Data Science Vs Data Analytics VS Big Data: What Does Your Business Need?

Data Science Vs Data Analytics VS Big Data: What Does Your Business Need?

In this digital age, it is impossible to lead our lives without data practically. The unstructured data amount increases every day as diverse avenues have opened up in the Big Data landscape with the humongous data, leading to the sole basis that data has evolved as one of the most critical aspects of businesses. Thus, there is a continuous necessity for advancing elevated technologies that stand the potential to transform the data available so that the business objectives are met. In this magical world of data, Wissenhive sheds light upon Data Science Vs. Big Data Vs. Data Analytics!

Data Science

Data Science refers to an interdisciplinary area that utilizes a combination of scientific methods, algorithms, tools, processes, systems, and machine learning techniques to extract knowledge to find the hidden pattern from given raw data that contains structured, semi-structured, and unstructured data. Data Science uses the theories and techniques from various fields such as Statistics, Information Science, Mathematics, Computer Science, and Domain knowledge to lead the information and data.

Data science works with the most potent hardware, efficient algorithms, and programming systems to solve data-linked problems. It is very similar to big data, data mining, and machine learning. Which is considered the future of artificial intelligence. In short, data science is all about:

• Analyzing the raw structure and unstructured data.
• Modeling and presenting the data using various efficient and complex algorithms.
• Visualizing and reflecting the data to achieve a better perspective.
• Deep searching about data to find the final result and to make better decisions.

Data science has successfully catered to a wide range of applications – recommendation systems, digital advertisements,  internet search, and so on.

Data Analysis

Data Analysis refers to a structural process that includes working with huge data by exciting some activities such as cleaning, ingestion, assessing, and transforming it to deliver insights used to drive revenues. In the beginning, data collected from various sources is respected as a raw entity, as it has to be processed and cleaned to fill the missing values out by removing the entities that are out of the usage scope.

After preprocessing all the data, it is analyzed with the help of different advanced models, which utilize the data to conduct some analysis. The final step includes ensuring and reporting the data output that is converted into a format that caters to non-technical people alongside the data analysts.

There are considerable numbers of data analytics applications. Some of the primary industries include – the healthcare sector, where data analytics is utilized for tracking and optimizing patient treatment, flow, and equipment used in hospitals. The gaming industry, where the primary role is to collect data by optimizing and spending within and across the games. Travel industry where the travel corporations can focus on gaining insights into the preferences of the customer.

Big Data

Big Data refers to collecting complex and massive volumes of data, which is divided into three different sections unstructured (like images, videos, audios, etc.), semi-structured (such as XML files), and structured (like DBMS tables) that not a single traditional data management device or mechanism can store or process efficiently. It inundated companies, organizations, or businesses on a daily basis.

However, some of the basic tenets describe Big Data in simpler ways, which are

• Big data refers to a vast volume of data that is growing exponentially with time and technology.
• Big Data is so massive, which cannot be processed and analyzed with traditional data processing techniques.
• It includes almost everything linked to data, such as data visualization, data storage, data mining, data sharing, and data analysis.
• It is comprehensive, which includes three types of data: semi-structured, unstructured, and structured, a data frame with different tools and techniques used for processing and analyzing the data.

Numerous enterprises and businesses can generate significant data, which rely on the financial sector heavily on big data for compliance analytics, operational analytics, fraud detection, and a lot more. Additionally, the retail industry also uses big data for a better understanding of the customers. Apart from these two, there are countless other industries as well for making better-informed decisions based on the gathering of big data.