Dive into data alchemy with Sohum
By Aiden Jewelle Gonzales
Data: a deceptively small word that encompasses so much of our current existence. I remember when I first watched The Matrix (1999) as a child, I was blown away by the concept that our world could simply be a construction made of ones and zeros. Now, over two decades into the 21st Century, with the exponential advancements of Artificial Intelligence (AI), our reliance on technology to know exactly what we want before we even know it, and the sheer amount of information that we’ve entrusted to the internet, the truth isn’t far out – our lives are comprised of, and run by, data.
Sohum Sachathamakul, Principal Data Engineer at McKinsey & Company Thailand, is someone who’s answered the siren call to learn more about this fascinating and all-encompassing subject. Born and raised in Bangkok, his journey towards understanding Advanced Analaytics began with his Bachelor of Computer Science from Mahidol University International College, where his passion for the subject grew after a course related to Machine Learning and Big Data. “The idea of not only being part of the future, but hopefully influencing the future, was and is something that has kept my passion in tech alive,” he tells me. “It’s a critical part of all companies, especially with the skyrocketing growth of software development, cloud computing, and artificial intelligence.”
Affable and animated, Sohum is far from your usual technological recluse – while his work may live in the cloud, his head is certainly grounded in reality, showcased by his gratitude for his family and loved ones. “I’m forever grateful to my parents, who were very influential in helping me cultivate my passion and education in computer science, who pushed me to grow and learn more, and always invested in my education,” he says. “And of course, my wife is a true partner in every sense of the word. We just recently got married in June 2023, and she supports, pushes and inspires me both in my career and in my personal life,” he says with no small amount of pride. He spoke to Masala further about his love for his work, how AI and Advanced Analytics are changing the world, and what we can expect in the rapidly changing technological landscape of the future.
What were your professional experiences prior to working at McKinsey & Company, and how did you feel like those experiences helped shape who you are today?
I first tasted the ‘real world’ industry through a Data Science Internship at a large Telco company in Thailand. It was the first opportunity to intersect theory and real-world practice. It was a great learning experience working with ‘real world data,’ which has generally poor data quality. What I quickly learned, was that with poor data quality, any form of analytics is simply not possible! This inspired me to pursue the field of data engineering – setting up the platform and foundation of data to enable data-intensive use-cases, such as advanced analytics. I spent two years in Agoda as a Data Engineer in their Content & Machine Learning team. There, I strengthened my passion and interest in data engineering, through my involvement in three projects relating to ‘Big Data’ and AI Chatbots. Both of those experiences helped me realise that data is where my passion is.
You’ve worked in technology-based companies and consulting-based companies in Thailand – What about McKinsey’s vision and USPs has garnered your loyalty for the past few years?
The nature of consulting is extremely unique and rewarding. The continuous learning and growth are something which I have greatly valued. In consulting, every time you are assigned to a new project, you will get the opportunity to work on vastly different topics; amongst diverse companies, new and unfamiliar industries, and with a new, multi-disciplinary team. For a data engineer consultant (or any similar tech consultant role), it’s especially rewarding to be able to work on different technology stacks. I was not only able to experiment with, but build different cloud providers, programming languages, tools and frameworks. The continuous learning on both the leadership and technical fronts is definitely a USP I value most!
Tell us exactly what your role as Principal Data Engineer entails.
I work as a Principal Data Engineer at QuantumBlack, the AI arm of McKinsey & Company. At QuantumBlack, we unlock the power of artificial intelligence (AI) to help organisations drive radical performance improvements. The methodology of QuantumBlack is called ‘Hybrid Intelligence.’ This is where we harness the power of AI combined with human insight to give a competitive advantage to help organisations innovate and thrive.
QuantumBlack offers end-to-end AI transformation by partnering with organisations to successfully adopt AIT at scale. It was founded in 2009 and born and proven in Formula 1. In 2015, McKinsey & Company announced its acquisition of QuantumBlack, and since then, QuantumBlack has been fully integrated in McKinsey & Company, and has been driving performance impact with clients around the world. Some of the exciting work we’ve done recently is helping the Emirates Team New Zealand to win the America’s Cup title in 2019 with a McKinsey-built AI sailing bot; and helping Telkomsel, a telco company in Indonesia, build a new data analytics platform to better understand its customers and drive personalisation.
QuantumBlack has diverse roles in Artificial Intelligence including Data Engineers, laying the groundwork to enable analytics, Data Scientists, applying advanced analytics techniques to solve business challenges, Machine Learning Engineers, deploying machine learning systems to scale, and Analytics Translators, bridging technical expertise with operational expertise to ensure insights translate to impact.
As a Principal Data Engineer, I partner with multiple parties, from senior digital clients to C-level executives, to build data platforms and analytics solutions. I lead large multidisciplinary teams to help clients build and deliver large-scale data and advanced analytics transformations with state-of-the-art technologies in software engineering, cloud, artificial intelligence and Machine Learning Operations (MLOps).
Everyone’s always curious about what it’s like to work for a global corporation, especially one of the management consulting world’s ‘Big Three.’ What are some surprising things that you can share about the corporate life, and do you have any recommendations for anyone hoping to enter this field?
Before joining McKinsey & Company, I come from a background of technology and computer science. As a matter of fact, I didn’t know what the consulting industry was, nor had I ever heard of McKinsey! It wasn’t until a close friend of mine told me that I “could not ignore McKinsey” when I began to interview.
McKinsey has rewarded me with life-long learnings both professionally and personally. Some highlights include:
The obsession with people’s personal development – The consulting industry is unique in the sense that there are no ‘products’ that are sold. It is us, the people, that are deployed to work on various projects across the world. That is why McKinsey invests heavily in people. I’ve felt that in two powerful ways. Firstly, the ‘feedback culture’ is extremely strong. Everyone in the company, from a new-joiner Business Analyst to a Senior Partner, is expected to give and receive feedback on a continuous basis. This promotes constant professional development. Secondly, embedded in our values is to “uphold the obligation to engage and dissent”. This states that it is each person’s obligation and responsibility to speak up about an idea which we have – regardless of seniority or role! For example, you can (and are obliged to) challenge a Senior Partner on something you don’t agree with. Just bring your facts and supporting arguments!
Another highlight has been my introduction to several C-suite executives. On my first days at McKinsey, I was assigned to a new project. Upon entering the client’s building, I was awash with a feeling of complete anxiety, as the Associate Partner on the project brought me to the floor where all the clients were sitting. Ten minutes later, he asked me to join a meeting with the clients. During the introductions, I was shocked to learn that I was sitting in the room with the COO and CPO of a large publicly traded company! I could not process what just happened, but I immediately learned that working here gives me a ‘seat at the table’ with the clients!
Finally, I was pleasantly surprised to learn that I’m often at the forefront with the clients. Operating as a ‘techie,’ it took me by surprise to truly be hands-on with the clients – literally! One of the most exciting projects that I have been part of was when we were helping a client in rural Indonesia improve their operations with advanced analytics. What was truly exciting about it was that it took one international, one domestic, and one twin-propeller flight to arrive at one of their worksites. Next, I needed to put on my safety equipment, including a high visibility vest, safety goggles and steel-toe boots. Never would I have thought to be ‘rolling up my sleeves’ as a techie! It was truly a once in a lifetime experience!
Tell us a little about the data trends that you foresee in the next few years, based on your experience.
I believe the data trends for the next few years will be focused on Generative AI (GenAI). GenAI are algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Since the release of OpenAI’s ChatGPT in November 2022, companies have raced to embed GenAI’s capabilities in their business functions. According to the McKinsey Global Survey report on The state of AI in 2023 published on 1 August 2023, one-third of survey respondents say their organisations already use GenAI in at least one business function. 40 percent of respondents say their organisations w ill increase their investment in AI overall because of advances in GenAI.
GenAI’s potential impact can be seen in two lenses: Introductions of new GenAI usecases: GenAI brings a new set of potential use-cases which can be applied to industries. For example, applying GenAI to create personalised marketing emails, which reduces the time invested to create the content, as well as enhances effectiveness for higher-quality and personalised content. Increases of worker productivity: GenAI can impact time spent and work productivity on work activities in 850 occupations, across 2,100 work activities. According to the McKinsey report, The economic potential of generative AI, new GenAI usecases can add 15 to 40 percent to the economic value that we now estimate nongenerative artificial intelligence and analytics could unlock. On top of this, the increases of worker productivity due to GenAI can add an additional 35 to 70 percent. Both could add USD 8.7 trillion to USD 12.3 trillion in the global economy annually (shifting AI’s total potential impact from USD11 – USD17.7 trillion to USD 17.1 – USD 25.6 trillion).
Aside from GenA1, what are other trends you foresee?
Firstly, according to McKinsey’s report on McKinsey Technology Trends Outlook 2023, developing AI at scale (whether its Generative AI or others) will continue to be very important for organisations to scale their AI across their company. This is important to ensure that the end-to-end lifecycle of an AI model is built to scale – not just one or two use-cases, but rather in the hundreds.
Secondly, I believe the way that organisations think about their data will be forefront. Many of our studies found that companies have shifted, and will continue to shift, to treat their data as products. Data products can deliver a high-quality, ready-to-use set of data that enables people across an organisation to easily access and apply to different business challenges.
Lastly, our report on McKinsey Technology Trends Outlook 2023, shows that cloud and edge computing will continue to be a significant accelerator to all industries in their journey to become a data-driven organisation. Cloud enables faster time-to-market, simplified innovation, easier scalability, and reduced risk. Now as the cost of cloud computing declines and adoption increases, AI use-cases will have a faster time-to-market with more advanced capabilities (leveraging the out-of-box cloud services). The rise of cloud-native tools (tools which are designed to operate on the cloud) such as Snowflake and Databricks will enable organisations with the latest, out-of-box innovations on their cloud platform.
As someone who deals in technology and data, how do you cope with the continuous innovations and ever-changing technological landscape, and how do you keep your skills current?
Technology and data indeed are a fast-moving universe! The best way to keep your skills current, is not to try to consume as much information as quick as you can, but rather to pick and choose where you want to go deeper in. No one can possibly be an expert in all the technologies, tools, articles, research papers and programming languages – it’s simply not possible. You will need to design your learning goals, which is something I do every quarter.
Once you have identified your learning goals, there are lots of online learning material courses (e.g. Coursera or Udemy), and tech blogs (e.g. Hackernoon or Medium). You can also perform in competitions (e.g. Kaggle), attend conferences (e.g. the Tech in Asia Conference or the Data+AI Summit) or even experiment with open-source tools. On top of self-learning, having the right network for your area of expertise is crucial to ensure that you have the right collaborators and support on your learning journey.
