Being told that we live in a data-driven world is no longer breaking news. It is a fact - a norm that has defined the way we live and go about our daily lives.
For example, every transaction made at the supermarket is the outcome of rigorous data mining on shopping habits by various brands and each movie screened is a response to audience taste segmented across regions and countries.
Almost everything is underpinned by data today, which makes sense why data analytics is storming around the market in recent years, creating a demand for a talent pool of data-loving masters.
Storytelling in the 21st century
Data analytics, by definition, refers to the visualisation and comprehension of raw data gathered through various means such as cameras, computers and algorithms, and then making conclusions from the information processed. In simple terms, it is used to tell stories in various contexts.
In the business sense, data analytics is one of the most sought-after competency as its deliverables help to improve processes and foster innovation in ways that push businesses to achieve their objectives faster than ever before. Being able to understand raw data and transform it into impactful business strategies has become an essential skill in the economies of today and tomorrow.
So a data analyst is simply a storyteller of the 21st century in business speak – one who harnesses the power of data to tell stories of the present and what businesses – and our society – can look like in the future.
Data analytics drives smart cities like Singapore
With smart cities proliferating across the globe, we have a mammoth of data gathered at a pace that exceeds our speed to analyse it. Data analytics now power smart cities in the world, including Singapore where much of our lives are underpinned by digitally-connected devices that hoard data. We are basically characters of the stories told by data that in turn help to structure the lives we lead.
Four basic types of data analytics tell the stories of a smart city like Singapore across various types of industries. Here we tap on the public transport industry as a hypothetical example:
- Descriptive analytics: Information that describes what has happened over a certain timeframe (i.e. Has the number of train commuters during off-peak hours increased over the year? Which day of the week recorded the lowest number of off-peak travels?)
- Diagnostic analytics: Information that explains why something has occurred using a series of data to hypothesise (i.e. Did the number of commuters during off-peak hours increase due to smart travel rewards initiatives? Is there any change in peak hour travels as a result?)
- Predictive analysis: Information that allows analysts to predict what will happen in near future (i.e. How has off-peak and peak hour travels fared in the past three years from the launch of the rewards system? What is the estimated increase in off-peak hour travels over the next two years?)
- Prescriptive analytics: Information to suggest an action to undertake as a result of the above analysis and hypothesising with the data gathered (i.e. If the rewards system has led to a 20% increase in commuters during off-peak hours and 10% decrease during peak hours, we can consider adding more trains to cater to commuters during off-peak hours.)
Businesses that operate within smart cities will thus make data analytics a key tenet in their operations – this has rendered the field increasingly popular among professionals in Singapore today. Continuous training in data analytics is crucial for business intelligence, a technology-driven role that is a resource to organisations striving for success at every node across the digital landscape. If your professional pursuits lie in taking part in a progressive economy and telling stories driven by data, being competent in data analytics is the way to go.
Browse COMAT’s data analytics courses in Singapore here and start training!