Computers form an essential part of modern workplaces. Invented less than a century ago, the humble processor has evolved from a simple number cruncher to a tool that can provide endless amounts of data to the businesses they serve.
With this advent of modern data streams, businesses have had to rethink their data strategy to capitalize on the information gains on offer this decade. How did we get here, though? Let's discover how data literacy has become essential for businesses that wish to leverage their data sources to create meaningful insights, and how a current qualification such as the Master of Data Science can provide you with mission-critical skills and know how to dive into the complex statistical problems that face enterprises this decade.
The Benefits of Data Literacy in the Workplace
It often seems a little folly to talk about numbers once you've finished school. After all, you're not stuck in a classroom anymore - there are powerful tools like calculators, and perhaps the computer you're reading this on.
While it's important to validate why you may be learning something, in recent years, this despondency with mathematics has led to a decline in the pursuit of courses that are reliant on graduates from fields that study mathematics and statistics, such as engineering.
This has had a flow-on effect, particularly with respect to data literacy in the workplace. In 2020, accounting firm Accenture and analytics provider developed a report, to endeavour to explain some of the human roadblocks to data literacy within an organization. In their research, they found that only one in five Australian workers surveyed considered themselves data literate, and an astonishing 12% of those workers felt there was a significant amount of time being used to learn the skills necessary for them to do their job effectively.
Previously, businesses considered self-servicing as a reliable path to making data-led decisions. However, when data volumes are so large, complex, and variable, a changing mindset is leading to a focus on self-sufficiency - rather than a single analytics team, having the capability to embed in different areas across the business to provide insights and learning in ways that teams can take advantage of.
In essence, this highlights just how critical an end-to-end process is for developing and nurturing data literacy. Having a business that is empowered to use data and analytics, and that feels they have the skills and technologies available for them to interpret the data, ultimately helps drive better data outcomes.
A Reinvigorated Career Path - Statistical Analysis
Automation has become the word of the week in recent years, as businesses seek to implement the latest innovations into their workplace. Initially expected to replace the roles of analysts and researchers with automated insights, the evolution of automation has had the opposite effect - resulting in a surging demand for qualified specialists that can understand the volumes of data that new and evolving automated processes produce. Beyond simple discovery, statistical analysis is rapidly becoming a skill that is desirable for potential job seekers and necessary for employers to succeed.
Much can be said for the benefits of a strong background in statistical analysis. In recent years, films such as Moneyball have highlighted how having a data-driven mindset, as well as being able to interpret and understand data, can lead to great success in the industry. In the case of Billy Beane and Oakland Athletic, that industry was baseball - but that's not to say that their principles can't be applied elsewhere.
Consider some of the startups that form the modern tech titans we use today. Google, for example, builds algorithms that drive traffic using elements such as site visits and network connections. They're not alone - Spotify uses the power of data and statistics to build their viral Spotify Wrapped.
Data and statistics are intertwined - and as data has surged in the workplace, it's highlighted just how valuable analysts are within the modern, data-literate workforce.
Where Can Data Analytics Take You?
Roles in data analytics span nearly all industries. At the time of writing, nearly 15,000 open analytics positions were listed on the national hiring site Seek - highlighting just how in-demand data analysts are.
There's a wide range of potential employment opportunities - from organizations that are only just beginning to leverage their data to make informed decisions to teams that are looking to take the next step with processes such as machine learning and artificial intelligence (ML and AI).
Some recent analytics opportunities that were available on job boards included:
Junior Risk Analyst with a bank operating nationwide, providing insights on consumer spending, customer behaviour, and fraud risk. This sort of analytics role works with spending data to drive insights - potentially creating and maintaining complex models to improve results.
Transport Analyst for a major logistics company, developing a transport framework using driving statistics to optimize and improve network efficiency. In today's world, where transitioning to a sustainable and less polluting model has become imperative, businesses are looking for new ways to take data to reduce the amount of fuel and consumable material they use.
Electricity infrastructure analyst for a state-based power network. Understanding the impacts of different events on the power grid can potentially mean millions of dollars for businesses - analysts use the information available from internal and external sources, such as market regulator AEMO to drive data-led decision-making.
Leading the Way - Data Pioneers in The Workplace
What are some of the businesses that are using data to drive change across a range of industries? From social media to space logistics, there's a wide range of industries using data to pioneer their way to the data-rich world tomorrow. Some examples include:
In the transport sector, companies such as Uber use complex, highly variable datasets to identify potential growth opportunities within their data teams. This can then be fed back into company pricing models, allowing them to set prices in the more than 300 markets they operate in, with the best data available to them.
In the consumer goods sector, companies such as Amazon are finding new ways to offer rich datasets to their suppliers. By developing products such as Store Analytics, brands can get information on how customers interact with their product, and then use that data alongside statistical methodologies to identify ways to improve their product range.
It's an exciting time for the budding data analyst, especially for those with a penchant and a willingness to learn. Looking forward to the next decade, it would be difficult to imagine a place where a data analyst didn't have a place of prominence in the workforce - so if analytics was something you might not have previously considered as a career - now is the time.
