Data analytics 101: Types, methods, and techniques

Data analytics focuses on drawing conclusions from data. It consists of the procedures, equipment, and methods used in data management and analysis, including gathering, arranging, and storing data. Data analytics' main goal is to use statistical analysis and technology on data to identify trends and resolve issues. Enterprises now place a greater emphasis on data analytics as a way to better understand and shape business processes, as well as to enhance decision-making and financial performance.

Data analysis is done on data to characterize, forecast, and enhance performance. It draws from various fields, including computer programming, mathematics, and statistics. Data analytics teams use a variety of data management approaches, including data mining, data cleansing, data transformation, data modeling, and more, to ensure thorough analysis.

Although "data analytics" and "data analysis" are sometimes used synonymously, data analysis is a subset of data analytics that focuses on scrutinizing, manipulating, modeling, and extracting meaning from data. The instruments and methods used to carry out data analysis are included in data analytics.

Types of data analytics

Analytics can be broadly divided into four categories:

Prescriptive analytics suggests actions to achieve the desired outcomes,

Diagnostic analytics evaluates the reason why something happened,

Predictive analytics determines the likelihood of something happening in the future,

Descriptive analytics tries to describe what has happened at a specific point in time,

By finding trends and patterns, descriptive analytics analyzes historical and current data from several sources to characterize the current condition or a specific previous state. This is the domain of business intelligence in business analytics (BI).

Diagnostic analytics uses data (typically produced by descriptive analytics) to identify the causes or explanations for previous performance.

Predictive analytics uses methods like statistical modeling, forecasting, and machine learning on descriptive and diagnostic analytics results to predict future outcomes. Predictive analytics, which usually relies on machine learning or deep learning, is frequently seen as "advanced analytics."

Prescriptive analytics is a subset of advanced analytics that uses testing and other methodologies to suggest particular solutions that produce desired results. Predictive analytics in business uses business rules, algorithms, and machine learning.

Methods and techniques

To analyze data, data analysts employ a variety of techniques and approaches. Seven of the most popular are as follows:

Regression analysis is a statistical technique used to evaluate the correlations between variables and ascertain the potential effects of changing one or more variables. For instance, how would invest in social media impact sales?

Monte Carlo simulations are used to simulate the likelihood of various outcomes in a process that is difficult to forecast because of the interference of random factors. Risk analysis typically makes use of it.

Factor analysis is a statistical technique for condensing a large data set into a more manageable, smaller one. This analysis also frequently reveals hidden patterns, which is a bonus. Factor analysis is frequently used in commercial settings to investigate issues like client loyalty.

Cohort analysis divides a dataset into cohorts or groups with similar features for analysis. Understanding client segmentation typically involves using this.

Cluster analysis is used to make data structures visible; for example, insurance companies might employ cluster analysis to find out why specific locations are linked to specific insurance claims.

Time series analysis is a statistical method that deals with trend analysis or time-series data. Data in a sequence of specific periods or intervals are referred to as time-series data. When analyzing time-series data, such as weekly sales figures, it is possible to spot trends and cycles across time. Forecasting for the economy and sales is regularly done using it.

Sentiment analysis employs natural language processing, text analysis, computational linguistics, and other technologies to comprehend the emotions portrayed in the data. Sentiment analysis aims to evaluate and categorize qualitative data by grouping it into themes instead of the preceding six methods, which examine quantitative data (data that can be measured). It is frequently used to learn what consumers think about a company, its goods, or its services.