What is Data Analytics?

Data analytics transforms raw data into actionable insights. It allows you to discover patterns in data raw and extract valuable information from it. Data analytics can improve decision-making and shape business processes. Data analytics processes may use applications incorporating machine learning algorithms. The systems and algorithms work on the unstructured data for human use.

Data analytics refers to the process of collecting, examining, transforming, and explorating raw data in order to draw conclusions such as make predictions, drive informed decision making, and deeper understanding of an issues. Data analytics is applied in a variety of fields including business, healthcare, finance, science, marketing, and more. It has become increasingly important with the rise of big data as organizations seek to leverage the vast amount of information they collect to gain a competitive advantage and make informed decisions.

In simpler terms, the role of data analytics is to help companies generate profits and lay the foundation for their survival and growth. Data analytics is also the process that uses a variety of analytical techniques, including mathematics, statistics, and computer science to draw conclusions from data sets. In general, data analytics refers to the future, so it can be used to find hidden patterns, correlations, customer preferences, market trends and other necessary information that can help make more informed decisions for business purposes.

The process of data analytics typically involves several stages, namely :

1. Data Collection : the first stage in data analytics is to collect the required and relevant data from various sources, both in the form of qualitative data and quantitative data. And also we can get data from two main sources, its primary data and secondary data. These are the two main sources of data that researchers and analysts use for various purpose.
2. Data Cleaning : cleaning the data from errors, outliers, and duplicate data to ensure the validity and accuracy of the data. In this stage, data is cleaned, transformed, and prepared for analysis. This might involve removing duplicates, filling in missing values, and standardizing formats.
3. Data Analysis : after all the data is clean and ready, the next step is to perform statistical, mathematical, data mining, and perspective analysis to discover patterns, trends, and insights.
4. Data Visualization : this stage is important so that the results of data analysis can be read by users. You can use graphs, histogram, chart, and other visualizations to present analysis results and create information that is easier to understand. Then, we also presentation of information in accordance with the research problem.
5. Interpretation and Insight : extract meaningful insights from the analyzed data and translate analysis results and also their impact on business so as to help produce the right recommendations and business decisions.
6. Evaluation : the final stage is monitoring the results of implementation and conducting evaluations to ensure effectiveness and identify areas for improvement.