Types Of Big Data (With Importance And Career Options)

By Indeed Editorial Team

Updated 21 August 2022

Published 13 December 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

The term big data is used to define data that is massive in its size and complex in its nature. These datasets are so voluminous that companies often use smart analytical applications to process them for various purposes like gaining insights into revenue growth. Learning about big data can help you understand its various aspects and decide if you want to pursue a career in this field of technology. In this article, we outline the common types of big data, review their importance and explore some popular career options in this field.

Related: What Is Big Data Hadoop? (Definition And Career Opportunities)

Types Of Big Data

The main types of big data are as follows:

Structured data

Structured data can be the easiest to work with since you can find it stored in fixed fields within a record, guided by a particular schema that makes the data possess similar properties. Structured data can be available to you in the form of rows and columns, which you can process, examine and store in a fixed format. You can consider the data of an e-commerce website or a company's payroll database as structured data.

Unstructured data

Unstructured data lacks a particular form or a definite schema. Its arrangements are often unsystematic and unplanned. This data can come in the form of a text file, image or video. You can find this type of data on the Internet, as it can include activities on various social media platforms or content on a website. Examples of unstructured data can include a text you send to a friend, a video you watch on a video-sharing website or a picture you add to an online photo-sharing platform.

Semi-structured data

Semi-structured data can appear to be structured or unstructured data, and it is hard to categorise this type of big data. You may not find this type of data stored in a traditional database format, but it can contain various properties that can allow you to categorise it. You can process semi-structured data using tags or keywords, which can help you separate important components of the data from the inconsequential information in the datasets.

Related: Top 20 Big Data Tools: Big Data And Types Of Big Data Jobs

3 V's Of Big Data

Besides the types mentioned above, there are a few properties that can further define big data. Here are the three main V's of big data:

1. Volume

Volume in big data refers to the amount of data that originates from online portals like websites and digital applications. The world generates a few quintillion bytes of data in a single day. Some companies store terabytes or petabytes of data in data warehouses and on their servers.

2. Velocity

Velocity defines the speed at which the voluminous data is being created. Many companies are creating large amounts of data fast. For example, social media users are uploading content like photos and videos which is resulting in the expansion of both structured and unstructured data at an incredibly fast speed.

3. Variety

When huge amounts of data are being created at an astonishing speed, it results in a wide variety of data like the information in spreadsheets and media on the Internet. The variety can range from text messages to satellite images. With variety comes a requirement to process this data and classify them into different categories so that companies can decipher, manage and utilise the data to their advantage.

Related: 10 Characteristics Of Big Data And How You Can Use Them

Why Is Big Data Important For Organisations?

Companies often record, measure and process big data to perform competitively in a marketplace. Top executives of organisations often work with information technology experts to devise ways to monetise this data. For example, you purchase a new smartphone from a retailer, who then shares your information with a mobile repair shop, and this shop can get business from you through targeted online advertisements. Below are some ways in which organisations can use big data to improve their business:

  • Understand what, where, when and how customers buy

  • Provide tailored services using information like name, age and location

  • Generate cross-selling and upselling possibilities for their business

  • Display targeted promotional offers using online advertisements

  • Forecast a user's needs and provide services at the right time

Related: 18 Big Data Examples (Common Uses In Different Industries)

How To Make A Career In Big Data?

Here are a few steps that you can take to make a career in big data:

1. Complete school education

The first step that you can take is to complete your higher secondary education (12th grade) from a board recognised by the government. Since big data is more about your analytical and technical skills than the overall educational qualifications, some hiring managers may seek accomplishments in the field of information technology. For that, you can start with learning computer subjects offered by your school. These subjects can include programming languages like Structured Query Language (SQL) or Python.

2. Earn a bachelor's degree

After completing your school education, you can pursue a bachelor's degree in computer science or data science. You can also consider pursuing a bachelor's degree in information technology or a related field. This can help you build a strong technical knowledge base which can prove to be rewarding going forward in your professional journey.

3. Pursue additional courses

Once you have completed your school and college education, you can consider pursuing a certificate or diploma course. These courses offer an advanced understanding of a subject and can help you build an extensive set of technical skills. Many courses in data science aim to teach topics related to the tools and libraries used by professional data scientists and how to work on datasets and evaluate machine learning models.

Related: 42 Common Big 4 Interview Questions (And Sample Answers)

4. Complete an internship

Completing an internship before getting a full-time job can offer you firsthand experience in your field. An internship with a technology firm can help you build your data science skills and get familiar with its various intricacies. This exercise collectively can equip you with the know-how of deep learning, data analysis and programming alongside gaining confidence and creating an impression on future hiring managers.

5. Build a portfolio and apply for jobs

Once you gain experience working as an intern, you can take up freelance projects and add those to your portfolio. Make sure your portfolio highlights your educational journey and professional experience. After building a robust portfolio, you can start applying for full-time jobs.

Related: How To Become A Data Analyst: A Complete Guide

Top Career Options In Big Data

Here are some of the popular types of careers you can pursue in big data:

Big data engineer

A big data engineer is in charge of the data pipeline of an organisation. They design, process and manage the company's data at a petabyte-scale. They employ various methodologies to ensure data quality and data integrity. Their core responsibility can be finding solutions to problems that arise out of large-scale data processing.

Related: How To Become A Big Data Engineer (With Essential Skills)

Data architect

A data architect collaborates with internal product teams to explore and create practical approaches to meet a company's data management objectives. They design databases and give them a structure using relevant data models to help a company strengthen its data analytics alongside ensuring accuracy, accessibility, scalability and performance of the company's data.

Related: 6 Important Big Data Developer Skills (And Popular Roles)

Data scientist

A company can hire a data scientist to restructure and transform its data for big datasets that are interconnected. They make use of machine learning and statistical modelling to create novel and insightful products. Data scientists use deep learning and machine learning to optimise the efficiency of the company's data.

Data analyst

A data analyst develops and maintains machine learning or artificial intelligence algorithms and integrates them into a company's data pipeline to forecast various risks. They analyse big data to discover market trends to boost a company's growth. They prepare in-depth reports on analysis, validation and processing of data alongside developing automation tools.

Database manager

A database manager manages a company's database infrastructure using various programming languages like SQL. They make sure that the databases perform efficiently and do not encounter stability issues. A company can require them to design a database that is unique, consistent and durable in its design and operation.

Data modeller

A data modeller carries out an extensive analysis of a company's data and aligns it with the company's goals, objectives and needs. By analysing the big data, they propose solutions that generate quantitative and qualitative business insights. A company may also require them to collaborate with analysts to produce data visualisations that are visually pleasing.

Related: What Is a Data Modeller and What Do They Do?

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.

Explore more articles