Process Types and Methods involved in Data Analysis
Today businesses are looking for every edge and advantage they could gather.
There are barriers such as unexpected conversion markets, financial uncertainty, shifting political landscapes,
The capricious attitudes of buyers, which make businesses operate today with thin margins of error.
Without smart choices and proper data analysis, it is impossible to sustain the growth and development of any business.
Then how does an individual for this matter an organization make those choices?
It is simply by accumulating as a whole a lot of useful, exploitable cases as viable, then using it to make better-informed decisions.
Common sense is the strategy that can be applied to business as well as personal life.
Important decisions are only made until and unless something is at stake. In order, for any company to succeed, decisions shouldn’t be made based on ignorance.
Organization requires data, and this requirement of data is the reason why data analysis comes into the picture. Below is the list of topics that gives you a better understanding of data analysis.
List of topics
1. Data Analysis
2. Process of Data Analysis
3. Types of Data Analysis
4. Data Analysis Methods
- Data Analysis:
Numerous agencies and professionals have different methods of data analytics, but most of them can be described under a common definition.
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
The program helps in reducing the possibility of risk inherent in decision-making by providing useful information and statistical data (usually presented in the form of a chart, tables, graphs, and images).
The term “big data” is often used in discussions of data analytics.
Data analytics plays an important role in processing big data and transforming it into actionable information. Novice data analysts looking to explore the principles of big data should return to the basic question “What is data?”
Data are quantifiable units of information collected through Research observation.
2. Data Analysis Process: The data analysis process, or discrete steps in data analysis, involves collecting and processing all information, examining it, and using it to find patterns and other information. This process includes:
Data Requirement Gathering: Ask yourself why you are doing this analysis, the type of data you want to use, and what data you want to analyze.
Data Collection: Start collecting data from the source according to the requirements you define. Resources include case studies, surveys, interviews, questionnaires, personal observations, and focus groups. Organize collected data for analysis.
Data Cleaning: Not all data collected is useful, so now is the time to clean it up. This process removes free disk space, unwanted recordings, and inherent errors. It is important to clear data before submitting it for analysis.
Data Analysis: Here you can use data analysis software and other tools to interpret, understand, and estimate your data.
Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
Data Interpretation: Now that we have our results, we need to interpret them and do our best based on them.
Data Visualization: Data visualization is nothing but a graphical presentation of the information that people can read and understand. It can be in the form of tables, charts, bullet points, maps, or any other methods.
Visualizations provide valuable information by comparing data sets and monitoring relationships.
3. Types of Data Analysis:
There are currently half-dozen types of data analysis that are widely used in technology and business. They are:
Diagnostic Analysis: “Why is this happening?” the one who has an answer to this question is diagnostic analysis.
The analyst uses the information obtained from statistical analysis to identify patterns in the data through diagnostic analysis. Ideally, the analyst looks for similar patterns that have existed in the past and use solutions to solve current problems.
Predictive Analysis: Predictive analysis can answer “What is most likely to happen?”. Analysts use historical data from current events and future patterns to predict future events.
NO prediction is 100% accurate, but analysts are more likely to make predictions if they have enough specific information and enough discipline to thoroughly investigate.
Prescriptive Analysis: Combine all information from different types of data analysis which gives you prescriptive analysis.
Sometimes, an issue cannot be solved completely with one analysis type, and instead requires a lot of information.
Statistical Analysis: “What happened?” can be answered by statistical analysis. It involves the collection, analysis, interpretation, modeling, and visualization of data through dashboards. Statistical analysis can be divided into two subsets:
(a) Descriptive: Descriptive analysis works on the whole set or summarized numerical data. It explains the mean and deviation in continuous data and the percentage and frequency in categorical data.
(b) Inferential: Inferential analysis works with samples derived from any data. Analysts can draw different conclusions by choosing different samples from the same bulk of data.
Text Analysis: Another Name of text analysis is “data mining”, which uses databases and data mining tools to find patterns in large data sets. Transform raw data into actionable business transformation. Text analysis is probably the simplest way to analyze data.
Now, we will try to understand the data analysis method in depth
Cluster Analysis: Grouping a set of data elements according to similarity to each other than to those in other groups.
This method is often used to find hidden patterns in data because there is no target variable when clustering. This approach is also used to deliver additional context for a process or data set.
Looking from a business perspective. In an ideal world, marketers could analyze each customer individually and provide the best personalized service, when we deal with a large customer base, it is next to impossible.
But when we group customer base according to demographics, purchasing habits, monetary value, or other business-related factors.
which might be relevant for our company, we can improve our efforts and the needs of the customers giving the customers a great experience.
Cohort analysis: This method allows you to use historical data to investigate and compare specific parts of user behavior and integrate them with other parts with similar characteristics.
We can use this data analysis method to gain a general understanding of consumer needs and a broader understanding of a larger audience.
Cohort analysis is very useful for market research as it gives you an idea of the impact of a campaign on a specific group of customers.
For example, let’s say we send out an email campaign that encourages customers to sign up for your website.
To do this, create two versions of the campaign, each with different designs, promotional invitations, and advertising content.
You can then use group analytics to track campaign performance over time to understand the types of content our customers are asking to subscribe, repurchase or engage with.
Regression analysis: Regression analysis uses historical data to understand how the dependent variable affects when one (linear regression) or more (multiple regression) independent variables change or remain constant.
Understanding the relationship between each variable and the past variable will help you anticipate possible outcomes and make better career decisions for the future.
Let’s take an example, imagine in our 2019 sales regression analysis, we found that variables such as product quality, store layout, customer service, marketing campaign, and sales channels affected the overall results.
Now, we want to use regression analysis to analyze which of these variables have changed or which new ones have appeared in 2020.
For example, due to COVID lockdowns, the sales figures have gone down in our physical store. Due to this, there is a sales dropped in general or an increased in online channels.
Likewise, we can understand which of the variable affected the overall performance of your dependent variable, annual sales.
Neural networks: Neural networks are the foundation of intelligent machine learning algorithms.
This is a type of data-driven analysis that seeks to understand how the human brain processes knowledge and predicts value with minimal intervention.
The neural network learns from every data traction. In other words, it evolves over time.
Predictive data analysis is a common application of neural networks. There are business intelligence reporting tools that implement this feature including Predictive Analytics tools from datapine.
This tool allows us to quickly and easily make different types of forecasts. Just select the data to process based on KPI’s and the software will automatically calculate forecasts based on historical and current data.
It can be managed through an easy-to-use interface for all users in any organization. No advanced scientist is required.
Factor analysis: Factor analysis, also known as dimensionality reduction, is a type of data analysis used to account for variations between the observed minimum number of correlated variables, called factors.
The goal is to find the hidden independent variable. This is an ideal analysis method for simplifying certain data segments.
A good example of understanding this data analysis method is customer reviews of products.
The initial evaluation is based on several variables such as color, size, fit, current trends, material, comfort, where the product is purchased, and how often it is used.
This way we can create endless lists based on what we want to track. In this case, factor analysis is performed by combining all these variables into homogeneous groups.
Data mining: A method of analysis that is used to describe technical standards and concepts of values, direction, and context.
Data mining is performed using exploratory statistical evaluation to identify dependencies, relationships, data patterns, and trends to gain insights.
Adopting the ideas of data mining when it comes to data analysis is the key to success.
Data pine intelligent data alerts are a perfect example of data mining. It uses artificial intelligence and machine learning to send automated signals based on specific commands or events in a data set.
For example, if we are monitoring supply chain KPIs, we can set up a smart alert that fires when invalid or low-quality data is displayed.
This allows us to better understand the problem and solve it quickly and efficiently
Text analysis: Text analysis, also known in the industry as text mining, is the collection and organization of large amounts of text data in a manageable way.
Reading all the details of this cleanup process can provide valuable information to help you extract data that is truly relevant to your business and move forward.
The latest tools and technologies for data analysts accelerate the text analysis process.
The combination of machine learning and intelligent algorithms allows the application of advanced analytical techniques such as sentiment analysis.
This technique allows you to understand the purpose and sentiment of a text (positive, negative, neutral, etc.) and rank it according to specific factors and categories related to your brand.
Sentiment analysis is widely used to track brand and product reputation and to understand customer success based on experience.
Analyzing data from a variety of text sources, including product reviews, articles, social media, and survey responses, can provide valuable insights into your audience and their needs, preferences, and complaints.
Create campaigns, services, and communications that meet the needs of your audience at an individual level to grow your audience and build customer loyalty. It is one of the most effective data analysis tools and techniques that anyone can invest in.