Data Analysts: What Do They Do, and Why Are They In Demand?

Data analysts make it make sense. Make what make sense, you might ask? Well… everything. 

From numbers, to words, to images, almost anything can be considered data. Data analytics is a type of data science that aims to find the patterns, correlations or relationships in the data and analyse their meaning. They then use these insights  to answer some big questions and inform some big business decisions.

Data-driven analytics is becoming increasingly important across all industries. Why? It allows companies to make more informed decisions. These decisions are based on the insights data analysts deduce from large sets of raw data, that otherwise would be pretty useless to the untrained eye.

“Put as simply as possible,” says data analyst Jesse Peterson, “The role of a data analyst is to take abstract information and transform it into usable information.”

A data analyst’s work can:

• Help create targeted content to the right customer group
• Optimise productivity and opportunities for improvement
• Ensure more objective decision-making takes place
• Help people innovate and explore with purpose

“Data analysts take endless rows of seemingly random dates, numbers and dot points, then convert them into easily readable forms, such as graphs, charts, tables and dashboards,” Jesse explains.

To break it down a little more, the data analysis process generally follows these five steps:

1. Identify the data, define the question 

What question are you trying to answer? What do you need to ask to make sure the right data is chosen to answers questions like:

• What are we doing well?
• What are we doing poorly?
• What are our opportunities for improvement?
• What trends are we seeing?
• Why has x changed?

2. Collect the data 

Data collection can occur via a wide range of places, however, some common sources include:

• Surveys
• Website visitor tracking
• Buying datasets from collection specialists

3. Clean the data

Although often very accurate, computers aren’t perfect, so, cleaning data is essential. This means sifting through large datasets and making sure only relevant information remains.

4. Analyse the data

There are four main types of data analyses methods that you’ll use throughout your data analyst career:

1. Descriptive
   Describes the features of the data set so you can better understand how you should move forward.
   
   
2. Diagnostic
   Helps you find out why something has happened —- what are the correlations between values in the data
   
   
3. Predictive
   Uses past data trends to understand current or possible future trends in the data. Predictive analytics uses techniques from data mining, such as using data models, algorithms and machine learning techniques to make predictions about possible future events. 
4. Prescriptive
   Informs future decisions and course of action based on the trends found in the data. Often, this is reached with the help of machine learning techniques.
   

5. Report the results to stakeholders

Now that you have found the answers or at least, possible answers to the questions you defined in step one, you need to report it. This means putting your analyses into an understandable format so you can inform all and any stakeholders of your findings and advise them on how to move forward.

Data analysts can work in a range of environments. Like any role, a data analyst’s job description will change depending on which industry you work in and whether you’re an entry-level or senior data analyst. Each industry will have its nuances, its challenges, and specific flow of operations.

Though used to mainly work in investment banks and private equity firms. But now that data is a valuable resource in almost all Australian industries, you can find them working in:

• Marketing
• Healthcare
• Retail
• Insurance
• Technology

Some unique and exciting data analyst career opportunities include:

Being a good data analyst means you’ll need a strong blend of both hard and soft skills. 

Your technical knowledge is essential to complete the analysis aspect of the role. Some critical skills you’ll need to have under your belt include:

• Using database tools such SQL and Microsoft Excel
• Understanding programming languages like Python or R
• Data visualisation through programs like Tableau or Jupyter Notebook
• A firm grasp of higher level maths and statistics 

This is because you’ll be expected to present and justify your work to key stakeholders in an understandable and effective way. You’ll also want to develop strong:

• Critical thinking techniques
• Problem-solving skills
• Analytical skills

“A person who enjoys a bit of number crunching, who prefers to work independently, and comes at problems with a critical eye” Jesse says.

However, he also warns that, “Trying to understand all types of information that are being collected can be overwhelming, let alone finding and extracting the very specific data you need.” 

A hot tip from Jesse for all budding data analysts (no matter the industry) is to befriend your company’s data engineer ASAP — “They will be your greatest resource in understanding what data is being stored.” 

Training.com.au has a wide range of online courses from Australia’s top education providers that can get you ready to leap into this thriving industry.