National Data Sharing Policy Discussion panel: Data is the new currency, yes, but…

  • Panellists recommend being focused in scope to data necessary for objectives
  • Industry & regulator engagement key to ensure access to right data provided

National Data Sharing Policy Discussion panel: Data is the new currency, yes, but…

Adopting data analytics can be a game-changer for companies looking to leverage the value of data in decision-making. Malaysia has recognized this potential and has been proactively developing its big data analytics (BDA) market. As far back as 2014, MDEC had established the National Big Data Analytics (BDA) Framework, and according to IDC, the Malaysian BDA market is expected to grow from US$1.1 billion (RM5.22 billion) in 2021 to US$1.9 billion (RM9 billion) in 2025. Similarly, the Southeast Asia data analytics and business intelligence market is forecasted to grow at a CAGR of 7.25% from 2023 to 2028.

Despite these optimistic projections, companies face various challenges in adopting data analytics. In a recent panel discussion organised by Malaysia Digital Economy Corporation (MDEC), industry experts shared insights on overcoming these hurdles to harness the power of data analytics and improve productivity.

One key takeaway from the discussion was the importance of starting with specific business decisions in mind. George Chua, Chief Data Officer at QSR Brands, emphasises the need to work backwards from the decision at hand. "You start with the decisions that you want the research to inform. And then you say, Okay, this is the decision I'm trying to make: Do I launch product A, product B, or product C?" 

By determining the relevant and necessary data for effective analysis, it helps companies to determine what data is needed for effective analysis, rather than collecting all possible data.

Alongside this, panellists encourage companies to adopt an experimental mindset and conduct small pilots or experiments before fully implementing analytics initiatives. Chua stresses that even large companies need to be willing to try things on a smaller scale first, stating, "Even big traditional companies can change." This approach allows companies to test and iterate on their data analytics strategies, mitigating any potential issues or surprises that may arise.


Talent: Qualifications are nice, relevant skills are better

However, as with all discussion in things related to tech, the topic soon turned to the issue of talent. Data specialists are currently in high demand, and while qualifications are important, they are not the be all and end all. "A lot of hiring managers discover that people with certifications can't really execute projects," says Chua. 

Instead he suggests looking for those strong in critical thinking and are detailed-oriented who can navigate data quality issues and ensure accurate analysis. "These kinds of attitudinal orientation are very hard to teach. Some people are just more careless, and lack attention to details, despite the fact of having an analytical degree."

Indeed, he finds that the best data scientists are those with a background of accounting or architecture. "Not all of them are actually trained in AI."

Building data skills and capabilities within the workforce was another key topic. Sharala Axryd, Founder and CEO of The Center of Applied Data Science (CADS), believes that data analytics can no longer be the reserve of those in the ivory tower of IT or technical support. "You cannot expect IT graduates or mathematicians to all be centralised in a data science group, because then they become the bottleneck for the organisation."

In particular, she emphasises the importance of reskilling existing employees through projects and online courses. "It's basically no (longer) upskilling, it's reskilling now," she says. "There's so many tracks out there, there are free courses, but the challenge has always been, where do I start?"

Sharala suggests a tool like the complimentary online assessment being offered by MDEC to help organisations identify their data and analytical readiness as well as AI readiness. Participants who complete the assessment will receive a personalised report on how their organisation can progress to the next level, perhaps by looking at courses from Coursera, LinkedIn, and Code Academy, as well as CADS. 

However, while certifications might ensure a theoretical understanding of concepts, the real test of aptitude comes into play when managing and executing data-related projects. As Chua says, "(Qualifications) get you someplace but actually the best way to learn is to work on real life projects."


Visualising data: Tell stories with your data and take the red pill

The panellists also touched upon the importance of effectively visualising data and insights. They recommended books on data visualisation by Alberto Cairo, Edward Tufte, and Hans Rosling, which provide insights into how data should be presented to effectively communicate insights. He emphasised the importance of telling stories with data, stating, "Once you start learning about effective data visualisation, it's like taking the red pill from The Matrix - you'll never see data presented the same way again."

On the whole, the panel agrees that while technology has given companies the opportunity to benefit from data analytics, organizational culture and mindset are as important as technology tools in unlocking value from data through a customer-centric and curious approach.

Participation in discussions and shaping the national data sharing policy is also crucial to create an environment that enables data sharing while protecting individuals and ensuring compliance. With the right approach and the necessary skills and capabilities in place, companies can harness the power of data analytics to drive growth and innovation in today's digital economy.

To the last point, Sharala referenced a recent paper titled "Navigating the Jagged Technological Frontier" published by the Harvard Business Review that found that consultants using AI were more productive, completing 12% more tasks, finishing them 25% faster, and 40% higher in quality when compared to those without AI. But for these technologies to provide benefit, it must have access to the right data, from both a technical and policy perspective.

As a result, addressing regulatory and legal considerations around data sharing is crucial. Bernard Sia, Chief Information Officer of Manulife Malaysia, urged companies to view regulations as guidelines rather than roadblocks, and to seek as many points of informed view as possible.

"In working with (regulators) it is very important that we get a view from experts in the field, because sometimes corporate lawyers may not even be exposed to data per se or compliance."

As Bernard advised for companies looking to engage regulators and lawmakers, "Are we willing to have a discussion? Are we willing to be curious about what is out there? And how to work within the confines so that we can have data sharing that can create value? Because data, if it stands alone (and) is hidden somewhere, it's practically useless."


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