Defining the Data Puzzle: Data Science vs. Big Data vs. Data Analytics & More

Article Highlights

• Data analytics explains what happened; data science predicts what will happen.
• Big data provides the infrastructure for massive scale.
• Machine learning offers the predictive algorithms.
• Artificial Intelligence (AI) is the broader pursuit of simulating human intelligence.

I am often frustrated by the sensationalist use of terminology in data science. A sentiment I saw on a social platform summed it up well:

• When you're fundraising, it's AI (Artificial Intelligence).
• When you're hiring, it's ML (Machine Learning).
• When you're implementing, it's linear regression.
• When you're debugging, it's print.

As someone with a background in operations research, I am also familiar with this joke:

Q: What is the difference between an operations researcher and a data scientist?
A: About $40,000 per annum.

(According to 2024 BLS data, the top 10% of Operations Researchers and Data Scientists earned $159,280 and $194,410, respectively).

Data science can feel like it's putting shiny new labels on dusty old concepts. This results in confusion for those who are new to the field, and even for seasoned professionals. To build effective teams and deliver business value, we need clarity.

A Rose by Any Other Name...

The following terms often appear to be used interchangeably:

• Data analytics
• Data science
• Big data
• Machine learning
• Artificial intelligence (AI)

Strangely, "statistics" or "operations research" are rarely mentioned in the same context. French mathematician Henri Poincaré said that mathematics was the art of giving the same name to different things. He might have made the same observation about data science today.

In this field, labels matter. The five areas mentioned above cover a huge amount of conceptual ground. Organisations need to know what capabilities best serve their needs and how to recruit people with the appropriate skills. If you recruit experts in logic-based AI and set them to work on d3 dataviz projects, the results will probably be sub-optimal.

The sheer scale of data makes this clarity even more critical. It was estimated that in 2025, the world generated around 474 exabytes of data per day. To put that into perspective, if you printed all that data into standard paperback books, the stack would reach from the Earth to the Sun and back over 25 times. The 2026 figures are even higher, on track to generate 630-657 exabytes daily.

Let's attempt to define these terms to bring some order to the conversation.

What is Data Analytics?

Data analytics is the science of examining raw data to draw conclusions about the past or present. It is typically used for day-to-day operations, reporting, and dashboarding.

Data analytics is the most common use of data in organisations. It produces things like daily sales figures, revenue by department, and inventory reports. Data analysts often extract data from relational databases, like SQL Server and Oracle, and present them as reports and corporate dashboards. Data analysis is generally about understanding what has happened or is currently happening in the organisation.

Desirable Skills for a Data Analyst

Data analysts should be able to:

• Manipulate databases using SQL.
• Use dashboard tools and understand how to design effective organisational dashboards.
• Utilise statistical tools to ensure that poor data, such as biased samples, do not filter through to decision-makers.
• Produce effective, clear charts that inform, rather than confuse.
• Take a wider interest in the business to suggest what data might be useful.
• Provide a bridge between management and database administrators (DBAs).

What is Data Science?

While data analytics focuses on the past and present, data science is largely about predicting the future. Data scientists look to uncover patterns, which often leads to more assumptions than are required in data analytics. They must get used to most of their explorations ending up in blind alleys. Data science projects typically combine well-structured relational data with disparate, messy, unstructured data, including sources such as customer feedback or third-party datasets.

Desirable Skills for a Data Scientist

Data scientists should be able to:

• Use relatively advanced statistical tools and methods.
• Program in at least one data science language, preferably Python, then R.
• Extract and manipulate data from diverse sources like relational and non-relational databases.
• Clean and transform data between different formats, often called ‘data wrangling’ or ‘data munging’.
• Use machine learning methods for tasks such as clustering, classification, and regression, employing algorithms such as random forests where appropriate.
• Produce custom visualisations using tools like ggplot2, Matplotlib, or D3.js.
• Explain complex, statistical analyses to non-technical decision-makers.

What is Big Data?

Big data refers to data that is difficult to store, process, or analyze using conventional tools, due to characteristics such as:

• Volume — the sheer size of the data
• Velocity — the speed at which data is generated, ingested, or must be processed
• Variety — the range of formats and structures in which the data is represented
• Variability — fluctuations in data rates, structure, or meaning over time.

The boundaries are constantly shifting; it is now possible to spin up a machine with 1TB of RAM in the cloud, so workstations can handle fairly hefty loads. Big data provides the data that other areas require to sustain them. Apache Spark is now generally the preferred big data platform for data scientists, with a powerful machine learning library (MLlib) that makes it easy to perform analyses on massive datasets.

Desirable Skills for a Big Data Specialist

Big data specialists should be able to:

• Operate and manage clusters of networked computers.
• Maintain high availability of the cluster.
• Understand cybersecurity issues related to securing a cluster of computers.
• Program using enterprise languages, such as Java and Scala.
• Tune their chosen big data platforms to ensure and maintain performance.

What is AI?

AI has been a fixture of computer science research since the 1950s, cycling through phases of optimism and scepticism. Today, it is experiencing a surge in real-world impact. These technologies have rapidly moved from research labs into products and enterprise workflows, shifting AI from theory to practical toolset.

This transformation is not just technical. The AI field has seen the emergence of new job roles between 2022 and 2026 as organisations adapt to the pace, power, and implications of AI systems. Organisations now recognise that successfully deploying AI is as much about responsible governance and effective collaboration as it is about technical capability.

Newly Emerged AI Roles and their Skillsets

• AI Ethics Specialist: Focuses on aligning AI practices with legal, ethical, and societal standards (e.g., EU AI Act, ISO 42001). Requires deep understanding of governance, risk assessment, and policy writing.
• Prompt Engineer: Designs and refines prompts to guide and elicit high-quality responses from large language models. Requires expertise in prompt experimentation, understanding model behaviour, and working within model limitations.
• AI Product Manager: Coordinates cross-functional teams to launch AI-driven products. Requires strong communication skills and experience managing the full AI product lifecycle.
• AI Explainability Analyst: Develops methods for interpreting and auditing model logic. Requires expertise in frameworks like SHAP/LIME and the ability to translate model behaviours into business insights.
• AI Security Architect: Protects AI infrastructure against adversarial threats and data leakage. Requires proficiency in securing ML pipelines and understanding compliance requirements.
• Human-AI Interaction Designer: Optimises user interactions with AI systems for trust and usability. Requires knowledge of cognitive ergonomics and human-computer interaction (HCI) principles.

What is Machine Learning?

As ML is a large and complex area, a general data scientist likely will not have deep knowledge of its techniques and tools. Organisations with novel requirements may turn to experts in a particular branch of ML, like deep learning. Designing and tuning ML systems can require significant specialist experience. The tooling around many ML approaches, such as TensorFlow for deep learning, can take time to master.

Desirable Skills for a Machine Learning Specialist

Machine learning specialists should be able to:

• Provide deep expertise in one or more modelling techniques.
• Understand the statistical basis of the algorithms they use.
• Write programs in popular ML languages, such as Python.
• Exploit fast processing technologies such as GPUs and FPGAs.
• Tune model "hyperparameters" to improve predictive accuracy.
• Identify where ML tools will be effective.
• Work closely with data scientists to ensure ML technology delivers results.
• Advise on how models can be moved from research to production.

Here's what I mean

Ultimately, it does not matter where you draw the boundaries between these terms, or even what terms you use. It does matter that you have some terms, with clear, discrete definitions, and that you use them consistently in your organisation.

As in all human endeavours, language matters. It is especially important to attempt to be clear in areas where there is considerable pre-existing confusion. Adopting a shared vocabulary allows teams to collaborate effectively and helps organisations build the capabilities they need to succeed.

Frequently Asked Questions

Q: What is the main difference between a Data Analyst and a Data Scientist?

A: A Data Analyst typically focuses on descriptive analytics—explaining what happened in the past using reports and dashboards. A Data Scientist focuses on predictive analytics—using statistical models and machine learning to forecast future trends.

Q: Is Machine Learning the same thing as AI?

A: No. Machine Learning is a subset of AI. AI is the broad concept of smart machines, while Machine Learning is the specific application where machines learn from data to improve their accuracy over time without being explicitly programmed.

Q: Why is Big Data important for Enterprise AI?

A: Big Data acts as the fuel for Enterprise AI. Without the massive volume, velocity, and variety of data provided by Big Data infrastructure, AI and Machine Learning models would not have enough information to learn effectively or make accurate predictions.