What Is Data Science, and What Is It Used For?

What if your data could give you a competitive edge? Data science transforms raw data into powerful insights to grow your business.

Modern businesses collect an enormous amount of data, but without the tools and processes to analyse and explore it, gathering insights that drive organisational growth becomes impossible. Data science is a way of making sense of the chaos.

Data science combines different fields, such as mathematics, statistics, AI, machine learning, and predictive analytics. Its goal is to make sense of data and help businesses make intelligent decisions, predict trends, and plan ahead.

In this guide, we’ll examine data science in greater depth, its benefits, and how modern Australian businesses use it to achieve success. First, we’ll clarify the difference between data science, data analytics, and business intelligence (BI).

Business intelligence, data analytics, and data science all revolve around data but differ in their approaches, methodologies, and goals.

Data science is about using big, complex data to make predictions and help businesses make better decisions.

Data analytics focuses on looking at past data to answer specific questions and give businesses clear, helpful insights to improve their work.

Data analysis is actually part of data science. Often, data scientists also do the work of data analysts.

First, we should clarify that business intelligence (BI) isn’t really a process in the technical sense. It’s a set of applications and technologies that businesses can use to derive meaning from data and make smarter decisions.

The main differentiator between BI and data science is that BI is focused on past data, while data science uses descriptive data to predict future outcomes. Data science is also broader. It covers more of the data lifecycle, whereas BI is focused on extracting takeaways.

Another point of difference is that data analytics turns raw data into insights. BI, on the other hand, uses those insights to inform decision-making. Both come at different points during the data science lifecycle and serve different roles.

You’ll typically see data science techniques applied in four different ways.

Descriptive analytics looks at historical data to show businesses what has happened. A typical workflow includes techniques such as:

• Data aggregation: Unifies siloed data that are collected through multiple sources.
• Data cleaning: Removes and fixes data errors and inconsistencies.
• Data segmentation: Divides data into groups based on criteria that the business sets.
• Data visualisation: Presents data in charts or graphs making data easier to visualise and understand.
• Statistical analysis: Using different mathematical models to analyse data.

Descriptive analysis is the most basic data science approach - it only answers the ‘what’ based on historical data rather than the ‘why’ or ‘how.’

Consider how a gym monitors attendance to find which time of day is the busiest. This monitoring comes under descriptive analytics, which only answers what is happening, not why it’s happening.

Diagnostic analysis takes the descriptive analysis one step further by examining the ‘why’ behind past events. This can often include:

• Data discovery: Finds patterns by exploring data.
• Drill-down analysis: Looks for takeaways by breaking data into more detailed parts.
• Root-cause analysis: Identifies the underlying cause of a problem.
• Correlation analysis: Looks at how different variables relate to each other.
• Hypothesis testing: Tests an assumption or theory based on data.

This approach looks at the underlying causes behind trends. For example, you could be looking to investigate why something is happening to determine the underlying cause and understand a trend better.

Predictive analysis is even more advanced. It uses the what and the why of historical data to look at what could happen in the future. To distil that further, it takes past events and turns them into predictions. That said, here are some of the most common ways it’s being used:

• Predictive modelling: Predicts future events through historical data.
• Regression analysis: Predicts values by looking at the relationship between variables.
• Clustering: Groups similar data into clusters.
• Machine learning: Trains algorithms to learn from data and make decisions
• Forecasting: Uses historical data to predict what could happen in the future.
• Pattern matching: Find trends by looking at recurring patterns.

As you can see, it’s all about using the past to predict the future. It’s how a weather analyst can forecast an upcoming hurricane using climate data from the previous decade, for instance, or the way that an e-commerce platform uses past sales to predict seasonal demand.

Next, we’ve got prescriptive analysis, which takes the forecasts from the predictive methods and uses them to work out the best decision to make in context. Some of the most common techniques involve the following:

• Simulations: Let scientists create models that they can use to test theories.
• Scenario planning: Using future forecasts to plan ahead.
• Decision analysis: Assess available options to land on the best decision.
• Optimisation modelling: Finds the best solution based on objectives.
• Neural networks: Using algorithms to recognise patterns and make predictions.

For example, companies use predictive analysis to decide how much stock and staff they need during periods of high demand.

One of the most overlooked and most important benefits of data science is its ability to bring together scattered data from all across an organisation. It gives a complete view of businesses and customers so organisations can make decisions safely with the knowledge that they have the complete picture rather than just a fragment of info.

Another core benefit is the opportunity to produce valuable takeaways. Data equals insights. With the evidence behind them, companies can be more confident they’re making the right choice. This not only leads to more accurate decisions but also speeds up the whole process by removing uncertainty.

These benefits help businesses create fresh strategies and make rapid decisions backed by proper evidence. That equals superior customer experiences and improved profits over time.

The data science lifecycle is really just a linear collection of stages that any data science professional will follow to solve problems. This is how it works:

The first step is to aggregate (unify) unstructured and structured data so it’s all in one place. Companies use techniques like web scraping, API integrations, and sensor data gathering to do this.

This data can come from anywhere in the business’s tech stack: internal databases, CRM software, point-of-sale systems, social media, and Internet of Things (IoT) devices.

Once you’ve got your data, it needs a little TLC to get rid of the errors and make sure it's ready to use. You need to standardise the data to make sure it lines up with your business’s preferred format. A good start is to fix basic spelling and grammar problems as well as remove duplicates and anomalies where needed.

The goal here is to process data so that it’s easily recognisable and in a usable format. Data integration tools like ETL (extract, transform, load) are often used to simplify this aspect.

Once the transformation stage is complete, the data can be loaded into a data warehouse or data lake to hold it until it’s ready for exploration.

The next step is to use data analysis to build a basic understanding of the info. Data scientists will look for patterns to inform and refine their hypotheses.

This step also helps plan and create models for ML and predictive analytics. In essence, this descriptive analytics stage prepares the ground for deeper analysis.

Now that they have a preliminary understanding of the data, the experts can use data science tools and ML algorithms to explore the ‘why’ behind patterns and predict future outcomes so they can determine the best course of action.

This stage often involves training and iterating models repeatedly to gain more reliable outcomes.

The data scientists will then transform any gathered insights into an action plan that helps a business solve problems and achieve desired outcomes. This stage often means using charts and diagrams to visualise data so trends are easier to spot.

The final step is communicating the findings, such as gathered insights, to key stakeholders and helping them implement the best approach.

In most cases, this stage focuses on putting the data in context to help decision-makers understand the findings and how to use them for business growth.

Data science revolutionises every industry by helping businesses uncover insights and make more intelligent decisions. Its ability to analyse large datasets and predict trends transforms how businesses operate.

Here are five unique use cases for data science across different industries.

Businesses use data science in the travel industry to enhance customer care and personalise engagement across marketing and sales channels.

Australia’s Intrepid Travel, for instance, unified its customer data within Salesforce Data Cloud and identified travellers interested in India. It goes to show how analytics can drive meaningful action in tourism.

Data scientists help hospitals manage resources during peak times by deploying AI and ML models in the healthcare sector.

Ramsay Health Care’s data teams centralise patient records and historical admission information so they can then build forecasting models to anticipate peak hospital admissions. This lets them schedule doctors and nurses more effectively while ensuring the right people are available at critical times.

Ramsay can now use machine learning forecasts to provide evidence rather than relying on intuition to make sure patients get the best possible care. This saves lives while also cutting operational costs for the hospital as the model ensures optimal use of the available resources.

National Australia Bank (NAB) has a creative approach to predictive analytics that allows them to tackle fraud detection and credit risk.

NAB’s AI systems analyse data like transaction history and market conditions to find unusual patterns. They detect if a user spends more than usual and flag the transaction as unusual. A human analyst can decide what to do next. AI, paired with human insights, leads to faster response times and better accuracy.

Woolworths collects sales data in real-time from different data points — like its online shopping portal and in-store scanners. They can then use that data to work out which products will be the most popular at different intervals — such as a week or month ahead.

How does this help? Well, half the retail battle is trying to optimise supply chain orders to make sure there’s enough stock in store to cope with demand but not too much that you’re wasting money. By looking at the data along with other metrics like past trends and promotions, Woolworths can work out how to stock their shelves and how many staff to put on shift to handle busy days.

Being a large company, it isn’t surprising that Woolworths collects a large amount of real-time sales data. However, the way they use it sets the standard for the industry. We believe machine learning in data science can help retail businesses in a few ways:

• Reducing the amount of waste that comes from overstocking
• Improving customer satisfaction by making sure you always have the right amount of stock and staff
• Increasing sales by forecasting demand for busy periods

The City of Gold Coast is taking a unique approach to urban planning by adopting AI business analytics to create a smarter city. The team's data scientists aggregated traffic flow information, population forecasts, and citizen feedback into a central dashboard.

Diagnostic analysis pinpoints why certain areas experience frequent congestion; prescriptive models propose solutions. Solutions include expanding bus routes or improving bike lanes for users.

By acting on these detailed insights, city officials aim to use data to enhance liveability for residents. It’s an ideal example of how data science can fuel intelligent decision-making even at the scale of large public projects impacting millions of people.

By examining these use cases, we see data science in action, collecting raw business information, analysing it for trends and anomalies, and then turning those insights into strategy.

Data scientists usually aren’t directly responsible for every aspect of an organisation’s data strategy, and they typically have a broad influence on overall activities.

A data analyst generates insights from historical data and often helps design the processes and procedures for this analysis. They’ll also define the overall requirements for these systems.

A data scientist oversees every aspect of a business’s data flow, this can include things like:

• Defining problems
• Data ethics and legislation
• Data mining and data cleansing
• Analysing and exploring data sets
• Building models to generate deep data findings
• Visualising insights with charts and graphs
• Communicating findings to stakeholders

In this sense, a data scientist orchestrates the bulk of a business’s data activities. They may not specialise in every area, yet they typically have a broad understanding of multiple disciplines. This lets them manage and advise on all aspects of the data science lifecycle.

Unsurprisingly, data science skills are highly sought after, which is why you’ll often hear the role touted as the most in-demand job of the 21st century.

Traditionally, data science required extensive on-premises hardware, which limited smaller organisations because it was too expensive and hindered business growth as it was too difficult to scale.

Cloud services have revolutionised the data science lifecycle by making it easier for businesses to store and access vast amounts of customer data, perform advanced data analytics, and extract insights from enormous datasets.

Aside from the apparent benefit of scalable data storage, many cloud computing platforms offer integrated AI and ML services. This allows businesses to deploy custom machine learning models and gain AI-powered insights into every aspect of their operations.