The 4 Types of Data Analytics that Benefit Business

There are 4 main types of data analytics used for business intelligence. Find out what they are, why businesses value data analysts, and what kinds of careers and salaries you can expect in this industry.

If you’re looking for a career that’s full of promise, you have to dig deep. As in, you have to dig deep into data. Data scientists help businesses perfect their strategies by looking beneath the surface of data analytics, tracking and forecasting data trends. It’s a tactic that’s guaranteed to benefit any business, regardless of what they’re selling. And that’s why every business is desperate for data experts. 

Data experts are in high demand all over the world and Australia is no different; the average annual growth rate in the data analytics industry is 2.4% stronger than the 1.5% annual growth forecast for the entire Australian labour force. So, if you’re looking for a career that’s guaranteed to grow along with you, data science definitely won’t let you down — coming into 2021, the Australian Data Analytics Market is projected to grow a further 20%. 

With career growth that promising, the industry is definitely worth the jump — but what exactly would you be jumping into? Data science might seem like a lot to navigate from a distance, but up close, data analytics can be surprisingly straightforward. By learning the basic 4 types of data analytics, you’ll become hot property in business intelligence.

While the term “data analytics” might sound like jargon at first, it’s actually easy to explain if you bring it back to basics. Simply put, data analytics is technical data, analysed. Businesses use data analytics to develop actionable insights by evaluating the successes and failures of their strategies to make better decisions in the future.

The information used in data analytics can be taken from internal and external data sources. It can also be used in different sizes and formats, depending on how much raw data a business is prepared to analyse. A large amount of data used at once is referred to as ‘big data’. 

The reason data analysts are in demand everywhere is simple; if data analytics are implemented well, it’s incredibly profitable. In 2019 alone, Australia made just over $765 million in revenue from big data analytics and business analytics. So from a business point of view, it’s a strategy that’s worth investing in.

The reason data analysts are in demand everywhere is simple; if data analytics are implemented well, it’s incredibly profitable. In 2019 alone, Australia made just over $765 million in revenue from big data analytics and business analytics. So from a business point of view, it’s a strategy that’s worth investing in.

The best part? The fat profit margins in data analytics for businesses translates into a great payoff for data analysts, too.  The salaries in this industry are growing every year. The more experience you have, the more you’ll make. But even as in an entry-level career, the salaries are above average.

There are four different types of analytics that most businesses use: descriptive, diagnostic, predictive, and prescriptive. Within these four types, descriptive is the most simplified form of data analytics, while prescriptive is the most complicated.

In the data analytics world, the more complex it is, the more benefits there will be.

There are benefits to every type, but there are guaranteed benefits to incorporating more analytics rather than less. Incorporating each form of analytics will be sure to improve your analysis overall.

Descriptive data analytics is the first step in data analysis. In descriptive analytics, the analyst has to describe the situation: what is happening to the data?

Some examples include:

• Summing up past events according to historical data
• Reporting on trends over time
• Recording which data is accessed most often
• Determining the length of time data is accessed

Data analysts use standard aggregate functions (such as counts and aggregates) to assess descriptive data, allowing them to determine how a business strategy has succeeded or failed. Descriptive data analytics also allows analysts to assess trends via Google Analytics, condense big data analytics into accessible information, and more.

Are you interested in improving your data skills but not feeling ready to jump into a data analytics career just yet? Brushing up on descriptive analysis is your best bet. This is the most widely used form of data analytics, so it’s valuable knowledge regardless of where you’re working.

Diagnostic data analytics is the second step in data analysis. In diagnostic analytics, the analyst has to diagnose the situation: what has caused this to happen to the data?

Some examples include:

• Responding to anomalies
• Comparing consumer responses
• Establish the causes behind website rankings
• Determining relationships between different forms of data

Data analysts use training algorithms, principal components analyses and sensitivity analyses to analyse diagnostic data. These tools allow them to establish an in-depth understanding of the “why” behind the successes and failures of a business.

Prescriptive data analytics is the fourth step in data analysis. In prescriptive analytics, the analyst has to prescribe and advise the business with what they should do next: what does my business need to do in light of these data analytics?

Some examples include:

• Optimising product assortment
• Reducing unnecessary costs
• Developing better strategies for marketers
• Eliminating unnecessary tools

Data analysts use machine learning, statistics, and operations research to analyse prescriptive data, allowing them to understand the root causes of any issues and develop strategies in light of that information.

This is the most advanced and worthwhile form of analytics, full of the most valuable insights. Prescriptive analysis allows businesses to develop strategies centred on optimisation, based on past experiences and predicted trends.

Prescriptive analysis bounces off the 3 main forms of data analysis that come before it; any strategies developed in light of prescriptive data will be the most valuable when it comes to decision-making, visualisation, and optimisation.

If you want to steer the future of your industry in the right direction, you don’t have to leave it up to fate. Listen to what the data has to say and pursue a career that’s guaranteed to pay off.