Data Science vs Big Data vs Data Analytics

Digital data is growing tremendously and changing the way we live. While frameworks such as Hadoop have resolved the problem of storage, there’s still a burning need for effective processing of the humongous amount of data we handle daily.

In data processing, Data Science vs. Big Data vs. Data analytics are terms you’ll likely come across. Though these terms are related, there are still some variations in meaning and application in the digital world.

This article explains Data Science vs. Big Data vs. Data Analytics and their applications in many industries.

What is Data Science?

Data science is a career field that blends various tools, machine learning principles, and algorithms for data preparation, cleansing, and analysis. Through data science, humans can process and analyze insights from raw data.

What is Big Data?

Big Data refers to the large chunk of data, both structured and unstructured, pouring in from different data sources in various formats. Usually, these data cannot be processed by traditional applications, so modern and advanced systems are employed for efficient processing.

Large industries and big corporations benefit from the application of Big Data. The information and insights generated from Big Data can help in making better business decisions, improving operations, and discovering areas for improvements.

What Data Analytics?

Data analytics refers to the series of processes, systems, and technology for examining raw data in order to draw conclusions from the data.

Data Analytics cut across many fields, including cyberspace, marketing, and healthcare. Its application assists in uncovering facts, gaining insights, making comparisons, and drawing conclusions from the raw data.

The Need for Data Science

Different systems and technologies in many industries require the application of data science. Below are some of the fields where the knowledge of data science is indispensable.

Search Engines

Search engines use data science algorithms to process search queries and deliver results within seconds. For example, plugging in a keyword on any search engine such as Google will return thousands, if not millions of search results within a fraction of seconds. With the help of data science, search engines can process raw data of facts and transform them into needed information.

Digital Marketing

From door to door to Brick and Mortar sales, the digital marketing spectrum has revolutionized almost every old advertising model. With advancing data discovery, digital marketing solutions such as display banners and digital billboards have created a huge leap in business conversions. For example, digital ads attract higher CTR compared with traditional advertisements. 

Finance Industry

In the finance industry, various financial tasks are shaped and automated by data science. Applications such as machine learning enable financial firms to identify, monitor and prioritize tasks. 

Risk analytics, fraud detection, and management of customer data, which are parts of data science applications, also assist financial firms in making strategic business decisions.

Furthermore, with the help of data science, companies can optimize their production hours, keep tabs on energy costs, and work to improve the quality of their products. Even, technologies such as the Internet of Things (IoT) has enabled industries to effectively monitor systems, predict potential problems as well as analyze the continuous stream of data.

Role of Data Scientists

Data scientists are analytical data experts who can dig in and unearth insights from a large amount of data, information, and resources at hand. Previously they weren’t on many radars, but they have gained rapid popularity in almost every industry. A Data Analyst should be able to:

• Gather enormous data and transform them into a usable format.
• Work with a variety of programming languages such as SAS, R, and Python.
• Communicate and collaborate with both IT and business.
• Spot trends in businesses and interests as well as look for order and patterns in data.

Many scientists began their careers as data analysts or statisticians. Some took recognized and the best data science certification to boost their value in the marketplace.

Uses of Big Data

Big Data is used by large institutions like insurance firms, private wealth management, institutional investment banks, and credit card companies to run their financial services. It helps big businesses and organizations to process the humongous amount of multi-structured data residing in several disparate systems.

Listed below are industries where Big Data has proved of high importance.

Fast-food Companies

Fast-food companies like McDonald’s are big firms that process information from huge and various data sources. Designing marketing strategy, evaluating performance, and improving the quality of service all depends on the information of many customers.

Banking Industries

In the banking sector, big data can aid banks in cash collection as well as financial management. Through clustering techniques, for example, banks can make important decisions such as opening new branch locations where demands are high.

Applications of Data Analytics

Data analytics has found its way into organizations, corporations, and many more industries. Below are some uses of data analytics.

Hospitals

The challenge for many hospitals is how to treat as many as possible patients without compromising the quality of care. To solve this problem, instrument and machine data are employed to track and improve the efficiency of treatments given to patients and equipment used in the hospital.

Security Systems

Data analytics applications, such as predictive analysis, has been employed in most parts of the world with high crime rates.

In major cities like Los Angeles, Chicago, and London, availability of data helps when deploying police officers to such areas at certain times of the day, which has lead to a drop in crime rate.

Logistic Companies

Logistic companies such as FedEx, DHL, and UPS also make use of data analytics applications to improve their services. Through systems like GPS, these companies can find suitable routes for shipping and transportation as well as determine the best delivery time, amongst other uses. This helps in making strategic decisions that’ll boost cost efficiency and keep their business ruining.

Customer Interactions

Through data analytics applications, large organizations such as insurance firms can improvise on customer relations. With regular customer surveys, they can see what services are good and those that need improvement.

Data Analytics can also help determine the appropriate channel of communication. For example, some demographics may need to be reached via websites, phone, or simply by email.

Analyzing customer demographics with reference to feedbacks can help businesses improve on customer experience depending on customer behavior and proven insights.

Roles of Data Analysts

A data analyst gathers information from different data sources and interprets trends and patterns. Then, the Data Analyst will report back what has been found to relevant colleagues. A Data Analyst should hold a degree in either of the following as well have other related certifications as necessary:

• Mathematics,
• Statistics,
• Economics, or
• Computer science.

To further sharpen their skills, a Data Analyst can take a related, recognized, and the best data analytics certification for career development.

Now that you understand the roles and features of Data Science vs. Big Data vs. Data Analytics, which of the career path do you think, is more suited for you.