Even as data analytics emerges from the back room and becomes as ubiquitous as the camera in a smartphone, the vital ingredient for analytics success is not technology, but having a data-driven culture.
7 examples of big data going above and beyond expectations
Speakers at the Singapore launch of Tableau’s new data preparation tool said the growing prevalence of data analytics is due to the exponential growth in data volume and leaps in computing power. This presents huge opportunities for organisations to innovate and optimise their businesses.
“Data is the lifeblood of pervasive innovation,” said JY Pook, senior vice-president at Tableau Asia Pacific. “Everyone should be skilful in making decisions based on data on behalf of their organisation. We see smart nation initiatives, organisations and tertiary institutions all working towards understanding their data, and building a data culture.”
For example, the Singapore Sports Institute, a government statutory board, uses analytics to analyse the performance of more than 70 high-performance athletes to help them train effectively for competitions and forecast success.
“In the future, competitiveness is going to be determined by the ability of organisations to manage data,” said Fermin Diez, an adjunct professor at Singapore Management University.
Diez gave the example of how ride-hailing companies Grab and Uber both harness analytics in their operations, but Grab gained the upper hand through better use of technology.
He noted that although Uber, a global competitor, had entered Asia with a superior platform, Malaysian startup Grab used analytics to understand driver and customer needs better, eventually overtaking Uber in market share.
In fact, more than 60% of Grab’s staff engage in data analysis, said Diez. “We are seeing a broadening of analytics skills to as many people as possible, instead of depending only on data scientists who may seem like high priests in black robes in some back room gazing into crystal balls,” he quipped.
It is a challenge to prepare the data for analysis because it may be in the wrong shape or residing in disparate sources. A recent Harvard Business Review study found that people spend 80% of their time cleaning and shaping data, and only 20% of their time analysing it.
Besides having clean data, Diez said the main challenge is to convince people that data analytics does not always start with data.
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