Abstract
Modern businesses have come to rely heavily on data analytics because it offers insightful information about consumer behavior, operational effectiveness, and financial performance. Organizations employ a variety of data analytics technologies, including Tableau, Excel, Python, and R, to analyze enormous volumes of data and derive actionable insights. These tools assist in gathering, purifying, processing, analyzing, and visualizing data to find patterns and trends. Numerous sectors, including marketing, finance, healthcare, manufacturing, and retail, can benefit from data analytics. Businesses may make wise decisions, improve productivity, cut expenses, and gain a competitive edge by utilizing data analytics tools and apps not only for healthcare sector but also other business sectors.
Top2. Definition Of Data Analytics
Analyzing raw data to derive useful insights, spot trends, and come to wise judgments is known as data analytics. It entails gathering, scrubbing, processing, and analyzing data in order to produce insights that might aid businesses in decision-making. In order to provide the insights in a meaningful and intelligible way, data analytics aims to extract relevant and usable information from big and complicated data sets.
Figure 1. Type of data analysis for decision-making using data
Data analytics, which includes data mining, machine learning, and predictive analytics, uses a variety of statistical and computational techniques to glean insights from data (David, 2020) (Wikipedia, 2023).
Top3. Data Analytics Process
Data analytics often comprises a number of phases, which can change based on the particular project or use case. But the following phases can be used to breakdown a typical Data Analytics process:
3.1 Define the Problem
The phrase “defining the problem” in the context of data analytics refers to determining the opportunity or business issue that has to be resolved utilizing data. Understanding the organization's aims and objectives, determining the business questions that must be addressed, and comprehending the data that is available to address the problem are all necessary steps in defining the problem. In the Data Analytics process, defining the issue is essential since it establishes the scope and direction of the analysis. A clear issue statement makes it easier to choose the best data sources, the best analytic techniques, and the most pertinent and useful findings (Shah & Khang et al., 2023).