Twelve years ago, I pioneered the first Big Data Engineering graduate program in Asia and simultaneously launched my big data consulting company. With over a decade of experience in the data domain, I have witnessed a remarkable one-hundred-eighty-degree shift in opinions from data-driven is a hoax to data-driven is a miracle.
Data-driven is a hoax
1. We don’t have data
One of the most common concerns I encountered was the belief that companies need more data. However, this is quite different. Companies, regardless of their size, generate some form of data. Yes, you do have data; you do not have it ready for analysis.
Grab, a Southeast Asian ride-hailing company, leveraged ride data to optimize routes, predict demand, and improve services. This showcased how even basic operational data can be a goldmine of insights to improve efficiency and customer satisfaction.
2. We are too small
This is another top-hit data-driven hoax. Actually, the small size makes it easier to become data-driven due to agility and fewer complexities. Small is beautiful.
Love, Bonito, a Singaporean online fashion retailer, uses customer data from purchases and browsing to tailor its marketing strategies. This illustrates that small businesses can use data to enhance customer experiences and drive growth.
3. We trust our experience
This is another classic quote that I heard so many times in the beginning. Experience is valuable, but combining it with data leads to even better outcomes.
Vinamilk, Vietnam’s company, trusted farming techniques but began using data analytics and optimization and predicting market demands, significantly improving its operationalefficiency and market responsiveness.
4. We do not have a budget
I would be so rich if I collected a penny every time managers said this. Many free or low-cost data analysis tools can suit businesses with tight budgets.
Go-Jek, an Indonesian multi-service platform, started with cost-effective analytics solutions to understand customer behavior and optimize its services before scaling infrastructure as it grew.
5. We do not have a data team
Personally, I have not heard this myself, but I have heard this a lot from my students. Outsourcing or using cloud-based analytics services can provide the expertise needed without the overhead of a full data team.
Cloud Kitchen, a small Thai startup that provides shared kitchen spaces for restaurants, used cloud-based analytics to understand delivery times and locations. This demonstration shows that even without a dedicated data team, businesses can leverage data for strategic decisions.
6. We don’t trust data
Another classic quote, I usually hear this from the upper managers. Building trust in data starts with small, demonstrable wins that show its value.
Tokopedia, an Indonesian e-commerce platform, initially faced internal skepticism about data-driven decision-making. By starting small, such as using data to optimize their search function and showing a positive impact on sales and customer satisfaction, they gradually built a culture that values and trusts data-driven insights.
Data-driven is a godsend.
Time is changing. It is hard for me to believe that in less than five years, the opinions of managers will turn around one hundred eighty degrees. Adopting a data-driven approach can sometimes lead to unrealistic expectations, especially when the nuances of cultural and market dynamics in Asian contexts are not fully considered. Here are ten misguided expectations, specifically highlighting examples from Asian companies of the complexities involved.
1. Rapid Transformation and Results
Start today and transform tomorrow. I understand the need for speed in business, as I am also a business founder. However, this is a lot like building a house. Without a strong foundation, the house will crumble down soon. Build too fast, and soon you will fall.
Many of the Southeast Asian e-commerce startups invested heavily in data analytics to personalize shopping experiences but didn’t see immediate sales boosts. Transformation into a data-driven company is a gradual process. It requires not just investment in technology but also in training staff and altering the company culture, which takes time to yield results.
2. Flawless Predictive Analytics
It is true because AI said so. Wow, I can not believe that I actually live to hear this sentiment. Do not put all your eggs in a flawless predictive basket.
A Japanese electronics manufacturer relied on predictive analytics for inventory management but faced overstock issues due to unforeseen global supply chain disruptions. Predictive models are based on historical data and patterns. They may not accurately predict future events, especially those without precedent, highlighting the importance of flexibility and adaptability in planning.
3. Complete Automation Is Imminent
Most parking tickets in Thailand shopping malls are now mostly automated. However, it will take many generations before complete automation is achieved. Imminent is like perfection; it is unattainable.
A South Korean manufacturing giant aimed to automate its production lines automatics fully but found that human oversight was still needed for quality control and unforeseen issues. While automation can significantly increase efficiency, the complexity and variability of real-world scenarios often require human intervention to ensure quality and safety.
4. Unbiased Decision Making
I do remember that Face recognition was trained mostly with Caucasian facial features, and it did not work very well. The bias will always exist in the data, just like in all of us.
A Chinese HR tech company developed an AI-driven hiring platform, which inadvertently favored candidates from certain universities, reflecting biases in the training data. Algorithmic decisions can embed and perpetuate existing biases, underlining the need for continuous monitoring and adjustments to ensure fairness and equity.
5. Data Will Replace Human Expertise
Yes, your manager’s gut instincts can be correct. I remember clearly when one of my students told me that her boss didn’t really believe the data report. It is just too far off from her experience. In the end, her boss was actually right. So, always retain human expertise.
An Indonesian agriculture tech startup developed a data-driven crop management system but found that farmers still valued traditional knowledge for understanding local ecological conditions. Data insights complement but cannot replace the nuanced understanding and expertise that comes from years of experience in a specific field or industry.
6. More Data Guarantees Better Decisions
We have more data than anyone. We will definitely make better decisions than all of our competitors. Without derived meaningful insights, it is just eating up storage space.
A Singaporean finance firm collected vast amounts of transaction data but needed help to derive meaningful insights due to the noise and complexity of the data. The utility of data is not proportional to its volume. Collecting targeted, relevant data is often more beneficial than amassing large datasets without clear objectives.
7. Data-Driven Cultures Are Easy to Build
In the last five years, I have witnessed many companies make a claim that they are data-driven organizations. Today, I am still waiting for the results. Last year, I saw these same companies make a claim that they are AI-driven companies. Where are the results of data-driven transformation?
A Malaysian family-owned business faced resistance to adopting data-driven practices from employees who were accustomed to decision-making based on hierarchy and seniority. Cultural shifts towards embracing data require careful management of change, addressing both organizational and cultural barriers to ensure buy-in from all levels of the organization.
8. Immediate ROI from Data Investments
I am saving the best for last. Unless you considered PR value as the immediate ROI from data investments. It will take a while before your company reaps the ROI. So be sure to have a sensible OKR and KPI. So either fire your CTO fast or give him/her a bit more time.
A Thai retail chain implemented an advanced data analytics system expecting immediate cost reductions and revenue increases but found the ROI materialized over a longer period. Investments in data analytics often have a delayed payoff. Businesses need to be prepared for the upfront costs and the time it takes to integrate these systems effectively into their operations.
The evolution from viewing data-driven approaches as a ‘hoax’ to a ‘miracle’ and navigating the subsequent wave of over-expectations reflects a maturing understanding of the data’s role in our world. It emphasizes the need for a balanced, nuanced, and ethical approach to data-driven decision-making. As we continue to navigate this landscape, let us embrace data not as an infallible oracle but as a tool that, when used wisely, can significantly augment human decision-making, creativity, and innovation.
o
Alex.Worapol.Pongpech 
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