Introduction
Analytics is a field of computer science that is used to discover, interpret and find patterns in the data. Analytics has now become a means of propulsion for business development, providing organizations the potential to implement new and creative strategies that will eventually improve customer experience and increase the organization’s revenue.
In the past, data storage and processing speed limited analytics. Today, those limitations are no longer there. Analytics has opened doors to more complex algorithms of machine learning and deep learning. Today most organizations use analytics as an asset for strategy, and analytics is a center for many functional roles and skills.
The term analytics is used broadly around the world so it seems difficult to differentiate its purposes and applications. Business Analytics and Data Analytics are great examples of this. Both terms are often used correspondingly, yet they are quite distinct from one another.
So, let us now begin by understanding both the terms well.
Business Analytics
Business Analytics is the science of using data to interpret and analyze trends and patterns in the business. This helps organizations to improve their current operations and make better decisions for the future. Experts / Analysts use data to make practical and solid decisions for the organization. Above all, Business analytics can be used to improve relationships with customers and indicate potential risks for the organization.
Business analytics focuses on creating solutions and solving existing challenges that are unique to the business and usually stay on the front of the data pipeline. This may involve the use of analysis tools and visualization tools to improve business functions, such as Sales and Marketing.
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Descriptive Analytics
The purpose of Descriptive Analytics is to summarize the finding and understand what is going on. It summarizes an organization’s existing data to understand what has happened in the past or what is happening currently.
It can help identify strengths/weaknesses and can help identify customer behavior.
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Diagnostic Analytics
Diagnostic Analytics is used to find out why things happened in the past. We also know it as root cause analysis because it looks deep down to find the root cause.
Diagnostic analytics makes use of probabilities, and likelihoods to understand why events may occur. This type of analytics does not have unlimited resources to give insight. It only provides an understanding of causal relationships while looking backward.
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Predictive Analytics
Predictive analytics are used to deliver future outcomes. It uses the collected data and the results of descriptive and diagnostic analytics to tell what is likely to happen in the future. Moreover, it cannot predict if an event will occur in the future, and can only predict what are the possibilities of the occurrence of the event.
The most common application of predictive analytics is sentiment analysis. This collects existing data from social media and then used to predict the person’s sentiment on a particular subject. These predictions can solve problems and identify growth opportunities.
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Prescriptive Analytics
While predictive analytics shows the probabilities of occurrence of events, Prescriptive Analytics suggests the best future solution. It uses a strong feedback system that continuously learns and updates the relationship between action and the result.
Prescriptive Analytics are valuable because of their ability to measure the result based on different future scenarios and recommend the best action to be taken.
Importance of Business Analytics
Business Analytics is important as it reduces risk and helps organizations to do the same. It helps organizations to make better & prime decisions and also helps reach the goals of an organization.
Pros and Cons of Business Analytics
Pros
- Business Analytics helps organizations to monitor the progress in their mission
- It can gather huge amounts of data and display it in a constructive way to help organizations achieve their goal.
- Above all, It gives the organization insight into how to target customers’ thought processes
Cons
- Low transactional data quality – Sometimes, implementation of a solution fails because quality data is not available or data construction is too complex.
- Lack of commitment – It is very important to stay dedicated to the result.
Data Analytics
Data Analytics is the science of analyzing raw data to find trends and find answers to questions. The Data Analytics process has some components which can help the different kinds of organizations in achieving their goals. Above all, Every organization collects huge amounts of data which includes market searches, sales figures, or transactional data. Moreover, Data analytics allows businesses to improve their processes based on these learnings to make better decisions.
Broad multiform of businesses and industries can attain advantages from data analysis insights, which are now more accessible than in the past. Data analytics can do much more than just point out restrictions in the creation of products.
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Descriptive Analytics
Descriptive Analytics describes what is happening in the business in a given period. It uses historical as well as current data to identify the present state of trends and patterns. This type of analytics provides essential insight into past performances.
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Diagnostic Analytics
Diagnostic Analytics gives answers to the questions about why things happened. It takes results from descriptive analytics and then digs deep into the roots to find the cause. Diagnostic analytics could guide by helping to identify patterns and uncover relationships.
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Predictive Analytics
Predictive Analytics describes what will happen in the future. It takes historical data and analyses them to define trends and identify whether they will reoccur or not. This involves a variety of data inputs and a bit of presumption.
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Prescriptive Analytics
Prescriptive Analytics helps find out what should be done next or what will be the best course of action. Moreover, It considers all possible patterns or ways that an organization might take. Based on all the possible scenarios and outcomes, it helps an organization to decide what will be the better action to take.
Importance of Data Analytics
Data Analytics helps organizations obtain the understanding and knowledge they need to enhance productivity and to grow further. Above all, this vitalizes businesses by uplifting disciplined thinking, keeping decision-makers focused, and amending communication between business leaders and data analysts.
Pros and Cons of Data Analytics
Pros
- Data Analytics helps organizations to make better and faster decisions.
- It helps analyze large amounts of data and display it in a detailed manner. And, also detect and correct duplicate data from the data set.
- Moreover, It also improves the quality of products and services and helps with improving user experience.
Cons
Low Data Quality or Lack of access to quality data–Organisations may have a huge amount of data, but it is of no use if the data is not right.
Privacy concerns are one of the major drawbacks of data analytics as the collection of data could breach the privacy of the customers and it will share all their information with organizations
Head-to-Head Comparison: Business Analytics and Data Analytics
Goal
Business Analytics focuses on identifying trends in an organization that can be optimized to improve overall business planning and performance.
Whereas, Data Analytics focuses on recognizing patterns in the dataset and making accurate predictions based on events.
Data
They defined data sources in advance in business analytics, based on the project goals.
While In Data Analytics, uncovered correlations make analysis more extemporary with data sources.
Approach
The former involves defining the goal and requirements for programs and projects.
The latter attempts to answer specific questions and discover new insights for competitive advantage.
Conclusion
In conclusion, Data Analytics works more closely with the data itself, while they only involved Business Analytics to undertake business needs and recommend business solutions. Both have similar roles and both types of analytics use data to improve business decisions, but they do it in different ways and require unique skill sets.
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