15 MARCH, 2022


What is data analytics?

There are multiple types of data analytics, but at its core, it describes the process of analysing raw data to identify trends, answer questions, find patterns, and draw conclusions.

The importance of data analytics in business

Data collection is on everyone’s lips at the moment, but data on its own is meaningless if there is no one to analyse and interpret what has been collected. Right now, unprecedented amounts of data are being collected across all sectors, from healthcare to software development companies. The purpose of data analytics is to make sense of all this raw data and turn it into something useful. But what can data analytics be used for? How does it benefit businesses, and what kind of skills does it entail?

Big data

Big data is another common buzzword in today’s economy, but it actually has enormous value when used correctly. With the sheer volume of data being collected, combined with the wide variety of data points and the fact that it is updated in real time, conventional data analytics is often not enough in many large businesses. That is why big data analytics uses more advanced tools, including machine learning, to prepare, store, and analyse the vast volume of data.

Business analytics

In business analytics, data analytics is used to make informed business decisions. The four main types of business analytics are descriptive, diagnostics, predictive and prescriptive analytics. Depending on business needs, data analytics can be used to identify what has happened, why it happened, what might happen and what courses of action need to be taken.

How does data analytics help businesses?

Data analytics can be used for a variety of purposes across businesses and industries. In today’s environment, it has become essential to business success, as shown by DeakinCo’s newly-published report The Business Return on Learning and Development, developed in partnership with Deloitte Access Economics, which found that data analytics is one of the top five in-demand skills named by business leaders in Australia.

Identifying business opportunities

One of the benefits of data analytics in business is the ability to identify opportunities that may have been otherwise overlooked. With much more granular market insights, your business may be able to identify untapped markets, spot new uses of a product or predict potential changes with more precision and rigour than by relying on human intuition.

Improving efficiency of processes

By making use of the ability to report on, diagnose, predict and prescribe courses of action, businesses can deploy data analytics in finding and eliminating inefficiencies in their processes. This could be in manufacturing, marketing, project management or even in settings like protocols for certain health interventions. With data analytics, organisations can pinpoint and analyse exactly where processes break down.

Responding to customer needs

According to a McKinsey article based on their survey of over 260 Customer Experience Leaders, predictive analytics is the future of customer experience. The ability to capture and track behaviours, sentiments and the social media of customers opens up new doors in responding to customer needs.

Forecasting growth

Being reactive instead of proactive can cause massive losses. Using data analytics for forecasting and modelling, a business can anticipate growth and plan strategically for it, reducing many risks associated with growth, such as staffing or product shortages.

Making informed business decisions

Data-driven decision making can give businesses the edge over their competition. Instead of relying on gut feelings, businesses can go through different future scenarios, complete precise risk assessments, and base their decisions on hard data.

Where is data analytics used?

While the domain of data analytics was once mostly claimed by the financial sector, it is now ubiquitous and used across many industries and applications, including:

Finance and banking

Financial institutions use data analytics for market analysis, predictions and risk assessments. While traditional analytics have been used in this sector for a long time, more advanced analytics are now also being implemented.

Business intelligence

The insights gained in data analytics can be applied in business intelligence systems. The two disciplines are closely linked, with data analytics providing the information that the decision making and analysis processes of business intelligence can then build on.

Information technology

In the IT industry, data analytics is absolutely integral. It is unsurprising that businesses focused on developing IT solutions are at the forefront of using innovative data analytics tools.


Marketers rely heavily on analytics to plan, assess and adjust their strategies as they can gain a full picture of consumers through the wealth of data collected.

Consumer goods

Consumer goods manufacturers use analytics to spot gaps in the market, improve their products and eliminate inefficiencies in manufacturing, delivery, and other internal processes.


Healthcare providers amass enormous volumes of data on their patients. Using predictive modelling, past analysis and other techniques, they can improve outcomes and processes as well as cut costs.

Construction and property

In recent years, the construction and property industries have been making use of data analytics to improve workflows, increase site safety and conduct risk assessments.

What data analytics skills do employees need?

The skills needed for data analytics range from technical skills to soft skills and include:


Programming skills are essential for data analytics. Languages used include Python, which is an excellent all-rounder programming language, R, which can handle complex data and MATLAB, which can be used for machine learning and big data applications.

Quantitative skills

Statistics and mathematical skills like algebra and calculus are vital basic skills for any data analyst.

Data visualisation

Data isn’t worth much if it can’t be interpreted, which is why data visualisation is such an important skill for effective analysts.

Data cleaning

A huge part of analytics is data cleanup, because the better prepared the data is, the more insightful the results from an analysis will be.

Database programming 

In addition to programming languages, database languages like SQL and NoSQL are crucial for employees to hit the ground running in data analytics.

Big data analytics

As big data isn’t usually stored in data warehouses, knowledge of big data processing frameworks and systems like Hadoop is essential for working with big data.

Cloud computing

In the field of big data analytics, cloud computing is becoming increasingly vital as it allows accessing and processing larger data sets.

Critical thinking 

The benefits of critical thinking skills in the workplace are well-known, and they will enable data analysts to critically reflect on and question outcomes and data.


While the list of technical skills is long, data analysts also need soft skills. Analysts need to be able to clearly communicate their findings in order to translate them into real-world benefits for the organisation.

How to develop data analytics skills in your organisation

By following a well-planned corporate training strategy, you can develop data analytics skills within your organisation rather than having to go on the hunt for new talent.

1. Start at the top

First, you need buy-in and support from the top levels of the organisation. Ideally, leaders will also commit to increasing their data literacy and perhaps even upskill.

2. Illustrate the applications of data across the organisation

To get the whole organisation on board, you will need to demonstrate how data is used and the difference efficient data analytics could make across the organisation.

3. Define a minimum level of competency

Setting standards will allow you to take a systematic approach for promotions, shifting roles and will give everyone a benchmark to reach, which can be motivating.

4. Tailor learning initiatives to each employee’s role

It is likely that not everyone in the organisation will need to be able to understand what the difference between SQL and NoSQL databases are, so ensure that everyone is getting an appropriate level of support and access to knowledge, without overloading individuals unnecessarily.

5. Track key performance indicators for learning

Data is everywhere, including in learning and development. Tracking your organisation’s progress through KPIs will enable you to understand the return on investment into your employees’ data analytics skills.

Start developing your team’s data capabilities

DeakinCo. can help you develop your team’s data capabilities by offering courses in technical skills, including in data analytics, as well as soft skills like digital literacy and critical thinking.

Reach out to DeakinCo. and learn more about how we can help you develop your team’s data capabilities.

Talk to us today