Here is an overview of some of the techniques you will come across in data analysis:
● Data visualization
Data visualization is about presentation. You are already aware of most of the tools that are used in data visualizations, such as pivot tables, pie charts, and other statistical tools. Other than resentability, data visualization makes large sets of data easy to understand. Instead of reading tables, for example, you can see the data transposed onto a color-coded pie chart. We are visual creatures. Visual optics last longer in our minds than information we read. At a glance, you can understand what the information is about. Summaries are faster and easier through data visualization than reading raw data. One of the strengths of data visualization is that it helps in speeding up the decision-making process.
● Business intelligence
Business intelligence is a process where data is converted into actionable information in accordance with the end user’s strategic objectives. While most of the raw data might be difficult to understand or work with, through business intelligence, this data eventually makes sense. Business intelligence techniques help in determining trends, examining them and deducing useful insights.
Many companies use this to help in making decisions about their pricing and product placement strategies. This data is also helpful in identifying new markets for their products and services, and analyzing the sustainability of the said markets. In the long run, this information helps the company come up with specific strategies that help them thrive in each market segment.
● Data mining
Data mining involves studying large sets of data to determine the occurrence of patterns. Patterns help analysts identify trends, and make decisions based on their discoveries. Some of the methods used in data mining include machine learning, artificial intelligence, using databases and statistical computations.
The end result in data mining is the transformation of primitive raw data into credible information that can be used to make informed business decisions. Other than decision making, data mining can also help in finding out the existence and nature of dependency or abnormalities across different sets of data.
It is also useful in cluster analysis, a procedure where the analyst studies a given set of data to identify the presence of specific data groups. Data mining can be used alongside machine learning to help in understanding consumer behavior. Consumer tastes and preferences are traditionally dynamic.
Because of this, changes take place randomly. Given the popularity of ecommerce today, the dynamic shift in consumer tastes and preferences is more volatile than ever.
Through data mining, analysts can collect lots of information about consumer actions on their websites, and make an accurate or near-accurate prediction of the purchase traits and frequencies. Such information is useful to marketing departments and other allied sectors in the business, to help
them create appropriate promotional content to attract and retain more customers.
Marketing savvy experts usually create niches out of a larger market demographic. The same concept applies to data mining. Through data mining, it is possible to identify groups of data that were previously unidentified. Studying such data groups is important because it allows the analyst to experiment with undefined stimuli and in the process, probably discover new frontiers for the marketing departments.
Other than previously unidentified data, data mining is useful when dealing with data sets that are clearly defined. This also involves some element of machine learning. One of the best examples of this is the modern email system. Each mail provider has systems in place that determine spam and non-spam messages.
They are then filtered to the right inboxes.
● Text analysis
Most people are unaware of text analysis, especially since it is often viewed as a sub-group of other data analysis methods. Text analysis is basically reading messages to determine useful information from the content available. Beyond reading texts, the information is processed and passed through specific algorithms to help in decision making.
The nature and process of text analysis depends on the organization and their needs assessment. Information is obtained from different databases or file systems and processed through linguistic analysts. From there, it is easier to determine patterns in the information available, by looking at the frequencies of specific keywords. Pattern recognition algorithms usually look for specific targets like email addresses, street names, geographical locations, or phone numbers.
Text analysis is commonly applied in marketing, when companies crawl the websites of their competitors to understand how they run their business. They look for specific target words to help them understand why the competitor is performing better or worse than they are. This method can deliver competitor keywords and phrases, which the analyst can use to deduce a counter-mechanism
for their company.
● Data visualization
Data visualization is about presentation. You are already aware of most of the tools that are used in data visualizations, such as pivot tables, pie charts, and other statistical tools. Other than resentability, data visualization makes large sets of data easy to understand. Instead of reading tables, for example, you can see the data transposed onto a color-coded pie chart. We are visual creatures. Visual optics last longer in our minds than information we read. At a glance, you can understand what the information is about. Summaries are faster and easier through data visualization than reading raw data. One of the strengths of data visualization is that it helps in speeding up the decision-making process.
● Business intelligence
Business intelligence is a process where data is converted into actionable information in accordance with the end user’s strategic objectives. While most of the raw data might be difficult to understand or work with, through business intelligence, this data eventually makes sense. Business intelligence techniques help in determining trends, examining them and deducing useful insights.
Many companies use this to help in making decisions about their pricing and product placement strategies. This data is also helpful in identifying new markets for their products and services, and analyzing the sustainability of the said markets. In the long run, this information helps the company come up with specific strategies that help them thrive in each market segment.
● Data mining
Data mining involves studying large sets of data to determine the occurrence of patterns. Patterns help analysts identify trends, and make decisions based on their discoveries. Some of the methods used in data mining include machine learning, artificial intelligence, using databases and statistical computations.
The end result in data mining is the transformation of primitive raw data into credible information that can be used to make informed business decisions. Other than decision making, data mining can also help in finding out the existence and nature of dependency or abnormalities across different sets of data.
It is also useful in cluster analysis, a procedure where the analyst studies a given set of data to identify the presence of specific data groups. Data mining can be used alongside machine learning to help in understanding consumer behavior. Consumer tastes and preferences are traditionally dynamic.
Because of this, changes take place randomly. Given the popularity of ecommerce today, the dynamic shift in consumer tastes and preferences is more volatile than ever.
Through data mining, analysts can collect lots of information about consumer actions on their websites, and make an accurate or near-accurate prediction of the purchase traits and frequencies. Such information is useful to marketing departments and other allied sectors in the business, to help
them create appropriate promotional content to attract and retain more customers.
Marketing savvy experts usually create niches out of a larger market demographic. The same concept applies to data mining. Through data mining, it is possible to identify groups of data that were previously unidentified. Studying such data groups is important because it allows the analyst to experiment with undefined stimuli and in the process, probably discover new frontiers for the marketing departments.
Other than previously unidentified data, data mining is useful when dealing with data sets that are clearly defined. This also involves some element of machine learning. One of the best examples of this is the modern email system. Each mail provider has systems in place that determine spam and non-spam messages.
They are then filtered to the right inboxes.
● Text analysis
Most people are unaware of text analysis, especially since it is often viewed as a sub-group of other data analysis methods. Text analysis is basically reading messages to determine useful information from the content available. Beyond reading texts, the information is processed and passed through specific algorithms to help in decision making.
The nature and process of text analysis depends on the organization and their needs assessment. Information is obtained from different databases or file systems and processed through linguistic analysts. From there, it is easier to determine patterns in the information available, by looking at the frequencies of specific keywords. Pattern recognition algorithms usually look for specific targets like email addresses, street names, geographical locations, or phone numbers.
Text analysis is commonly applied in marketing, when companies crawl the websites of their competitors to understand how they run their business. They look for specific target words to help them understand why the competitor is performing better or worse than they are. This method can deliver competitor keywords and phrases, which the analyst can use to deduce a counter-mechanism
for their company.
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