" We built a single source of truth across our transaction data, and Quick BI gave us the layer to make it usable at scale. Today our teams monitor performance in real time and make decisions from the same numbers, across the entire TNG eWallet ecosystem."
— Marcus Naik,Head of Data & AI, TNG Digital
In Malaysia, e-wallets have evolved into essential daily infrastructure, driving significant transaction growth. As business accelerates, internal data capabilities are actively scaling to support this expansion. Currently, diverse metrics across departments stem from specialized data sources, presenting a clear opportunity to harmonize definitions and streamline reporting for more efficient strategic reviews.
TNG Digital operates TNG eWallet, Malaysia’s leading digital financial services and lifestyle app, with over 27 million verified users. Formed as a joint venture between Touch ‘n Go Sdn Bhd, a wholly-owned subsidiary of CIMB, and Ant Group, the company is now navigating this growth phase. Transaction data is distributed across four specialized systems—payment gateways, merchant platforms, user behavior logs, and customer service records. To maximize efficiency, the focus is now on unifying these sources. Currently, the team dedicates roughly 60% of their time to data preparation, setting the stage for a strategic shift toward deeper, actionable business analysis.
This phase presented three key areas for optimization. First, harmonizing metric definitions: different teams utilized distinct definitions for "MAU," meaning quarterly reviews initially required two days of data alignment before strategic discussions could begin.
Second, streamlining multi-system queries: analyzing merchant transaction trends required several days as analysts manually integrated data across platforms.
Third, balancing security with accessibility: as a regulated financial institution, TNG Digital prioritizes strict protection of user PII and transaction data, while actively identifying opportunities to streamline manual approval workflows for various teams. Addressing these areas accelerates the entire operating cadence, empowering the data team to focus on high-value strategic insights.
TNG Digital rebuilt its analytics on a cloud-native architecture on Alibaba Cloud. Transaction records, user activity, merchant APIs, and customer service logs flow into MaxCompute and AnalyticDB. Quick BI is the layer that everyone in the company actually opens.
The most visible change happened in monthly reviews. When the stakeholders across relevant business functions discussed transaction performance, they now looked at the same dashboards and the same numbers. The recurring "my number is different" argument went away — not because people compromised, but because the structural source of the disagreement was removed.
Previously, all data inquiries, such as analyzing user flow trends or seasonal patterns, were routed through a central team, which faced growing demand from across the business. Quick BI’s self-service model transformed this workflow. Featuring drag-and-drop dashboards, automatic cross-table relationship detection, and sub-second query responses, the platform empowers business users to explore data directly, independently of SQL expertise or analyst bandwidth.
Consequently, time-to-insight accelerated by 65%, reducing the process from days to minutes. This fostered a profound behavioral shift: data evolved from a requested resource into an integral, daily operational tool.
For a platform handling millions of transactions a day, even a small dip in success rate carries real cost — in money and in user trust.
Under the old workflow, a drop was flagged through a ticket, then investigated by a central team that queried logs across systems, manually aligned timelines, and identified the root cause. Hours at minimum, sometimes a full day.
With Quick BI's real-time dashboards and acceleration engine, transaction success rate, merchant response latency, and payment funnel metrics refresh at minute-level cadence. When a number drifts out of its normal range, the business team can drill down inside the same view — overall → channel → merchant → time window. Troubleshooting time fell by roughly 60%. The acceleration engine holds sub-second response on billions of transaction records while supporting hundreds of concurrent users.
Financial services require both robust security and rapid data access. TNG Digital achieved this balance through highly granular permissions. Quick BI’s row- and column-level security precisely controls data visibility by role and field.
Executives maintain a comprehensive view, while different teams across the business access their specific business lines. Sensitive information, such as user identity and transaction amounts, is automatically masked according to role, automating compliance and ensuring seamless access. This precise control fostered greater cross-team collaboration: when teams trust the system to enforce boundaries, they confidently share dashboards.
TNG Digital’s achievement represents foundational excellence: appropriately unifying, opening, and securing data. The 65% performance improvement stemmed from streamlining workflows, ensuring immediate data availability for business teams, analysts, and decision-makers. Compressing this access time from days to minutes accelerates the company's overall operating cadence.
As Southeast Asian digital payments evolve into everyday infrastructure for tens of millions of users, competitive advantage shifts from features to seamless data accessibility. Success relies on delivering precise metrics to decision-makers exactly when needed. Tools like Quick BI provide strategic clarity, ensuring that a robust data foundation is in place to support future innovations in AI, natural-language querying, and predictive analytics.
Looking ahead, TNG Digital's exploration of data value does not stop here. With traditional BI capabilities already powering its day-to-day business analytics, TNG Digital is now working with Lingyang to take data consumption to the next level through AI. Powered by Quick BI's AI capabilities (AIPro), the goal is to shift from "people searching for data" to "data reaching people" — delivering more proactive intelligent insights and anomaly alerts that bring critical business changes to decision-makers in real time, and enabling more multimodal, conversational ways for every business user to interact with data naturally. Together, the two teams aim to move analytics from "understanding the past" to "anticipating the future," continuously unlocking the business value of data.
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