Data Analysis Methods

The scope of data has become so wide that almost every IT, Industrial, Service and Manufacturing company relies on it today. Companies spend millions everyday on gathering data from multiple sources. But what is the use of all those databases and excel workbooks if you are not able to get any fruitful results out of it?

Almost 73% of data which companies collect eventually end up in trash bins, or stay ideal without any use [Source:]. While firms can utilize this crucial data to improve their sales revenue, productivity, quality and many other things, they most oftenly ‘waste’ it. This happens mainly because of lack of expertise and lack of employees skilled in data analysis courses, who can put this data to its actual benefit.

Putting data to better use helps in getting market insights, customer preferences, and eases operations and strategy making parts. Sorting data and filtering it on the basis of various parameters helps in deriving at outputs which could not have been predicted otherwise. Due to this, there is also an increasing demand for Data Analysts, Data Mining Specialists, Data Scientists, and Data Engineers who have the skills to analyze the data.

These professionals employ different data analysis methods to release their outcomes. Let’s discuss in detail about what exactly data analysis is and how it can be performed.

What is Data Analysis?

  • Data Analysis is a process involving collecting, cleaning, altering, and processing raw data into relevant information that can help businesses make informed decisions.
  • It helps businesses reduce the risks by providing useful insights and statistics in the form of tables, charts, images, graphs and various other visualization forms.
  • It plays a crucial role in processing big data and converting it into useful information.

Why is Data Analysis Important?

Data analysis is used in various industries like transportation, customer interaction, risk & fraud detection, city planning, healthcare, digital advertisement, web search, etc. Data generation is a continuous process and is generated through various sources. The business could gain various benefits through the process of data analysis, some of which are listed below.

  • Data Analysis helps to forecast trends and thus make better decisions.
  • It enhances business performance and competitiveness in the market.
  • It also maximizes sales by creating better strategies for marketing effectiveness.
  • Data Analysis streamlines the complete operational processes.
  • It creates a better and enriching customer experience.
  • It drives business agility, lowers costs and also helps in reducing wastage.

Not just these, Data Analysis Methods also help in raising the overall quality standards.

What are the Different Data Analysis Methods?

There are various methods used for data analysis, majorly based on 2 core areas of research: Quantitative Methods and Qualitative Methods. Here, we have discussed the seven major data analysis methods in detail.

Cluster Analysis

  • It is a method of grouping a set of data with similar elements.
  • It is used to find hidden patterns in the data. It also provides additional context to a trend or dataset.
  • This method helps group the customers based on demographics, purchasing behavior, monetary value, or other similar factors.
  • Cluster Analysis will help the marketers analyze each customer separately and give them the best-personalized experience based on their needs.

Cohort Analysis

  • It is a method that uses historical data to compare a determined segment of user behavior which could be grouped with others with similar characteristics.
  • It helps to gain insight into consumer needs and get a firm understanding of a broader target group.
  • It can help marketers understand the impact of their campaign on a specific group of customers.
  • Google Analytics can help with this analysis method.

Regression Analysis

  • This method uses historical data to understand how the change in the value of an independent variable affects the value of a dependent variable.
  • Here we understand the relationship between these variables to anticipate possible outcomes and make better business decisions.
  • For example, the overall annual sales depend on various independent variables like product quality, store design, marketing campaigns, customer service, sales channels, etc. Regression analysis can help figure out which of these independent variables affects the annual sales and to what extent.

Neural Networks

  • It is a method that uses an intelligent algorithm of machine learning to process insights and predict values.
  • Neural networks try to understand how the human brain works based on each and every data transaction.
  • It can be used in predictive data analysis. 

Factor Analysis

  • This method is used to reduce a large number of variables to a small number of factors. 
  • This method works on the basis that these various separate variables correlate with one another because they all are associated with a similar function.
  • It helps to condense large datasets into small manageable samples.
  • For example, variables like color, shape, material, comfort, frequency of usage, current trend, etc., can be grouped into a factor of design.

Time Series Analysis

  • It is a method used to identify trends and cycles over time.
  • It is a sequence of data points in which the variables are measured at different points in time.
  • These time-related trends help the analysts to forecast how the variables may fluctuate in the future.

Text Analysis

It is a method in which a large set of textual data is arranged to make it easier to manage.

  • This method will help extract the genuinely relevant data to the business and could be used to develop actionable insights.
  • It also helps the analysts understand the emotions and intention of a text.
  • Word-based sources like product reviews, articles, social media communications, survey responses, etc., will help gain invaluable insights into the audience’s needs, preferences, and pain points.

Individuals who are planning to build a career in Big Data, which is currently an emerging sector, can take up various online courses to learn the data analysis methods. There are multiple courses available through education platforms like Coursera, Simplilearn, UpGrad, edX, Udemy, etc. These skills can also be learned through courses offered by companies like IBM, Google, etc.

The average salary package for a Data Analyst is ₹5 lakh per annum in India and $61,000 in the USA. This is one career line that has stored a lot of growth potential. Therefore, if you also want to head for a rewarding career, don’t wait and simply grab a Data Analysis course today.

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