How to Become a Data Analyst

Data Analytics explores the methods, processes, algorithms, and systems used to extract knowledge from data. Data analysts are investigators, storytellers, and most importantly, problem solvers who use raw data to draw actionable insights.

As the backbone of any well-informed decision, data has become vital in driving the strategy and future roadmap of many businesses. Statista found that the big data industry is expected to be worth US$49 billion in 2019, and by 2027 is expected to reach US $103 billion worldwide.  

As more industries become heavily data-reliant, new opportunities for data science roles have emerged including; data scientists, data analysts, data architects, data engineers, statisticians, and database administrators.

A snapshot of the data analytics industry:

• Over 300,000 people were employed in data analytics in Australia between 2016 – 2017
• Annual forecasted growth in the data industry is expected to be 2.4% YoY by 2022
• 76% of companies expect to invest more in data analytics capabilities over the next two years
• Forecasted average income for a Data Analyst is AU $130,176 by 2022.

How data analytics provides business value

With data becoming the new currency for business decisions and strategic roadmaps, some key benefits of data analytics include:

• Helping businesses define decisions and goals: By dissecting previous performance, businesses can use data to prioritise their goals according to highest importance and optimal results. Rather than making decisions based on gut, data can prove what has worked well within a business and define what future goals they should work towards.
• Adoption of best practices: Applying analytics to the design and control of processes enables businesses to optimise their activities to fulfil customer expectations and achieve operational excellence.
• Test informed decisions: Effective data collection enables businesses to stay competitive by testing and validating informed decisions and anticipating market demand.
• Reduce risk and fraud: Data Analysts are able to identify data patterns that can be used to make frameworks to detect fraud. These alerts help businesses track unusual activity and respond in an appropriate timeframe.
• Deliver personalised experiences: By using data to tell stories, Data Analysts are able to empower sales and marketing teams to better understand their audience. With increased knowledge of buyer behaviour and motivations, organisations are then able to make personalised, well-informed solutions.

Key attributes of a Data Analyst

According to LinkedIn, data mining and statistical analysis were the second most in-demand skills requested by employers, with the most number of job openings, listed in 2016.

As the main driver of strategic business decisions and high industry demand, here are some key attributes of a successful Data Analyst:

• Critical thinking: Before developing any hard skills such as programming, it is vital that a data analyst adopts a critical thinking mindset. To ensure useful insights can be drawn it is necessary that a data analyst has the ability to ask the correct questions in the first place. The role of the analyst is to then uncover and synthesise connections, make sense of the information connected, and present their findings in an easily digestible manner.
• Understanding the data lifecycle: Familiarity or knowledge of the acquisition, management and pre-processing of data, as well as mathematical and statistical analysis, reporting and decision making is extremely useful. It allows a Data Analyst to interpret data and present it in a meaningful way that can be used to support business decisions.
• Computer programming: Competency in programming languages such as R, Python, SQL, SAS, MATLAB and Excel are invaluable for Data Analysts, who use programming skills to extract, discern, and manipulate data. This information can then be presented into digestible visualisations in tools like Tableau.
• Data visualisation and presentation: Data visualisation and presentation are hand-in-hand skills for a Data Analyst. The ability to tell a compelling story with data and drawing valuable insights is key to making any data useful.   
• Machine learning: Machine learning and predictive modelling are increasingly becoming the most popular topics in the field of data science. To develop these skills, a Data Analyst is required to have proficiency in programming languages in order to make predictions and automate existing data systems.