Digital Transformation = Geek x Data x Business

Recently, I read a book titled “Digital Transformation: A Field Manual,” authored by two Koreans, Park Soo-Jung and Kim Kuk-Hyun. Delving into the secrets of digital transformation is the main focus of this book, as indicated by the title. There’s a standout sentence in the book's preface.

For those thinking about business transformation or launching new ventures to disrupt stagnant markets, keep this formula in mind.

DT = (Geek + Data) x Business

How is “Geek” defined in this context? In the preface, it’s explained as ‘talent transformed by digital.’ Digital transformation cannot occur without individuals who recognize its necessity, plan, execute, and evaluate its results to lead long-term change. Taking this perspective into consideration, it would be more precise to write the formula like this because if Geek equals zero, DT would also equal zero.

DT = Geek x Data x Business

The pharmaceutical industry is witnessing a growing interest from companies in incorporating AI drug discovery. There were comparable demands when I was at CJ Healthcare, which is now HK inno.N. Even around 2016-2017, when I thought of ‘AI’ as nothing more than a buzzword interchangeable with terms like ‘CADD (computer-aided drug design)’ or ‘big data,’ I believed all research data needed to be well-organized in a database. When I started at the company, my initial responsibility was to set up a molecular modeling foundation and develop a database for storing drug discovery data. Setting up a data capture system was essential for our digital transformation efforts. The company recognized this need, which is why they hired me.

Setting up the molecular modeling foundation was within my control and manageable, but building a compound database to store research information required changing most researchers’ workflows, making it a challenging task. The difficulties weren’t just with researchers; meeting the IT management department’s requirements and security standards added layers of complexity. The most challenging aspect was setting realistic goals within a long-term plan for digital transformation. While theoretically digitizing ‘all data’ is ideal, practically, it’s crucial to differentiate between short-term and long-term goals and allocate resources accordingly.

The project was completed at a specific level tailored to the situation, and upgrading that level became someone else’s responsibility after I left the company. Referring to Shionogi’s case mentioned in another article, we can see that all companies are planning and executing digital transformation at some level according to their circumstances. Before achieving AI drug discovery, having the data visualization system discussed in this article obviously requires digital transformation first.

The role of Geek can be explained through XPERT:

• X: eXperience

• P: Problemitize

• E: Envision

• R: Revolutionize

• T: Transform

Recognizing problems through experience, envisioning solutions, revolutionizing workflows, and ultimately transforming businesses are tasks that people must undertake. Securing individuals who understand and can drive these processes is the first challenge for any company aiming for digital transformation.

When I visited Kakao, one of Korea’s largest IT companies, to speak with an AI expert about this process in 2017, he mentioned, “There are so many datasets within Kakao that can create enormous value through analysis. Why should we focus on digital transformation for pharmaceutical companies, even with a need for dirty works to capture data?” I was speechless.

Can you attract top AI talent if you’re still grappling with data capture? In order to compete with industries that use data analysis to generate ideas and evaluate them in real-time, automation of this process is essential, even if not in real-time. At the very least, pharmaceutical companies should have this level of readiness, though they need to make certain preparations (details will be provided in a separate article) in order to be ready for the AI era and attract AI experts. They need to keep talented individuals who can meet this minimum requirement. Does your company have such talent?

Key takeaways:

1. Essential Elements of Digital Transformation: Digital Transformation (DT) requires ‘Geek’, data, and business, where ‘Geek’ refers to talent transformed by digital means. This formula implies that the role of ‘Geek’ is critical in DT. Without ‘Geek’, digital transformation cannot occur.

2. Importance of Data Management: For AI-driven drug discovery, organizing all research data into a database is essential. Differentiating between short-term and long-term goals and appropriately allocating resources is crucial.

3. Readiness and Attraction of AI Talent: To compete in the AI era, companies need automated systems for real-time data analysis and idea evaluation. Having a well-established data management system and being prepared to utilize data effectively is essential for attracting and retaining AI experts. Companies must ensure they have the necessary talent to meet these basic requirements.