your organization. This article discusses the three data analysis phases essential for company success.
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RAW DATA
Any intriguing and important information to your organization is considered raw data. For instance, raw data could be a sales report from a freshly released product or a list every
time a product has been mentioned in forums, social media, or online reviews.
Raw data used to be mostly kept in a company's data warehouse. Still, this approach is no longer the best because it ignores external information (forums, social media, or PR) and restricts your business to internal resources.
INFORMATION
It's time to analyze the raw data after you have it at home. You do this by sorting out the important information that will affect your company from the rest. But hold off on making any choices just yet; you're not done yet. We claim that this step transforms raw data into information because it enriches the data by interpreting, classifying, computing, correcting, and simplifying it. Using the previous example as a guide, we can now divide all of the social network comments into categories like "neutral," "positive," and "negative," as well as order them all according to the area.
KNOWLEDGE
Knowledge, the final stage of data analysis, renders the information acquired understandable. More difficult activities, such as comparing different pieces and finding connections and patterns between them, are part of this phase. You prepare the data here so that you may start making decisions.
Continuing with our example, we can aggregate all of the sales data and local social network user comments in this phase and examine the success of every region. We can now pinpoint important problems like the proportion of critical comments in California or an exceptionally low number of critical comments in Florida. Because of this, the decision-makers will learn when we share this information with them that we have a local competition in California, so we should develop a special strategy there. Because we didn't do enough marketing in Florida, many people are unaware of our product.
THANK YOU
If you are looking for Data Science Course Visit Us At
The business environment is more competitive than ever, particularly online. Adding value through data analysis to better understand customers and change strategy for quick success is the only way to differentiate your organization. This article discusses the three data analysis phases essential for company success.
ABOUT US
RAW DATA
Any intriguing and important information to your organization is considered raw data. For instance, raw data could be a sales report from a freshly released product or a list every
time a product has been mentioned in forums, social media, or online reviews.
Raw data used to be mostly kept in a company's data warehouse. Still, this approach is no longer the best because it ignores external information (forums, social media, or PR) and restricts your business to internal resources.
INFORMATION
It's time to analyze the raw data after you have it at home. You do this by sorting out the important information that will affect your company from the rest. But hold off on making any choices just yet; you're not done yet. We claim that this step transforms raw data into information because it enriches the data by interpreting, classifying, computing, correcting, and simplifying it. Using the previous example as a guide, we can now divide all of the social network comments into categories like "neutral," "positive," and "negative," as well as order them all according to the area.
KNOWLEDGE
Knowledge, the final stage of data analysis, renders the information acquired understandable. More difficult activities, such as comparing different pieces and finding connections and patterns between them, are part of this phase. You prepare the data here so that you may start making decisions.
Continuing with our example, we can aggregate all of the sales data and local social network user comments in this phase and examine the success of every region. We can now pinpoint important problems like the proportion of critical comments in California or an exceptionally low number of critical comments in Florida. Because of this, the decision-makers will learn when we share this information with them that we have a local competition in California, so we should develop a special strategy there. Because we didn't do enough marketing in Florida, many people are unaware of our product.
THANK YOU
If you are looking for Data Science Course Visit Us At
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