Axiata Group Berhad is a Malaysian telecommunications conglomerate with extensive operations in Asia. It is the country's largest wireless carrier. We spoke to Pedro URIA-RECIO, head of Axiata Analytics Center, about the work he is doing and analytics in general in Malaysia.
Pedro URIA RECIO is a strategic and hands-on marketing leader with more than 15 years of track record in technology and advanced analytics in emerging markets.
As the head of Axiata Analytics Center, Pedro Uria-Recio leads advanced analytics across the operational companies of Axiata Group, a Malaysian telecom and digital conglomerate in 10 Asian countries. With a cross-functional team of business consultants, data scientists, engineers as well as digital professionals, Pedro focuses on marketing analytics, digital growth hacking, artificial intelligence and automation as well as cost analytics for the telecom networks.
Prior to this role, Pedro was a management consultant in the technology and banking industries at McKinsey & Company, where he worked on strategy, analytics, process digitization, customer experience, digital marketing and transformational programs. Pedro also worked in IT strategy and architecture in Veolia Water Asia Pacific in Hong Kong and Shenzhen (China), as well as in Research & Development in France Telecom in Paris. Pedro holds an MBA from the University of Chicago Booth School of Business and a bachelor’s degree in engineering from Bilbao (Spain).
Analytics India Magazine: Pedro, what is Axiata Group and what is your role there?
Pedro Uria-Recio: Axiata is one of the leading telecommunications and digital groups in Asia with approximately 350 million customers and with a presence in 11 countries in South Asia and South East Asia.
I am a business professional at the intersection of analytics / AI and marketing. I am currently leading the data analytics and AI transformation of Axiata. Prior to Axiata, I was a consultant at McKinsey. I studied an MBA at The University of Chicago Booth School of Business.
In this interview I am sharing my personal views about how analytics and AI play a role in business and the social sector, but these opinions do not necessarily represent those of my affiliations.
AIM: How important is analytics & AI within to the telecom industry?
Pedro: The telecommunications industry services billions of customers daily and generates enormous amounts of data. There is no doubt that analytics and AI are crucial for the industry’s future but there is certainly a debate about whether the return on investment is worth pursuing. Those operators that figure out how to leverage these technologies will thrive; those that do not will be left behind.
While the voice business used to be very profitable before WhatsApp, the same cannot be said for mobile data. The telecom industry is immersed in a paradox where volumes increase exponentially every year and prices fall exponentially every year. India is an extreme example due to the emergence of Reliance Jio.
Looking into 2019 and beyond, with a high likelihood of recession in the global economy, shareholders are demanding shorter term cashflows at a time when massive investments in 5G, the next generation of mobile communications, are on the horizon. However, it is unclear which new services will really need 5G’s ultra-high speed and, if they do, whether customers will pay extra for them.
In this context, analytics and AI can make a difference through both cost efficiency and revenue stimulation initiatives. Revenue initiatives such as churn, upselling and cross-selling have been explored for decades in telecom but analytics-driven opex and capex optimization are less explored. For this reason, there is a huge value potential for cost-efficiency though analytics.
Additional there is also a significant opportunity in using data to fuel new revenue streams outside the core business, while preserving customers’ privacy. However, this potential is not easy to materialize at a significant scale. For example, in countries with low credit card penetration such as Indonesia, Vietnam or the Philippines, some operators have managed to create analytical models that predict customers’ ability to pay back banking loans based on their telco payment behavior.
AIM: Can you elaborate on some specific use cases of data science or AI that you worked on?
Pedro: I would briefly describe some revenue stimulation and cost optimization use cases that I find most promising according to my experience.
On the cost side, the single largest opportunity in telco is optimizing the billions of dollars the industry invests in network equipment every year including mobile and fixed, which requires analyzing greats amounts of network traffic data, subscriber records as well as external data.
But cost efficiency through analytics does not stop there. Analytics can also help optimizing online and offline advertising spend. Robotic Process Automation, an AI technology, can bring substantial short-term returns by automating repetitive processes in support functions such as finance and IT. Finally, AI-driven chatbots, video-bots or intelligent agents can bring in high scalability and better customer service at lower cost.
On the marketing side, telecoms have a large experience using simple data models to target marketing campaigns on SMS, as I explained above. Today AI makes it possible to learn through complex analytical models the best action in terms of customer experience or best products to recommend to every single customer. Additionally, AI enables operators to completely automate these campaigns. This approach is most likely to bring incremental revenues when external data from third parties is integrated with internal sources and when additional channels on top of the traditional SMS are used.
AIM: What are some of the challenges that the industry in Malaysia faces in terms of AI adoption.
Pedro: Companies in Malaysia face many challenges when it comes to deploying AI and Analytics. But these challenges mainly fall under two categories: building data assets and, most importantly, using those data assets to transform the business.
Regarding the first kind of challenges, IT investment in Malaysia has been focused on more traditional assets like Enterprise Resource Planning with very limited budgets for AI. Multiple industries lack the adequate technological systems to track the operational data flows that are required by AI programs to make decisions and trigger actions. Even if they have acceptable data capture set up, many organizations lack the right infrastructure to store data, aggregate it into actionable forms, and make it available to users or machines for decision making.
Regarding the second kind of challenges, companies even in industries at the vanguard of adoption, struggle to find a programmatic approach to using their data assets they have built to transform the entire enterprise. In many companies, data remains in silos with split ownership. In others, huge data sets are collected but never analyzed. A programmatic approach to monetize AI needs to identify clear use cases where AI or analytics can help solving real business problems; It needs to empower employees to using these systems and tools. It needs to integrate data and AI with operational workflows; and finally, it needs to establish an open culture that embraces making decision through data-driven experimentation.
AIM: Is AI talent an issue in Malaysia? If yes, how can we resolve this?
Pedro: Malaysia faces a shortage of technology talent, including analytics and AI, as it attempts to bring IT up with the digital world. In fact, Malaysia has unveiled a plan, to reach at least 20,000 data professionals and 2000 data scientists by the year 2020.
Having said that, Malaysia is in good shape in comparison with other countries in the region. Establishing a faithful comparison framework is complicated. But if you count the number of people who claim to know machine learning on linked-in, you will see that Malaysia has approximately 11 of them per billion USD of GDP. This looks small in comparison to Singapore or the USA with 33 and 16 respectively. However, Malaysia scores far better than nearby countries, such as Thailand, Cambodia and Indonesia, which respectively have 5, 4 and 3 machine learning professionals.
Malaysia is a relatively developed economy by regional standards and a country with widespread use of English. Additionally, Malaysian universities, such as the Asia Pacific University, the Multimedia University or the University of Malaya, offer bachelor’s and master’s degrees in analytics. The private sector is also contributing to developing a talent pool of data professionals. For example, Axiata Group last year organized the largest datathon in South East Asia (ex-Singapore), where 100 brilliant data enthusiasts, out of 300 candidates, competed to solve a data problem and to create a suitable business model for it.
In fact, hiring data scientists in Malaysia is not a mission impossible. But requires creating an attractive employer value proposition, sense of purpose, sense of fit and learning opportunities as well non-traditional remuneration approaches specifically targeted to millennials. On the contrary, in Indonesia, talent is far scarcer. Indonesian data scientists are typically online self-learner, who end up working for the Indonesian unicorns, the only ones that can afford paying a Singapore-level salary in Indonesia.
AIM: How do you see the analytics ecosystem flourishing especially in South East Asia region?
Pedro: Today there are just a few major hubs for AI development: The Silicon Valley and New York in the US, which have pioneered many applications; Shenzhen and Beijing in China coming up fast; and Bangalore is emerging in India.
In South East Asia, the A.I. scene is led, no doubt, by Singapore with strong government support for entrepreneurs. Jakarta is developing as a satellite hub due to the massive opportunity scale of Indonesia. Kuala Lumpur is a second potential satellite hub because of good quality of talent and ease of doing business.
Having said that, most of the region will need to build foundational digital and data infrastructure to realize the opportunity of AI. On the flipside, our countries are starting from a cleaner slate than developed countries, and they are less likely to be held up by legacy systems and regulations.
AI use cases have sprung up across South East Asian countries, but they are limited in number and scale. Banks and telecom operators are the main sectors now adopting digital technologies and advanced analytics, which are the forerunners of AI.
AI initiatives in South East Asia should focus on developing applications that are practical for industries and social objectives in the region, but not necessarily on more groundbreaking solutions that are being developed by global tech giants in the US or China.
AIM: How can governments and citizen associations come together for a healthy discussion as well as implementation of AI?
Pedro: The market will drive the development and adoption of AI. However, governments can play a critical role to deliver the benefits to society. I see five priorities for governments when it comes to AI and analytics:
- Establishing a policy framework, ideally a regional level, to support AI development and adoption. Regulation requires defining data ownership, which is not yet totally understood by business and policy makers alike. Additionally, regulatory frameworks require striking a balance between data availability and privacy to protect an individual’s personal information for the purposes of commercial transactions. Some aspects involving AI ethics will also benefit from clear policy frameworks. For example, in which cases do companies who use AI have an obligation to explain how their machines arrive at recommendations? Having said, overregulation, such as forbidding storing or transacting customer records, even encrypted, outside national borders, can hinder AI development, for example cloud technologies.
- Converging public debate on ensuring that AI contributes to inclusive business growth and constructive social outcomes. Business leaders and civil society also have valuable input into the questions AI will oblige us to make. South East Asia will have to find its own answers that work its cultural and political context.
- Making government data available to businesses in machine-readable formats and deploying AI in government use cases such as detecting tax fraud.
- Developing AI talent, making long-term investments in education, including continuing education to help mid-career workers to keep pace with the digital economy.
- Supporting the development of AI hubs as epicenters of talent, AI entrepreneurship and development as well as commercialization.
AIM: What is the biggest trend in data science/ AI that you look forward to in 2019?
Pedro: The biggest trend I can see in South East Asia in 2019 is the emergence of the Chief Data Officer or Chief AI Officer, which should be the same role. 20 or 30 years ago Chief Marketing Officers were not in the C-Suite. 5 Years ago, Chief Digital Officers were also not part of the C-Suite either.
Many organizations in South East Asia, which are still at an early adoption stage, are debating internally about the organizational design required for AI. What is the organization for analytics and AI? Should it be centralized or decentralized? Where should analytics fit in the organizational chart?
The biggest reason why transformational programs, including AI fail, fail is lack of leadership and organizational commitment. Therefore, the answer to these questions that has been proved successful in American and Chinese companies passes through the elevation of the Chief Data & AI Officer.
A successful Chief Data & AI Officer has a business-oriented profile which allows him to understand the details of the most critical business problems the company is facing and to design analytics and AI driven solutions for them. At the same time, he needs to have the technical capabilities to be able to follow every single technical conversation with his technical teams.
I believe South East Asian companies will seriously start hiring senior Chief Data & AI Officers in 2019, can drive a drastic business transformation across the whole organization using analytics and AI.
Disclaimer: opinions in this interview do not necessarily represent those of the interviewee's affiliations
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Bhasker is a Data Science evangelist and practitioner with proven record of thought leadership and incubating analytics practices for various organizations. With over 16 years of experience in the area of Business Analytics, he is well recognized as an expert within the industry. Earlier, Bhasker worked as Vice President at Goldman Sachs. He is B.Tech from Indian Institute of Technology, Varanasi and MBA from Indian Institute of Management, Lucknow.