Nayoko: My background is mainly in building companies, angel investing in startups, as well as advising startups in the digital industry and tech start-up environment. Before Algoritma, I had worked for Rocket Internet. I was actually in the same team with the co-founders of Go-Jek, and we managed to build a company with over 200 people. That experience gave me very useful lessons about the rapid expansion of startups. Then, I worked with EMTEK Group for digital media for a while, became co-founder of a startup for the first time at Seekmi, and spent some time building up the Plug and Play accelerator.
Nayoko: While I was working on building up the Plug and Play accelerator - this was during 2016-2017 - it became very clear to me that one of the central challenges companies were facing was the talent shortage. Startups across Indonesia and Singapore were all facing the same difficulties of securing good talents with technical skills. In 2017, right around the time when my tenure at Plug and Play was ending, I met Samuel Chan, who would later co-found Algoritma with me. We realized we were concerned with the same issue, and so we started discussing possible solutions. While there are several, we decided on creating an avenue to provide data science education for both individuals and companies. Our vision is to democratize data science skills and provide employment opportunity worldwide.
Nayoko: Mainly because it contributes to job loss. Not only are companies struggling to find talent - individuals who do not possess these required talents are also losing job security. This instability that workers face today are being implicated in wider society as well. As unemployment and economic inequality increase, social unrest ensues and affects all of us. The US provides a perfect example. How did Donald Trump win the election? He took advantage of the resentment created by job losses to encourage racism and prejudice on immigrants, while in fact much of these job losses were created by automation and technological development.
Here in Southeast Asia, we still have some time. A part of our economy remains untouched by automation. However, we do not have much time. When the change happens - and it will happen, soon - it will be much faster than we expect. The growth of the business economy continues to be linear, while startups and companies in the tech space are growing at exponential rates. Take Grab or Google. Who expected that so many services and products will be rendered irrelevant in such a short period of time? We need to change our way of thinking, and we need rapid skill development. If we don’t start now, the challenge will only get more difficult. Individuals will find it even harder to adapt in the near future.
Nayoko: Many companies built on the traditional business models of the 1950s and 1960s are faced with the imperative to catch up and adapt to the changing technologies to remain competitive in an economy increasingly dominated by startups and companies from the tech space. At the same time, data science is becoming increasingly apparent as a key tool to harnessing technological developments. Demand for data scientists is constantly increasing. This presents both a challenge and an opportunity. Recent report point to data scientist as one of the top 10 jobs in the world for the next 5-10 years in which around 1,3 million new jobs will he created in this field.
Nayoko: For individuals, there are several ways to build your own capabilities. For one, you can take an online course. However, the issue with online learning is that you require a huge amount of discipline and motivation. This is especially challenging for a difficult course like data science. Algoritma offers offline learning through workshops as we think this is a more sustainable way of learning.
For companies, there are several ways to innovate. You can either acquire new companies to get their technology and know-how, or you can build your own internal capability by either outsourcing to the consultant in the short run or training yourself. Consultants are effective as a short-term solution, but they are expensive in the long-term. As for training, this is where you can truly build your own capabilities. It may be difficult in the short run, but it will pay off in the long run. Additionally, training opportunities can also increase job satisfaction for employees.
It’s time to transition into a Data Science career for every individuals also never too late to start building capabilities in Data Science in your organisation. Check out the upcoming Data Science workshop by Algoritma in Bangkok here: http://bit.ly/2Tyvs8K