The world is awash with data.

Apr 22
4 min read

The world is awash with data. As of early 2026, the world generates approximately 402 to 496 million terabytes (TB) of data daily. And it needs data professionals to organise and analyse all that information.

And this massive volume is expected to rise sharply, with predictions indicating that global data generation will reach roughly 230 - 240 zettabytes (ZB) in 2026.

Note: 1 Zettabyte is equal to 1 trillion Gigabytes.

Key Data Creation Drivers in 2026

Daily Velocity: Approximately 403 million terabytes are created daily.

Machine-Generated Data: A significant portion comes from IoT sensors, connected cars, and industrial machines, with 29.3 billion networked devices already active.

AI and Cloud: Rapid adoption of AI/ML, cloud storage, and video streaming are major contributors.

User Activity: Social media and internet usage (6 billion+ users) add to the total, with roughly 3 billion photos and 500 million tweets shared daily.

Digital Economy: A vast majority of the world's GDP is digitalized, pushing more activity into data-heavy cloud systems.

The Data Landscape

Cumulative Data: The total volume of data created is projected to reach 181 – 200 zettabytes annually by the end of 2026.

Data Storage: While much data is transient, it creates immense pressure on storage infrastructure, with the global cloud storage market predicted to grow massively.

Data Velocity: The world's data is estimated to double in volume every two years, an exponential increase from previous estimates.

Looking for a career in data? You won’t have to look very far. Data careers have become one of the fastest growth fields over the last 10 years, driven by widespread digital transformation, a mass cloud migration (which was then supercharged by COVID-19) and more sophisticated listening tools. In short, the world is awash with data and it needs data professionals to organise and analyse all that information.

1. Data Analyst

Data analysts gather and organise data sets, and then – this is the important bit – analyse that data to inform the wider business and optimise its processes. This could be anything from user traction on social media, to purchasing trends on an e-commerce site, or how to improve customer retention through loyalty programs. Data analysts are curious by nature. They want to know the why behind the numbers, and then communicate those findings to stakeholders within the business. You can become a data scientist with a bachelor’s degree, or even an online diploma.

Average salary: $95k

Source: SEEK

2. Data Engineer

Data engineer is a slightly more advanced data career, and usually involves a master’s degree, or a bit of experience working as a data analyst, or both. While data analysts deal with data acquisition and processing, data engineers actually build the algorithms and architectures that capture that data in the first place. They need a solid understanding of programming, statistical methods, and SQL. Some machine learning and ETL knowledge wouldn’t go astray, either.

Average salary: $125k

Source: SEEK

3. Data Scientist

Data scientists and data analysts perform similar roles, but there is a fine distinction. While analysists examine and collect data, scientists are more concerned with creating the framework and operational models for that analysis to occur. This usually involves a lot of statistical models and algorithms, running tests, developing products, and optimising data collection frameworks. In-depth programming knowledge of SAS, R and Python will also come in handy.

Average salary: $125k

Source: SEEK

4. Business Analyst

Business analysts use data techniques all the time, but their focus tends to be on business processes and efficiencies. Figuring out how to get the most out of teams, where the gaps are, or creating financial models to support business decisions. They tend to work in Information Technology departments, but they move around the business a lot, talking to stakeholders and leading project teams. You’ll need a good knowledge of basic data skills, SQL, Excel and enterprise architecture.

Average salary: $115k

Source: SEEK

5. Machine Learning Engineer

Machine learning engineers are essentially programmers. However their primary focus is building algorithms, models and frameworks to allow ‘machines’ to learn and work independently. They’re basically AI architects. Data scientists might build some models, but machine learning engineers have to transform those models into actual code, and then scale that code for production. This is a massive growth field. You’ll need to be fluent in languages like Java and Python, with sprinkling of computer science, maths and statistics, too.

Average salary: $115k

Source: Indeed

6. Data Architect

Think of data architects as the creators of data structures. Instead of physical buildings, however, data architects map out the frameworks and warehouses needed to acquire, organise, store, analyse and – most important of all – use data. They’ll often work closely with data engineers for this purpose, organising how data flows through an organisation. Where to store it. How to access it. And how to protect it. Most data architects have a post-graduate data degree, and you’ll need a solid knowledge of data mining, Python and Java, machine learning and SQL.

Average salary: $170k

Source: SEEK

7. Marketing Analyst

Everyone works in data now, and that’s especially true in marketing. Marketing managers are expected to have some basic data skills, but the real heavy lifting (outside Google Analytics) is still mostly done by marketing analysts. These are essentially data analysts with a marketing skew. They help companies understand their customers better. How they think, and how they purchase. Marketing analysts conduct a lot of market research, and use data to help inform and design marketing strategies. Communication is a big part of this role, so you’ll need to be able to visualize data patterns, and then translate those patterns for internal stakeholders.

Average salary: $105k

Source: SEEK