What is Data Analysis: Methods, Process and its 5 Types 

What is Data Analysis: Methods, Process and its 5 Types 

Businesses today operate on a very narrow margin of error and that means that they need every possible advantage they can avail to effectively manage changing markets, economic uncertainty, changing socio-political landscape and shifting customer attitudes. Digitalization has therefore become important for businesses to remain relevant and tap technical resources to augment their business operations. And with digitalization comes large volumes of data collected through various sources and platforms. This data can provide crucial and invaluable insights regarding the product/service, customers and market and in turn help companies in making informed decisions, strategies and policies. This is where data analysis comes into the picture. 

What is Data Analysis?

Data Analysis is the process of cleaning, changing and processing raw data to extract relevant insights, actionable information, supported by statistics, charts, images and tables to alleviate the inherent risk in decision making. 

The simple process of analyzing the trends from the past to predict the future is a process followed in the most mundane activities of everyday existence however data analysis for businesses can be crucial to determine their success or failure. 

Additionally the “Big Data” can only make business sense after they have been analyzed. 

Advantages of Data Analysis

Data Analysis can have immediate observable benefits in the following areas:

  1. Better Customer Targeting – A focused targeted advertisement campaign helps in tapping the relevant demographics which means better lead generation and conversion for the business. Data Analytics can provide better insights regarding the spending habits, disposable income and area of interest. It can help the business decide the prices, length of the campaign and scalability scope for the business. 
  2. Optimizing the operational cost – Data Analysis can provide a better picture regarding the resource consumption by each business area and can help make better decisions in evaluating the need to scale up or down. 
  3. Better Problem Solving – More information about a problem and a holistic understanding of the various elements constituting the problem helps in arriving at better solutions and data analysis can help avoid costly pitfalls and make right choices while looking for solutions for business problems. 
  4. Accurate data – Data Analysis provides accurate data and thus paints an accurate picture of the business operations. 

The Data Analysis Process

The process of data analysis takes place in steps.

  1. Data Requirement – The first step in the process is identifying the type of analysis and then gathering the data required to complete the analysis. 
  2. Data Collection– The next logical step is to collect data from relevant sources like case studies, interviews, questionnaires, direct observation and focus groups. The collected data then needs to be organized. 
  3. Data Cleaning – Data Cleaning is important to remove white spaces, duplicate records and other basic errors and only then the next step of analysis can take place. 
  4. Data Analysis – This step involves analyzing the cleaned data using Data Analysis tools including Excel, Python, R. Looker, Rapid Miner, Chartio, Metabase, Redash and Microsoft Power BI. 
  5. Data Interpretation – This steps involves interpreting the data and extracting relevant insights which can be used for creating actionable steps for the next course of action.
  6. Data Visualization – It is the graphical representation of the data in the form of charts, graphs and bars to make it visually appealing and easily understandable. It makes comparing data and deciphering trends easier. 

Types of Data Analysis

There are several types of data analysis used in technology and business. Lets take a look:

  1. Diagnostic Analysis – This analysis basically answers the question , “ Why did this happen”. Statistical analysis shares insights about the patterns in data and analysts discover similar patterns that existed in the past and use that information to solve the present challenge. 
  2. Predictive Analysis– As the name suggests this kind of analysis provides information about what can happen in the future. The patterns found in the older data and comparing it with current trends can help predict the future events. If the data is researched thoroughly, the chances of accuracy in predictions increase. 
  3. Prescriptive Analysis – This type of analysis relies on gathering perspectives from different sources of data analysis and using it to prescribe an analysis for a situation. 
  4. Statistical Analysis – Statistical Analysis provides insights about “what happened” in a given situation and relies on data collection, analysis, modeling, interpretation and presentation using dashboards. It has tow sub-categories:
  • Descriptive – This works for either complete ( providing means, deviations) or selection of summarized numerical data providing (percentages and frequencies)
  • Inferential – These are samples derived from complete data and different inferences can be drawn from the same data set based on the sample chosen.

5. Text Analysis – Uses databases and data mining tools to identify patterns in the large data sheets to extract relevant business information. 

Data Analysis Methods

Data Analysis methods largely fall under two categories:

Qualitative Data Analysis – This involves analyzing words, symbols, pictures, observations etc. and does not use statistics. 

  • Content Anamysis
  • Narrative Analysis
  • Grounded Theory – Causal explanation by extrapolating from the past events

Quantitative Analysis – Raw data which is processed into numeric data

  • Hypothesis Testing – Assessing a given hypothesis whether it is true or false
  • Mean
  • Sample size determination – Sample taken from a large group considered as a representation of the entire body

AI as a Tool for Data Analysis

AI has emerged as an important tool for data analysis and some of the techniques used are:

  • Artificial Neural Networks
  • Decision Trees
  • Evolutionary Programming
  • Fuzzy Logic

Mathematics and Statistics are other tools to data analysis technique

  • Descriptive Analysis
  • Dispersion Analysis
  • Discriminant Analysis
  • Factor Analysis
  • Regression Analysis
  • Time Series Analysis

Graphs and visualizations are other tools for the same. 

The Data Analytics industry is expected to create 11 million jobs by 2026 and therefore now is a good time to learn data analytics and invest in this career option.

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