There are so many uses for data analytics in business situations – it’s no accident it’s become one of the most sought-after skills for employers. Working hand in glove with burgeoning field of AI, data analytics shows no signs of becoming less important, because it allows practitioners to make sense of an often confusing world of information. Here are five reasons why you should consider studying data analytics.
They’re a life raft in a sea of information
You can ask your Google Home or Alexa almost anything, and they’ll have an answer for you. There are millions of data sets, tables, graphs and piles of information available online, but what are we supposed to do with all this raw material? Data analytics isn’t just about fetching the information – it’s about knowing which parts are important and which can be ignored.
“People get super-excited about the technology but those are just tools,” says Jonathan Choy, Solutions Architect at Databricks. “As a professional working data analyst, you really need to come in with more common sense and understanding of what problem you’re actually trying to solve. Work backwards and find the technology and the right methodology to help you achieve that goal.”
By the same token, AI can take on tasks that human minds are less well-suited to. Why suffer through the repetitive drudgery of sifting through spreadsheets when there’s a tool available that can do the same job more quickly and accurately, with less complaining?
That can sound scary, like the machines are coming for our jobs. But the rise of AI means a corresponding rise in high-skilled work opportunities. And it also frees us up to take on more interesting and useful roles.
The AI and analytics sector is constantly expanding
We mentioned Google Home and Alexa, but the field of AI is far more widespread than friendly robotic assistants telling you whether or not to wear a jacket today. AI is being implemented and expanded in industries ranging from the gig economy to the health sector. It can determine the optimal route for your pizza-toting delivery guy to take, and build predictive diagnostics to advise medical professionals on the best treatment options.
With so many varied uses for AI come equally varied jobs. Industries are crying out for people who can integrate and maintain these systems that are becoming more and more important.
Data Analytics skills are highly transferable
Using machine learning and algorithms to solve problems isn’t something that only happens in laboratory environments. Many business’ challenges benefit from implementing the precision tools and techniques developed in this space. For example, in a marketing department, data analytics can be used to build complex customer profiles, customise content and even design products for specific audiences. In an agricultural environment, the same skills can be leveraged to monitor crop health, soil conditions, chemical use and cultivation output.
What this means for you is that you can learn the fundamentals of data and AI, then apply them across as many roles and sectors as you choose to.
“It’s really about getting hands-on experience,” adds Jonathan. “Rather than just reading a book, pick up a project, find a data set, find a problem and build something.”
They support lateral thinking and drive innovation
Once you’ve wrangled your data into shape and don’t have to expend time and energy on tasks better performed by machines, you can make use of the more “human” ability to find patterns and make unexpected connections. Diagnostic analytics underpins this process by providing the raw material from which you can pull insights.
“This is important because of the volume of data that businesses and organisations collect every single day,” explains Associate Professor Seyedali (Ali) Mirjalili, Director of the Centre for Artificial Intelligence Research and Optimisation at Torrens University Australia. “They need a tool to be able to efficiently process the data, and be able to analyse them to answer their business questions, and solve their business problems.”