It’s about data literacy, says Qlik exec

  • Today’s workforce has no excuse to be data-illiterates
  • But it has to be both ways: Needs buy-in from C-suite and employees
It’s about data literacy, says Qlik exec

THE biggest threat robots realistically pose is not that they will take over the world, but that they will take over our jobs, instantly making a huge chunk of the workforce obsolete.
And that reality is inching ever closer, going by the recent wins of Google’s AlphaGo program over a human champion in Go, a game long thought to be unwinnable by a machine.
However, the truth is that robots and machines have been taking away jobs for a long time, argues Terry Smagh (pic above), Asia vice president and managing director at business intelligence and data visualisation company Qlik.
“Automation has been there for a long time – it’s there to make things simpler and more idiot-proof,” he quips, speaking to Digital News Asia (DNA) in Singapore recently.
“The whole idea here is getting people to understand that automation can help you in your job and not take it away,” he adds.
For automation and machines to be fully effective in helping humans with their jobs however, people need to have data literacy, according to Smagh.
“How much do we understand from the data we have? This is the whole conundrum of big data,” he says.
Data literacy is the ability to derive meaningful information from data. The exponential rate at which data is being created and collected today is leading to immense amounts of data to sift through, making data literacy ever more important.
READ ALSO: Birthing a modern data culture
“There are silos and silos of data, and at the end of the day, the data literacy issues within an organisation are immense,” says Smagh.
“We assume everybody today understands the level of data they have, but that’s a very big assumption.
“They understand the data that is presented to them only; they only understand the data that they can make sense of, and that’s it.
“If you ask them for insights across multiple data sources or to make sense of data with statistical analysis, to [formulate] a predictive perspective of things, or [discover] the correlation between SKU (stock-keeping unit) and marketing – that particular human is not going to have that broad range of industry knowledge,” he adds.
Machines and robots, on the other hand, can help people derive a different perspective from the data, bringing about different ideas or answers, according to Smagh.
Not everyone gets it

It’s about data literacy, says Qlik exec

The data literacy problem is not just limited to senior management, although it might seem more acute at the top of the pyramid.
“It goes all the way down, and I think the higher you go in senior management, the data literacy problem might be higher,” says Smagh.
“The reason I say that is because they have become so siloed at what they want to look at – it is profit and loss or business operations, very targeted, and I think there’s where people lose the picture,” he adds.
Looking to machines to fill the data literacy gap is not the answer either, as there is still the possibility of mechanical error, argues Smagh.
“Why do we still need the human? Because at the end of the day, as much as there is human error, there is mechanical error also.
“Data-driven possibilities are extremely huge, but the tacit knowledge you get from human intervention, from cross-cultural sharing, is very high – and I don’t think robots can do that,” he says.
Some companies are trying to beat the data literacy problem by hiring more business analysts, but they also need to look at what industry background these analysts have.
“Imagine if you get someone from manufacturing and the person goes to work in the finance industry – the difference is night and day in terms of the type of reporting,” says Smagh.
But the issue is not the reporting element, it is the mindset shift the analyst would have to make. “That’s where education comes into play.
“And that’s where many organisations fail, in trying to empower their employees and skill them up,” he adds.
No excuses

It’s about data literacy, says Qlik exec

There is no point in talking about being lean and highly-efficient and productive if employees are not enabled, according to Smagh.
“For example, I can have 10 people doing highly efficient and productive things in their own domain – but if I streamline this process, can I make do with just five?
“I’m not taking jobs away from five people, I’m just streamlining, being more ‘efficient, productive and effective’ – those are the three words that companies will use, but they need to look at their workforce and see if they are enabling them,” he adds.
But employees themselves have to also take the bull by the horns. Given the ubiquity of digital technology today, they can no longer use data illiteracy as an excuse, declares Smagh.
This where social collaboration can play a key role, he suggests.
“Knowledge management is a huge thing in Singapore, but what is irreplaceable is experience, your tacit knowledge.
“The only way organisations can provide enablement and education is through a sharing collaboration.
“That’s where organisations need to spend more time on – there’s not enough being done there in that space,” he adds.
Finally, data literacy cannot be addressed by a top-down order from the C-suite – it needs employee buy-in and a bottom-up approach too.
“It needs to come both ways, not just bottom-up or top-down, and that’s where the education element comes into play,” says Smagh.
Getting people to understand data literacy in the context of its effect on the company – what it means to the company and what it means to the employee’s role – would be a start, he argues.
“That educational process needs to bring cross-functional teams together, employees need to understand what they can do to make a difference.
“When they see they are being entrusted with such things, you’ll see the big difference they’ll make,” he adds.
Related Stories:
AI: Man vs machine, or man AND machine?
Analytics and the real world: Making the connection, Part 1
What are you going to do with all that data?
Getting value out of data: It’s about ideas, not investments
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