The world of data analysis is an exciting one, 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 the interpretation of the mass of data that is available to them.
Data analysts have proven to be vital in not only the formation of business strategy but also in product development, staff and customer experience, service offerings and much more.
A 2017 report from Morgan McKinley said, “As demand for specialist talent has increased we have seen real talent shortages across several role types, including data scientists, predictive modellers, data visualisation and specialists.”
The salary that data analysts can demand is increasing. The 2017 Skills & Salary Survey Report from the Institute of Analytics Professionals of Australia (IAPA) says the top ten per cent of data analysts saw an annual salary increase of seven per cent to a median of $235,000 annually. The bottom five per cent also experienced an increase of nine per cent, to $72,000.
The median salary of team managers and technical experts is $163,000, and “top dollar is paid for more bespoke skills like natural language processing, social network analysis and optimisation as well as machine learning and artificial intelligence (AI), cloud, big data and text mining,” the IAPA report says.
A range of technical and soft skills, from change management and business engagement to various architecture and analytical method knowledge, can be required in various data analysis roles. But 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
Let’s get the obvious one out of the way, first. When most data is stored within a database, and there is a ubiquitous database programming language, it is clearly very important that a data analyst is fluent in that language. This statement is true even if the language is 45 years old!
The 2017 Stack Overflow Developer’s Survey said over 50 per cent of all developers use SQL. It is very good at what it does and has been battle tested over the decades. This means it is mature, reliable and applicable to the majority of data queries. It saves enormous amounts of coding time and, amazingly, it’s 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 its most basic form, it’s the ability to make connections between sometimes disconnected ideas. Critical thinking requires a completely objective analysis of a challenge, a problem, or an opportunity, to properly assess and reconstruct it.
Critical thinking is all about facts and figures and has nothing to do with opinion or ego. It is the deliberate and systematic analysis of information which, of course, is also the job of a data analyst. This type of thinking allows 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 is not reported in a way that stakeholders can easily understand and digest is likely a waste of time.
Being able to crunch the numbers is only the first part of the job. Making the results meaningful to others is just as important. This includes clear verbal communication, storytelling skills, the ability to understand business strategy and therefore fold your information in to make it matter within the framework of that strategy, and good knowledge of data visualisation software.
4) Be fluent in several languages
We’ve already mentioned SQL, but the more scripting and statistical languages you know, the more employable you will be. Two of the most in-demand languages in today’s market are Python and R. It goes without saying that exceptional Excel skills are also vital, but other languages can take what Excel can do to an entirely new level.
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.
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.