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HOME NRI JOURNAL Preventing Data Scientists From Falling Into the Programmer’s Pitfall


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Preventing Data Scientists From Falling Into the Programmer’s Pitfall

Kotaro Kuwazu, Division Manager of the Center for Strategic Management & Innovation

#Data analytics

Nov. 19, 2021

It is said that data scientists—and big data, a sine qua non of their profession—have been attracting the public’s attention from 2010 onward. The massive accumulation of data over the internet has birthed new perspectives and approaches via digital technology, and has brought a transformation away from the conventional image of “statistical analysis” to what we now know as “data science”, which derives new insights that are scientifically and socially beneficial in areas including information science, algorithms, and a wide range of other fields.

The ever-growing importance of data science

Data science departments and courses have sprung up at many universities, and data scientists are being trained and assigned as specialists particularly in marketing departments. As a result, the field now enjoys widespread recognition as a profession involving the handling of data, and it has come to occupy an essential place in government agencies, companies, universities, and other institutions.
And much like highly-skilled actuaries in the insurance field, data scientists have even had an effect on existing occupations, with their skills and capabilities being highly prized in the context of traditional work operations.
In addition to advances in digital technology and corporate DX developments, with the progress of DX in our society at large, data and the corresponding data science approaches are playing a crucial role not only in the study of sales and supply chains, club member characteristics, and other quintessential subjects for analysis, but also in terms of functional hierarchies supporting new socioeconomic activities, including general human activities, the use of networks, and the consumption, generation, and distribution of renewable energy, for example.
One noteworthy approach is that involving digital twin technology, as seen for instance with smart cities and Beyond 5G, whereby social and industrial activities themselves are projected onto or recreated in a large-scale digital space. It is thought that digital science will take on a new role in linking activities in the real world with the digital realm, in terms of creating more sophisticated predictions from modeling, or controlling models, for example.

The lack of recognition of data science at Japanese companies

Meanwhile, if we look over Japan’s industrial sector, compared to the situation in the West, where data scientist “stars” are born, take on leadership roles in their industries, and are highly regarded by students with career aspirations in the field, the situation currently leaves much to be desired.
With the rise and proliferation of data science, the field is no longer just the purview of a small group of analytics and statistics specialists, but is instead becoming a major occupation made up of cutting-edge human talent in academic fields, mid-level staff handling operational analyses and assessments, and even younger base-level personnel. Yet nevertheless, one can sense that at some Japanese companies, data scientists are still regarded distinctly as specialists, and treated as a “niche” occupation, albeit a highly sophisticated one.
Although this situation has been pointed out before, it is the same circumstance as the human resources problem that has affected the programming field, and in the medium term this problem could become a weakness for Japanese companies.
Data science no longer just involves professionals doing research in cutting-edge fields for academic purposes, and going forward it will increasingly take on the aspect of a general skill used daily in corporate environments and operations, depending on their tools or infrastructure. Rather than being used by a limited few, data science is a field where all personnel will find it important to acquire a basic background understanding and minimum level of skill.
If we think of this together with the “programmer problem”, we may surmise that there are two basic elements underlying this issue. One involves a superficial dichotomy between the sciences and the humanities in Japanese society, whereby people self-identify as “liberal arts” types, or think of analysis as the work of science-oriented persons. The other entails a sort of tacit, shared cultural understanding that occurs in a village-type social structure, where people tend not to see much need for logical explanations. At the very least, although efforts are underway to teach programming more in school curriculums, it will still take a considerable amount of time before university graduates are automatically expected to have a certain level of programming comprehension regardless of whether they majored in the sciences or the humanities. Furthermore, the concern is that if things stay as they are currently, data scientists will likewise remain a minority, and end up being celebrated as some exalted special group of people.
At this stage, data science should not be confined as a specialized academic field for “science-oriented people”, but should instead be regarded as something essential in the form of general knowledge and skills. The old-fashioned reading, writing, and arithmetic (plus English) may have given way to the Word/Excel/PowerPoint trio of the PC age, but going forward it would seem that communication skills (long-distance, cross-cultural, team-to-team etc.), analytics/data science (running analyses and providing logical explanations), and visual expression (expressiveness not limited by textual constraints) will be the skills that companies demand of their personnel.
Even when we consider the impact of the Covid-19 crisis, communication and visual expression are skills that Japanese companies will likely work on acquiring of their own accord, regardless of the science/humanities split.
That said, although knowledge and skills in data science will be absolutely essential for quantitatively measuring how things work and change, ascertaining their structure, and explaining them logically, the trouble is that at the current rate, efforts in this area could lag well behind those involving communication and visual expression skills.
Excel is not a science-related tool used only by science-oriented persons. By the same token, analytics and big data also should not be regarded as scientific tools or something specialized. This sort of mental block will only shackle the competitive abilities of Japanese companies, just as it did with the “programmer problem” before.

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