FAQs: Data Analytics
We’ve compiled this list of questions most frequently asked about Data Analytics to help you understand what it’s like to work with data.
Data Analytics and why it’s important
Data analytics is the process of sourcing, cleaning and analysing raw data to identify meaningful patterns, trends and insights.
Insights extracted from data can be used to find answers to all kinds of questions, and solutions for even the trickiest problems.
Data analytics helps us understand the effects of what we’ve been doing, what we could be doing, and any probable outcomes that come with a new course of action.
At its core, data analytics tells us what to do next. The ultimate aim is to use hard facts to make well-informed decisions that help a project reach its goals.
- Data analysts start by consulting with teammates and stakeholders to define the objectives of a data analysis project.
- With the informational goals set, they gather and combine data which might come from a range of sources – both internal and external.
- They then clean, manipulate and reorganise data, removing any outdated or unrelated data and getting ready for analysis.
- Now it’s time for an analyst to live up to their title, as they spot trends and patterns that can be translated into actionable insights.
- Finally, they present their insights in a clear and convincing way to inform decision making.
There are three main categories of data analytics that businesses use to drive their decision making:
- Descriptive analytics – this tells a business what has already happened.
- Predictive analytics – this helps a business understand what could happen.
- Prescriptive analytics – this enables a business to make proactive decisions about what should happen in the future.
Many businesses have a wealth of raw data at their fingertips, but translating it into actionable insights is easier said than done.
Data analytics helps a business tap into a vital resource and better understand its products, customers and competitors, as well as its own operational procedures and capabilities.
Armed with this knowledge, businesses are able to identify inefficiencies and opportunities. Because data is not an opinion or a theory, it can act as an impartial source of truth when making important decisions.
- Quantitative Analysis
Quantitative data analysis is all about analysing numerical data to draw statistical conclusions. To give a simple example, a business could assess the popularity of its products in various parts of the world by comparing sales figures in different nations.
- What is Qualitative Data Analysis?
Qualitative data analysis normally refers to analysing text and verbal information, such as open-ended responses to survey questions or user interviews. It can also incorporate images, audio files and videos.
The same business could perform qualitative data analysis by examining customer surveys, which might clarify why a product is performing well in one country and not another.
While data analysts normally work with structured data to solve tangible business problems, data scientists often deal with the unknown, using more advanced techniques to make predictions about the future.
Data scientists are widely thought of as more professionally advanced than data analysts, and often have a computer science or mathematics background. With enough experience and training, a data analyst could eventually find themselves in a data scientist role.
A data scientist’s role might include:
- Designing predictive models and machine learning algorithms for mining large data sets.
- Developing tools and processes to monitor data accuracy.
- Building custom data visualisation tools and creating advanced dashboards and reports.
- Writing programs to automate data collection and processing.
Data Analytics skills & tools
Regression modelling is a reliable data analysis technique that enables analysts to accurately identify which variables impact a topic of interest. It helps analysts confidently determine which factors matter most and which can be ignored, as well as how important factors influence each other.
To give a simple example, a person’s weight will definitely increase as their height increases. Analysts look for these kinds of relationships between statistics because they clarify how one factor will affect another, making outcomes predictable.
Because data that sits untouched in a spreadsheet is of minimal value to a business, Data Analysts are responsible for visually and verbally sharing their data insights.
Data analysts will convert their findings into high-impact visualisations (charts, graphs, tables and other infographics) that make data insights easy to comprehend.
Our ability to absorb visual information is far speedier than our ability to grasp the meaning of words and numbers. If a picture is worth a thousand words, a clear data visualisation might be worth a million data points.
As well as being able to visualise data, data analysts need to be able to tell compelling stories about their data insights.
Data analysts need to deliver simple, linear narratives that clarify what their data uncovers about a business. This can create an “aha” moment, when a deep insight is fully understood by an audience.
Data analysts don’t need to be bestselling authors, but they do need to tailor their pitch. To secure stakeholder buy-in for a recommended initiative, it’s vital to build a narrative that’s meaningful and relevant to whoever is listening.
Data Analysts rely on a wide variety of tools to be more effective and efficient in their day-to-day work. Some of the handiest tools that Data Analysts use include:
Beautifully simple and perfectly functional, an Excel spreadsheet organises raw data into a readable format and makes it easier to extract insights. With more complex data, Excel allows you to customise fields and perform automated calculations.
- Python Programming
Python is a multi-purpose programming language that’s popular among data analysts due to its extensive collection of libraries, which are useful for combining multiple datasets and performing complex calculations.
Python coding skills come in handy at all stages of a data research project, helping data analysts clean, manipulate, examine and visualise data.
As its name suggests, Structured Query Language (SQL) is a standardised programming language that’s used to retrieve and query data. Because SQL is standardised, it’s easy to understand and learn.
SQL can be used to access and communicate with large amounts of data wherever it’s stored. This means analysts don’t have to copy data into other applications. Instead, they can instantly start organising and analysing data at the source.
- Power BI
Microsoft Power BI is a collection of software services, apps, and connectors that work together to turn disparate sources of data into coherent insights. Whether it’s stored in a spreadsheet or a cloud database, businesses can aggregate data into a single workable data model.
Assisting data analysis projects from end-to-end, Power BI also has the tools to turn insights into interactive, immersive visuals, which can be shared with other Power BI users throughout an organisation.
R is a programming language and a free, open-source software library that’s used for cleansing and prepping data, generating statistics and creating visualisations. R’s coding language is simple but powerful and often used by data scientists to train machine and deep learning algorithms.
R can be used directly and interactively on the web, and also easily integrates with BI software, helping analysts combine a range of critical data.
- Google Colaboratory
‘Colab’ is a Google Research product that allows anybody to write and execute Python code through a browser. Data can be drawn directly from GoogleDrive, or imported from an external source. It also works as a comprehensive notebook where analysts can write code, run code, see the output and then share the whole process with teammates.
- Google BigQuery
BigQuery is a cloud-based architecture that allows analysts to query masses of data without the need for a database management system. Analysts can auto-scale their search results up and down, and only pay for the data they process.
Analytics teams use Tableau to drill deep into data and then convert uncovered insights into clear infographics. Tableau’s emphasis on visuals makes it a great tool for quickly exploring data and packaging it in a way that’s interactive, collaborative and easy on the eye.
Data from various sources can be copied into Datawrapper, which then converts information into interactive pie charts, line charts, bar charts and maps. These can be embedded into a website and even customised to suit the aesthetic of a particular brand or platform.
Industry demand for Data Analysts
Data analysts are in demand worldwide and Australia is no exception. With big data exploding across all industries, the demand for data analysts is now outstripping the supply of qualified professionals.
Seek is currently advertising over 19,200 data analytics roles in Australia alone. Data analysts are globally sought after, so wherever you end up in life, you’ll have a skill-set that employers are searching for.
The pay opportunities for Australian data analysts are representative of an industry that’s growing fast and underserved by skilled professionals.
Talent.com records the average data analyst salary in Australia at $103,380 per year. Even entry-level positions start at $90,992 per year, while the most experienced analysts make up to $129,500 annually.
Becoming a Data Analyst
Believe it or not, being a data analyst is not as academic as the title suggests. You don’t need to be a mathematical genius or a programming whiz to break into the field. However, there is a wide variety of skills that go into being an effective data analyst, and some of them are quite technical.
Many of the most sophisticated skills a data analyst uses can be learned on the job, but it’s important to start with a solid foundation of the essential tools and techniques.
The chances of anyone learning these alone online are slim, so anyone keen to enter the industry should consider earning a formal certification. Without this, it’s difficult to get hired into that first role, since so many other candidates will have a proven, certified skill-set.
There are many options for qualifying as a data analyst. Traditional universities offer Data Analytics and Data Science degrees, which normally take 3-4 years to complete.
These days, there’s less expectation for data analysts to be university qualified, with many employers, including the biggest blue chip companies, valuing skills and experience above a degree.
As a result, more people are enrolling in condensed bootcamp-style courses, which leave graduates job-ready in a much shorter period of time and focus on up-to-date practical skills that the industry needs most.
Data analysts help businesses make informed choices (rather than relying on somebody’s best guess!), enabling businesses to function intelligently and grow. Do you want to bring that level of capability to your career?
Whether you want to become a data analyst, upskill and test the waters of a data-driven career, or simply harness the power of data for your existing role, Academy Xi offers a range of Data Analytics courses suited to your ambitions.
- Data Analytics: Transform – For those who want to start a new career as a Data Analyst.
- Data Analytics: Elevate – For non-data professionals who want holistic data analysis skills to use in their current role.
- Data Analytics (Pro): Elevate – For those who want to take their existing analysis skills to the next level with coding.
- Data Visualisation with Python: Elevate – For anyone who wants a gentle introduction to Python for analytics, with a focus on visualisation.