Big Data vs Data Science vs Data Analytics
We live in a data-driven world. In this digital era, the amount of data is growing rapidly and changing the way we live. According to a survey, the data
generating rate exceeds the human birth rate. With this huge data, diverse paths have opened up in the Big Data landscape. It includes Data Analytics and Data Science. However, people tend to use these terms interchangeably. This is because they perform various but notable jobs. However, there is a huge difference between these concepts. This article will discuss the following topics that will provide you with a clear understanding of Big Data, Data Science, and Data Analytics.
1. Overview of Big Data, Data Science, and Data Analytics.
2. What do a Big Data professional, Data Scientist, and Data Analyst do?
3. Roles and responsibilities of Big Data Professionals, Data Scientists, and Data Analysts.
4. Applications of Big Data, Data Science, and Data Analytics.
5. Skills required to become a Big Data specialist, Data Science specialist, and Data Analyst.
Before proceeding further, let us have a brief introduction to the data. Let us
get started.
What is data?
Data is a collection of facts, figures, and bits of information. The data is either structured or unstructured in the real world. In this article "Big Data vs Data Science vs Data Analytics," let us first have comprehensive knowledge about different data types.
1. Structured data_ has a well-defined structure and order. As the structural data is well-defined and consistent, it is easy to store and access it. Moreover, searching for data is easy because indexes are used to store structured data.
2. Unstructured data_ is an inconsistent data type, as it does not have a structure, sequence, or format. While performing indexing on unstructured data, is error-prone. Therefore, it is not easy to understand and perform operations on unstructured data. In the real world, we have data in an unstructured and inconsistent form. It can be in the form of text, audio, video, or other formats.
Overview of Big Data, Data Science, and Data Analytics.
What is Big Data?
Big data refers to the huge volumes of data that cannot be processed efficiently with the traditional applications currently used. Big data processing
starts with non-aggregated raw data, and mostly it is impossible to store in the memory of a single system. However, it has something to examine the insights leading to making better decisions and strategic business moves.
There are specific characteristics of big data defining its importance and structure. The six characteristics of big data, also called the 6Vs of Big Data, are listed below.
1. Volume: The amount of data generated in a day from multiple resources is very high, and it is not easy to store huge volumes of data. However, with Big Data Hadoop, it is easy to store this data efficiently.
2. Variety: There is a variety of data gathered from various resources. It can be unstructured text, audio, video, images, or documents. Big data tools can help the processing of a variety of structured and unstructured data.
3. Veracity: refers to the data quality. Organizations should take care of data quality while gathering it to make data relevant for them.
4. Velocity: In this digital era, internet users are increasing day by day. And the speed of data generation has increased. The term velocity refers to the speed of data generation and its processing.
5. Value: Big data emphasizes gathering data that creates some business values. It helps organizations stand in the marketplace.
6. Variability: Variability refers to the changes in the market trend. Big data helps to manage these changes that give advantages to businesses to come up with advanced products.
What is Data Science?
Data science refers to the slicing and dicing of great chunks of data. It leverages techniques to get insightful trends and patterns from data. Data scientists uncover the facts hidden in complex, unstructured data. It helps in making informed business decisions based on market trends. Data science involves the creation of Machine learning models. It also involves solving a problem in multiple ways to find a solution and design new processes for data modeling using various algorithms, prototypes, custom analysis, and predictive models.
To understand Data Science, look at the steps involved in its lifecycle.
1. Understanding business requirements
2. Data collection
3. Data understanding
4. Data preparation
5. Model creation
6. model evaluation
7. Model deployment
8. Iteration of process
What is Data Analytics?
Data analytics is used to give operational insights into complex business scenarios. The major concern of data analytics is to look into historical data from an advanced perspective and find new and challenging business scenarios. Then data analysts apply methodologies to find better solutions. They also predict upcoming opportunities that an organization can exploit.
It involves applying a mechanical process or an algorithm to get insights and running through various data sets to search for meaningful correlations. It is used in different industries that let organizations and data analytics companies make informed decisions and verify existing models and theories. Data analytics emphasizes inference, a process of deriving conclusions that are based on what the researchers already know.
What do a Big Data professional, Data Scientist, and Data Analyst do?
Big data professionals deal with the huge amount of data gathered from multiple sources coming at a high velocity. They describe the behavior and structure of big data solutions and how to deliver them using big data technologies, such as Spark, Hadoop, and Kafka, depending on requirements.
Data scientists perform exploratory analysis to get insights from data. For example, they use advanced machine learning algorithms to identify the occurrence of an event in the future. It involves searching hidden patterns, market trends, unknown correlations, and other useful business information.
Data analysts interpret numbers into plain text. Every business gathers data, such as sales figures, logistics, market research, or transportation costs. The job of a data analyst is to get that data and use it to help organizations make better business decisions.
Roles and responsibilities of Big Data Professionals, Data Scientists, and Data Analysts
The following table shows the roles of Big data professionals, data scientists, and data analysts.
Big Data
Data Science
Data Analyst
• Chief Data Officer
• Big Data Visualizer
• Big Data Solution Architect
• Data Architect
• Data Warehouse Analyst
• Database Developer
• Database Administrator
• Data Mining Analyst
• Business Data Analyst
• The Data creatives
• Data Researchers
• Data Developers
• The Data Businesspeople
• The Data Architects
• Database Administrators
• A Data Analyst
• Operations
Applications of Big Data, Data Science, and Data Analytics
Application of Big Data
Following are some use cases of big data.
1. Financial services: Retail banks, credit card companies, insurance firms, private wealth management advisories, institutional investment banks, and venture funds use big data for their financial use. The common issue is the massive amount of multi-structured data from disparate systems that big data can resolve. Big data can be used in multiple ways, such as
1. Operational analytics
2. Compliance analytics
3. Customer analytics
4. Fraud analytics
2. Retail: The essential element while trying to be advanced in retail is through staying competitive and serving customers effectively. It is possible through proper analysis of disparate data sources that organizations deal with, such as customer transaction data, weblogs, loyalty program data, social media, and store-branded credit data.
3. Communications: Telecommunication service providers' priorities are expanding the existing customer base, retaining customers, and gaining new ones. Therefore, analyze the customer's and machine-generated data regularly to accomplish this goal.
Applications of Data Science
1. Digital Advertisements: The digital marketing spectrum leverages data science algorithms, from displaying banners to digital billboards. It is the reason that digital advertisements have higher click-through rates than traditional advertisements.
2. Internet Search: Search engines use data science algorithms to deliver the best search queries results within seconds. In addition, it helps to provide accurate results based on user’s requirements.
3. Delivery logistics: In this digital era, e-commerce has become a vigorous industry with massive demand for online shopping. As a result, logistic companies improve their delivery experience, and organizations use data science to understand the absolute paths.
Applications of Data Analytics
1. Gaming: Data analytics in gaming involves data collection to optimize games. The manufacturing companies get better insights into the likes, dislikes, and relationships of users.
2. Travels: Data analytics in travel optimizes buying experience through data analysis, website blogs, and social media. Customers' preferences and desires can correlate with existing sales, followed by their browsing history can increase conversions.
3. Healthcare: Efficient treatment is the primary challenge faced by hospitals today as it tightens the treatment of various patients. Data analytics helps healthcare companies to improve their quality. Machine and
instrument data are used to track and optimize patient flow, equipment, and treatment used in hospitals.
Skills required to become a Big Data specialist, Data Science specialist, and Data Analyst
The following table shows the skills required to become a big data specialist, data scientist, and data analyst
Big Data
Data Science
Data Analyst
● Mathematics and statistics
● Analytical skills
● Hadoop
● Java
● Creativity
● Business skills
● R/Python programming
● SQL Database
● Analytical skills
● SAS
● Mathematics and statistics
● Visionary skills
● Hadoop
● Coding
● Programming skills
● Data wrangling skills
● Mathematics and statistical skills
● Communication
● Data intuition
● Artificial Intelligence .
Conclusion
This comprehensive guide has discussed the minor and major differences between big data, data science, and data analytics. These include an overview,
job roles and responsibilities, skills, and applications. Data is increasing day by day due to digital advancement. And if you are planning to make your career in data analytics, it is essential to consider any of these fields. Organizations are also finding experts in this field because they help gain insights and make informed decisions.