Why data science specialists are still essential in the 2020s

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As time rolls on, technology and innovation result in changing careers. Depending on the industry, this can vary - for example, the evolution of the retail employee from simple shelf stacker to technology leader.

It's important to understand that data science, while itself a role that has existed for many decades, is a position that is currently evolving and changing.

While individuals may take a Masters of Data Science to enter the field, let's explore some of the problems that data professionals face this decade and why they are essential to so many business processes.

To understand why data science specialists are essential in today's world, it's important that we are aware of the issues that organisations have with the data they have.

In broad terms, the issues facing most businesses can be expressed, in part, by one of three problems, colloquially known as the 3 V problem. These problems are:

The role of the Data Scientist Specialist can vary from business to business. Depending on the requirements of a business, leadership skills and experience in a number of applied concepts and software platforms may be beneficial.

For example, a data scientist specialist working with small volumes of data may identify methods in which the data can be used appropriately and effectively in the workplace. This may involve identifying trends and suggesting future opportunities for development to senior management.

In larger businesses, a data specialist may utilise tools such as machine learning and artificial intelligence (AI) to draw the most effective insights and outcomes from data.

In fact, having the right amount of technological know-how, in combination with the skills and knowledge to understand outcomes, can be advantageous in translating business intelligence into useful and interpretable outcomes.

One example of successful use of the data and information within a business is the impact of data scientists such as Silvio Georgio, in national shipping provider Australia Post.

With a variety of data available to Silvio and his team, from in-store customer data to the transaction data of tens of millions of monthly parcel deliveries, an opportunity was presented to use this data to improve business operations.

By reviewing and improving the data strategy held by the business, three golden rules were developed, enhancing engagement across the business. These three principles (fun, understandable and mind-blowing) have enabled a more engaged and enthusiastic data culture.

In Georgio's own words, today's Australia Post is willing to engage and work with data enthusiastically. Speaking to Australia Post, he identified that in today's data-rich environment, "Everyone in the business - from the C-Suite to the delivery bike - wants to use data. Our team helps make this possible."

As a result of these interactions, improvements in a range of areas have been identified. From the implementation of a safety tool known as Doogie to delivery analysis tools such as Zoltar, Australia Post is taking strides in getting the most out of the professionals in their teams.

While it might not seem readily apparent, there's a large number of future opportunities available in the data science space.

Demand for data science professionals has yet to outstrip supply in many industries - and as data science continues to be a focus of businesses, large and small, demand for data science professionals is expected to grow.