Insider Tips for Automating Analytics | 7wData

Insider Tips for Automating Analytics | 7wData

Automation is the new buzzword in 2022. Forbes Technology Council listed Automation as a critical focus for enterprises to reduce workload and error.

But that is not enough. With the pervasive use of AI and analytics, some enterprises take one step further to automate the automation. The idea is to democratize technologies among knowledge workers by bringing automation into complex tasks like data processing and building ML models. So, businesses can drive end-to-end automation by using automatically developed analytics.

At the recent CDOTrends Digi-Live! Summit Series, data analytics experts and technology practitioners discussed odds and ends to bring the vision of automating analytics into reality.

One reason automation is catching attention, according to Suganthi Shivkumar, vice president for Asia at Alteryx, is a result of the post-pandemic isolation era. In addition to enabling business resiliency during the crisis, automation has drastically raised the awareness of democratizing data.

IDC’s study indicated that 94% of Asia Pacific business and digital leaders agreed data fluency is essential for their organizations. But only 19% are considered an expert in this area.

“What drives this data fluency? The answer is simple, the more people who are on board in this data journey, the more mature the organization becomes and the more return it gets,” said Shivkumar. “It has become a non-negotiable need for data analytics to be a pervasive workforce skill set.”

Aiming to empower general knowledge workers to be involved in the data analytics journey, enterprises are looking for tools to bring end-to-end analytics automation. By bringing business domain expertise closer to the data and analytics processes, Shivkumar said enterprises could also create value faster.

“We increasingly see our clients spend millions on the exclusive few — the data scientist and elitist community — but still struggle to see the value,” she said. “The bulk of the people that drive insights and breakthroughs are the citizen data scientists or the knowledge workers, who typically don’t have access to the data analytical tools.”

This is precisely what the job search platform Hiredly is looking for in its data analytics journey.

“As a start, we don’t need Ph.D.-level data scientists; we need a citizen level,” said ThenHui Chong, chief technology officer at Hiredly. “How can we enable the non-technical business users to understand data? It’s very important to build this data-driven mindset within the organization.”

Chong added many organizations practice data analytics using manual processes. To simply understand a pattern or identify a trend, business users must request data from different systems. He noted there are great opportunities for automation.

“A platform that gives users a single point of access… including (the automation of) how we collect data, setting the ETL process from different data sources, will be convenient,” he said. “In this case, the on-boarding or adoption of the data-driven culture could be much faster.”

While it is an admirable ambition to democratize data analytics, we are still far from bringing end-to-end automation of analytics into reality.

“We need to have a healthy dose of skepticism around exactly what can be automated,” said Lee Sarki, head of data analytics (life & health), AP, Middle East, and Africa, Munich Re.

As a provider of reinsurance, primary insurance, and insurance-related risk solutions, Munich Re’s business heavily depends on its risk assessment and analytic models. Sarki added when the cost of model error brings financial consequences to the business, the use of automation and expectations from citizen data scientists require careful planning.

“We need to be realistic about citizen data scientists,” he said.

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