Five essential skills for an analyst

The field of data analysis is exciting, growing and changing every day. In the broader business environment, the demand for data analysts is booming. This trend will only continue as more companies discover greater value in interpreting the massive data available to them.

Data analysts have proven to be vital not only in the formation of business strategy but also in product development, staff and customer experience, service offerings and much more.

Today, the demand for data analysts continues to grow. According to the Talent More Than Money Salary Guide 2022, tech salaries in general remain on the rise, while the demand for data analytics, Python programming and cybersecurity skills continues to remain high.

Skyrocketing salaries 

The salary that data analysts can demand is climbing steadily. According to Salary Explorer, data analysts in Australia in 2022 can expect their salaries to increase by 11 per cent every 17 months. Within 10 years, they stand to see their salaries double. 

Education also plays a big part in what salary data analysts can command. A data analyst with a bachelor’s degree earns an average of $96,400 a year, while a data analyst with a master’s degree earns an average of $128,000 a year, according to Salary Explorer. 

A range of soft and technical data analytics skills, from change management and business engagement to various architecture and analytical method knowledge, can be required in various data analysis roles. However, some skills are so important that they’re considered virtually essential.

Here are the top five requirements of a successful data analyst 

1) SQL skills 

When most data is stored in a database using a ubiquitous database programming language, it’s clearly essential for a data analyst to be fluent in that language. This statement is true even if the language, namely SQL, is nearly 50 years old.

A 2021 Statista survey showed that over 50 per cent of all developers use SQL. It’s very good at what it does, and it’s been battle tested over the decades. This means it’s mature, reliable and applicable to the majority of data queries. It saves lots of coding time and, amazingly, is also relatively simple – so much so that people in non-technical roles are often asked to become familiar with it.

2) Critical thinking capabilities

It’s no secret that any type of analysis requires critical thinking, but what exactly is ‘critical thinking’? In basic terms, critical thinking is the ability to make connections between sometimes disconnected ideas. Critical thinking, however, also involves the following:

• Having the ability to complete an objective analysis of a challenge, a problem or an opportunity to properly assess and reconstruct it
• Understanding facts and figures in a way that has nothing to do with opinion or ego
• Knowing how to undertake a deliberate and systematic analysis of information 

All these skills are essential, as they’re all used regularly in a data analyst’s day-to-day role. Critical thinking skills allow a level of engagement that goes beyond superficial and leads to a deep and powerful understanding.

Fortunately, most people attracted to data analysis naturally lean towards being critical thinkers. Leading analytics educators will take this natural inclination further by developing critical thinking in students of their courses.

3) Communication skills and tools

The best idea in the world will never get off the ground if it can’t be clearly communicated. Similarly, brilliant data analysis that isn’t reported in a way that stakeholders can easily understand and digest is likely a waste of time.

Being able to process numbers is only the first part of the job. 

Communication skills required for a data analyst involve: 

• Making results meaningful to others
• Having clear verbal communication and storytelling skills
• Being able to understand business strategy and adapt communications accordingly
• Having a good knowledge of data visualisation software

4) Be fluent in several programming languages

The more scripting and statistical languages you know, the more employable you may be. In addition to SQL, five more key programming languages follow: 

Python

After SQL, Python is one of the most widely used data analysis programming languages. It’s an open-source, easy-to-use language that’s dynamic and that supports multiple paradigms. 

Its many benefits include the following:

• It has fewer than 1000 iterations.
• It’s fast and agile at data manipulation.
• It’s easy to read in a spreadsheet as it’s possible to create CSV outputs.  

R

R is a high-level open-source language used for statistical computing. It has multiple useful libraries for analysis. 

R is particularly helpful for analysing data sets. However, it’s a more challenging language to learn than Python, as it has many more iterations. 

Scala

Compared to SQL, Scala is a relatively new programming language. Initially created in 2003, Scala addresses issues with Java, but it can also assist with everything from web programming to machine learning. 

From a data analysis perspective, it’s also a scalable language that can handle large volumes of data, and it supports functional concurrent and synchronized processing. 

JavaScript

JavaScript can be extraordinary for data analysts: it’s a great tool to help create dashboards and for data visualisation. 

JavaScript also has multiple other benefits, including the ability to handle multiple tasks at once. It can be embedded in everything from web applications to electronics. It’s also easily scalable. 

Julia

Another essential data analysis programming language is Julia. This language was specifically developed for fast and high-performance numerical analysis. 

Julia is also handy for implementing mathematical concepts and can help deal with matrices. It can be useful in back- and front-end programming as well. 

Fluency in many programming languages is important for not only data analysis but also collaboration. Collaboration is key in the data analysis profession, and fluency in several languages enables analysts to communicate with each other and interpret their peers’ work. 

5) Broader business knowledge

Several times throughout this story alone we have referred to topics that may not originally appear to be intimately connected with data analysis. We’ve discussed the management of teams, change management, staff experience, customer experience, business strategy, communication, critical thinking and more. The IAPA report also mentions influencing, business leadership and engagement.
 
Add all of these together and you don’t just come up with a leading data analyst, you’ve also got a very competent business person who understands many of the challenges facing businesses today. They also likely have several solutions for those challenges.

Advance your skills in data analytics 

For true success in business, any specialist in any industry must develop a broader understanding of business itself. The most valuable players in organisations are the ones that understand what it is that their team-mates require, and know how to deliver in order to have those needs met. The superstars of the business world are the ones who know in advance what it is that their team-mates are going to need, and deliver before their team-mates even ask. This predictive ability is part of the magic and power of data analysis, meaning data analysts are in the perfect position to impress, as long as their business knowledge is up to date.
 
The same Morgan McKinley report mentioned earlier also said, “The best way for candidates to access the top brackets within their relevant fields, the most popular being data scientists, BI specialists and data visualisation experts, will be further education.” Find out more about how to learn these vital data analysis skills, and more, with our Master of Analytics.