The world is gradually changing into a small connected business society following the impact of technology on business operations. Perhaps, you have already heard more about the impact of data analysis vs. data analytics on the growth and development of enterprises.
Every business wants to get some of the newly developed technologies to enhance the growth of their businesses. Let’s face reality; the current business world mainly relies on data to overcome the headwinds across the industry.
Despite the ongoing transformation in the business industry, some business owners do not understand the impact of data analysis and data analytics on the success of their businesses. This content piece incorporates in-depth information to help them understand the industry tides.
What is Data Analysis?
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Data analysis entails the process of sorting massive amounts of the unstructured dataset to generate valuable insights that you can use in the business operations. The insights generated from data analysis are mainly used in the decision-making process.
However, most business operators tend to get confused between data analysis and data science. These two terms are not similar, although they are closely related because they originate from the same family.
Data science is a more advanced field that incorporates lots of programming and the creation of algorithms to generate the desired results. The field requires advanced technical knowledge to get the job done and get the desired results.
Role of Data Analysis in Business
The data analysis field plays a significant role in enhancing the growth and development of businesses. It is one of the fields that can greatly elevate the standards of a given business only if the work is done appropriately.
Companies are currently collecting massive amounts of data from their daily operations. However, the data is always available in the raw format. This form of data does not have any significant meaning to the business.
The raw data collected needs to be converted in an advanced format that the management panel of the business can use. This is where data analytics comes in to aid in the process. But what does it mean?
Data analytics entails the evaluation of raw datasets to generate actionable insights that impact the business’s normal operations and the management of the data. The business management team then uses the final output generated from the data to enhance its growth.
What does a data analyst do?
A certified data analyst carries out the entire work, extracting data, organizing it, and then analyzing the final results in line with the business operations. The data analysts transform incomprehensible numbers into vital, intelligent information.
In other words, you can evaluate data analytics as a type of business intelligence that is derived towards solving specific problems across the business environment. It is all about finding out specific patterns in the dataset to give you useful vibrant information and insights.
Types of Data Analysis
Even though the types of data analysis and analytics seem to be similar, data analytics is more detailed and a bit complex. The ultimate goal of data analysis mainly depends on the type of data analysis and the level of the skills used to execute the process. Let’s look at some of the most popular types of data analysis used by data professionals!
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Descriptive Analysis
This type of analysis model is primarily meant to help answer the question explaining what happened. This model gives a detailed summary of the entire data story that gives room for the next important step by answering the question.
Remember that this type of data analysis does not make any form of prediction during the process of data analysis. It only gives general reports that help data professionals understand the whole scenario in-depth.
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Exploratory Analysis
Exploratory analysis is the second type of data analysis that tries to go deeper into details. It gives a rough outline regarding the patterns and any relationship between the raw data generated from the business operations.
At this stage, you can identify some visible trends and patterns across the raw data that will help you comprehend more about what you have in hands.
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Diagnostic Analysis
Diagnostic analytics focus on shedding more light on the reasons why something happened the way it is. This stage goes further to identifying various anomalies across the given data. Note that this is something that the data can hardly explain.
To get the answer, the data professionals return to the discovery phase to identify any other possible data source that might give more direction about the possible anomalies.
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Predictive Analysis
Predictive analysis is a crucial type of data analysis mostly used by data scientists. Even though data analysts use this model, it is not common since it involves things to do with machine learning and computer algorithms.
The use of machine learning and the predictive nature of this stage focuses on giving possible predictions depending on the nature of the data. It plays a critical role in things such as risk assessment and sales forecasting.
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Prescriptive Analysis
Predictive analytics is mainly built from the outcomes generated from all other data analytics types. Besides, it shows you how you can take advantage of the results generated by all the other processes and come up with final useful results.
During the process of developing prescriptive analytics, data professionals consider a variety of scenarios and evaluate different actions that the organization might take. This is a complex stage that requires a wide range of technical skills to get the job done.
During the prescriptive analytics process, data professional uses machine learning skills and evaluate different algorithms to develop reliable insights that the business can use.
What is the Difference Between Data Analysis and Data Analytics?
Data analysis and data analytics are mostly treated as the same thing. However, the reality is that the two terms have a slight difference in meaning. The difference between the two is said to be a matter of scale since data analytics is a broader thing.
Data analysis is a subcomponent of data analytics. Alternatively, data analysis is the process of transforming, examining, and organizing a given dataset in a certain manner to give individuals a chance to study it and extract vital information.
On the flip side, data analytics is a technical discipline that incorporates a complete management of the given data. This process includes analysis, collecting, organizing, and storing data with the aid of different data tools.
The data analyst is responsible for collecting, analyzing, and translating the given data depending on the type of business. The advanced skills possed by data analysts have placed them on a competitive edge, especially in this era where the business industry is advancing steadily.
Which is Better for Businesses?
Businesses operate depending on their goals and objectives in the industry. Any business operating with clear goals and objectives is likely to make prudent decisions perfectly aligned with its objectives.
The ultimate objective of data analysis lies in its ability to identify possible trends and patterns across the business data. The patterns in the datasets given play a significant role in evaluating possible changes that are likely to affect the organization from attaining its goals.
On the other hand, data analytics evaluates business operations using the business data and identifies points that need changes and modifications. Analytics suggests changes depending on the business operations and the objective of the business.
The two features play a crucial role in enhancing the success of businesses. The best feature that works for one business might not be the best for the other business. The better option entirely depends on the goals and objectives of a given company.
FAQ
Is analysis and analytics the same?
Analysis and analytics sound similar. However, the two are slightly different since data analytics is a broader phrase while data analysis is a subcomponent. Besides, the two involve the analysis process, organization, and data collection to get the job done.
Final Verdict!
The two terms have become common within the business industry. They are mainly used in different aspects to help the business attain a general goal of success. This article covers all the important features that draw the difference between the two terms! Besides, it has highlighted al the major application of these features in the business setting.
