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How Pakistan’s Largest Islamic Bank is Embracing Data Analytics

Jawad Raza, SVP and Head of Data Analytics, Big Data and AI at Meezan Bank, outlines how he’s transformed the way the bank thinks about data

Pakistan is far from the most data-driven nation in the world. But while 65% of the country still lacks access to the internet, companies like Meezan Bank are racing to modernize and harness the power of data.

Since 2017, Jawad Raza, Meezan Bank’s Head of Data Analytics, Big Data and AI, has been spearheading the bank’s data transformation. In this episode of Data Conversations Over Coffee, he outlines how this transformation is gathering pace under his leadership.

“We’ve come on, over the past year and a half, leaps and bounds,” he says. “We’re in the process of installing our entire end-to-end data platform and we’ve got one machine learning use case in place as we speak.”

For Raza, this journey started by understanding what data the bank had, assessing its quality and creating a data strategy that prioritized use cases built around the best quality data. From there, he set about securing buy-in for his plans.

“It’s been a huge learning curve for everyone,” he remarks. “It took 6-8 months to, I would say, educate the entire bank.”

To do this, Raza began by explaining to key stakeholders that the bank was only using 4% of the data at its disposal. This realization helped him to show them that they would need to upgrade their infrastructure to become more data-driven.

So that staff understood exactly what his team could help with, he also agreed company-wide definitions for concepts such as ‘AI’ or ‘big data’. Meanwhile, he built data governance into the bank’s data strategy from the outset.

“As far as I know, in Pakistan, we’re the first and probably the only bank that is starting with data governance as part of its original strategy,” he notes. “From the ‘get go’, we understood that data governance is a strong pillar for data analytics.”

In this way, Raza says the bank will improve its overall data quality over time and ensure more advanced analytics and AI use cases will deliver ROI as it continues its data journey.

Key Takeaways

  • Data democratization is key. Being data-driven means ensuring entire organization has the ability to use relevant data to improve decisions or processes
  • Build an end-to-end data platform. Use cases delivered in silos are hard to update to account for model drift and aren’t scalable
  • Invest in your people. The success of any data strategy depends on recruiting and retaining good talent and constantly upskilling staff