Definition of Data analysis and analytics
Data analysis refers to a process of examining, transforming, cleaning, and understanding the data to identify some relevant information, advice conclusions, and aid in the decision-making process. Some of the tools of data analysis are Node XL, Google Fusion, and much more. Data analytics uses data, computer-based processes, statistical analysis, and machine learning to gain improved insight and make good decisions from data. Analytics is a method of changing data into activities using analysis and meaningful insight concerning problem-solving skills and organizational decision making. Some of the tools that support analytics are Microsoft Excel, R, tableau public, SAS, Python (libraries), and Apache Spark.
An in-depth comparison between data analytics and data analysis
The following are the comparisons between data analytics and data analysis based on factors as mentioned below.
Data analytics is defined as a common mode of analytics that is utilized in especially in a data-driven company to make decisions based on data. On the other hand, data analysis is a specific type of data analytics employed by businesses to examine the data and extract some meaningful insights from it.
Data analytics structure comprises of data accumulation and checking in general sense. However, it has over one usage. The data analysis process involves describing, examining, cleaning, and converting data in such a way to provide meaningful insights.
In general, data analytics can be utilized to identify hidden patterns, unknown correlations, customer choices, market trends, and other required information that helps in making better decisions for business purposes. Data analysis can be utilized in different ways. For instance, an individual can perform descriptive analysis, explorative analysis, probable analysis, predictive analysis, and extract meaningful insights from data.
The process of data analytics involves business case evaluation such as data findings, acquiring and filtering data, extraction and validation data, cleansing and collection of data, data analysis and visualization, data representation, and use of analysis outcomes. In addition, the data analysis lifecycle includes gathering, analyzing, scrubbing, and deciphering data accurately to understand the meaning of it.
An example of data analytics is that data analytics can be used to identify the customer patterns of customers based on past data of purchases. On the other hand, data analysis is the correct reverse of data analytics where the past performance related to customer purchases is analyzed based on past purchase data.
Nowadays, data is highly used. A large chunk of data is acquired by various organizations. Data can be linked to business purposes, customers, application users, stakeholders, and others. Here, the data is combined and separated to identify, analyze, and figure out patterns. Data analytics refers to different tools and skills that involve quantitative and qualitative techniques which uses this accumulated data and generate result that is utilized to improve business process. Data analysis subsist as an alternate part of data analytics which is specifically designed for decision-making processes.
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