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Customers in Asia regularly ask me questions like “How do we develop our analytics team?”, and “Where can we find data scientists?”. Data and analytics are already critical to their businesses, and the importance of using data resources effectively is growing. But there’s just one thing–Data-skilled professionals are scarce.


And without the analysts and data scientists they need, organisations are going to find the transition from traditional organization to data-driven company a long and testing one. According to this year’s annual Gartnersurvey, 73 percent of companies have invested, or plan to invest, in big data. Only 13 percent have actually deployed big data products.




The data-skills shortage is not a short-term problem, and as companies ramp up their recruitment efforts they will face serious and growing competition. Smart leaders will play the long game, making sure they develop a deep source of quality talent, rather than making ad-hoc appointments in response to last-minute requirements.


As part of this long-term planning, many are investing in standardised curricula, institutional programmes, and university-based courses. For instance, Infosys Infosys and Stanford recently announced a partnership to develop curricula for data science in India. EMC EMC has targeted Thailand as a data-science hub and plans to introduce training courses for university students.


However, university training courses are not the answer.


Good data scientists thrive in the open. That’s why leading urban tech centres have active data communities. Data Gotham came out of the New York City data community. Start-ups in Cambridge and Boston participate extensively in ‘R’ and ‘Python’ Meetups, and create new endeavours like hack/reduce. Data Science for Social Good Fellows (I was one in 2013) collaborate closely with the Chicago open government and data communities. Facebook’s data scientists devote time to non-profits like Bayes Impact.


Similar communities are springing up in Asia, and companies keen to develop their data capabilities should invest in them. But investment means more than just financial support. I’ve seen Microsoft Microsoft host the ‘R’ Meetup groups in Boston and Singapore, bringing those communities into their facilities and making them accessible to their employees. PayPal, MapR Technologies, Yahoo Labs and many other companies regularly send their data scientists and analytics professionals to speak at the Singapore Data Science Meetup. Teradata employees have engaged in datathons and Meetup groups across Europe, and in Malaysia and Singapore. At even greater levels of engagement, businesses like Prudential Singapore and Royal DSM have collaborated with the Dextra Data Innovation Challenge to harness the creativity of crowd-sourced data science.

Get involved with your local data community – don’t miss out.


Community and social activity around data and analytics can also provide indicators for commercial development. I’m frequently asked about ‘data hotspots’ in Asia, because decision-makers want to know where they can build off-shore analytics teams, where they can recruit staff, and even where to base their data science capabilities.


Meetup groups are a dataset I pay attention to, and to show the growth of communities in South and Southeast Asia, I wrote a light crawler to collect data on Meetup groups in the region‘s major cities. The graph below shows the total number of members of all Meetup groups involved with work related to data (from Hadoop, to Business Intelligence with Excel).




This summer, Bangalore and Singapore were chosen as two of the first five international DataKind chapters. The Singapore chapter leaders also founded the very active Singapore Data Science Meetup group. Cities with dynamic data communities have a broader population to draw on than universities. They enable students and young professionals to move out of the rigid and overly-sterile environment of classrooms and get involved with real-world data projects.


A solution grown from real-world problems


Useful data science isn’t learned by studying answers to static, already-solved questions from textbooks. The best data professionals learn by struggling to make incremental improvements to less-than-perfect, working solutions. More importantly, by engaging with diverse local communities they experience the same social and team-based environments they’ll encounter in the workplace.


However promising as a concept, data communities face serious challenges. It’s difficult to put together activities, on a regular basis, that inspire people to give up their evenings and weekends to work with data. This is where businesses can step in to offer resources, expertise, interesting problems, and data.


In return, they mark themselves as open-minded, innovative, and risk-taking organisations: the kind of organisations that good data scientists want to join. And companies that ignore their local data communities are missing out on an incredible opportunity.


The chance to nurture, and tap into an invaluable resource that will shape their data-driven future.   





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