Bridging the gap in data skills through citizen data scientists

IN A world where citizens' lives are increasingly managed by the accumulation, analysis and application of data, the need for data scientists is both obvious and growing. Unfortunately, there is a global shortage of people with the necessary skill set - and Asia is no exception.

According to the PwC Global Industry 4.0 survey, 82 per cent of companies in the Asia-Pacific anticipate that data analytics will have a significant influence on decision-making processes in five years' time.

But respondents said the absence of a digital culture and the right skills are major hurdles to making operations more digital.

For example, almost 40 per cent of the companies surveyed globally and 29 per cent in the Asia-Pacific rely on the analytics expertise of individual employees, but do not have dedicated data analytics departments, the survey found.

DATA-CENTRIC WORKFORCE, DATA-ILLITERATE WORKERS

The APAC Data Literacy Survey goes further - it sees an escalating skills gap in data management across the Asia-Pacific (APAC) workforce.

Their research reveals that a worrying majority of workers in the APAC region are suffering from data illiteracy in a workforce that is becoming increasingly data-centric.

Although expectations to use data at work are on the rise, findings from the survey revealed that four in five (80 per cent) of APAC workers are data illiterate.

This means that they lack confidence in their ability to read, work, analyse and argue with data.

Given the ever-increasing volumes of data and the need to understand the collected information, it is insufficient to just rely on tools without human talent.

Well-trained digital analytics consultants are crucial to ensure data-derived insights can be translated into effective business decisions.

DEMAND FOR DATA SCIENTISTS OUTSTRIPS SUPPLY

Yet, finding the right digital analytics talent is easier said than done. As demand for data analysts and scientists has steadily climbed, the supply has not kept pace.

But a potential solution to this challenge is in sight - from a perhaps unexpected source. The fact is, there is a large population of developers, analysts, engineers and business users who are attempting to fill the gap.

Who are they? Call them the "citizen data scientists", and get ready for them to make a major impact on the way enterprises manage their data science requirements.

They are staff already on company payrolls - smart line-of-business people who are not specifically trained in mathematics or statistics but whose daily responsibilities give them insightful perspectives on the business problems that could be addressed by big data solutions.

AUTOMATING REPETITIVE TASKS

The opportunity to productively deploy citizen data scientists arises from the software industry's new focus on simplification through the automation of tasks that are repetitive, manual-intensive and don't require deep data science expertise.

Advances in AI and cloud-based massive parallel processing are dramatically simplifying the process of building deployment-ready models. This is being driven partly by the structural shortage of well-trained and affordable data scientists. The premise is that fewer trained data scientists can use these tools to produce the same number and quality of models that required a much larger group much longer to do in the past.

Another driver is the ongoing push by some analytic platform vendors to "democratise" predictive analytics, by which they mean to make it possible for the citizen data scientist to build some of these models directly. There are many more analysts and citizen data scientists than there are fully trained data scientists - thus a more attractive market for platform vendors. As data volumes grow, it is impractical to expect the analytics to be administered only by a small priesthood of experts. There has to be a role for people in line-of-business (LOB) positions to pose, and solve, critical analytics problems. These individuals may lack the more advanced quantitative skills but their business expertise actually gives them deeper perspective on the marketing or operational problems to be resolved.

The organisations most likely to recognise this opportunity and successfully deploy citizen data scientists are those that have already developed a relatively open information-sharing culture. Companies that succeed in creating a recognised and trusted role for this new category of employee will be able to successfully blend skill sets, bringing together product and market knowledge with an understanding of the various aspects of data management.

Their employers are priming these individuals to be able to develop and administer models based on predictive or prescriptive analytics, giving them wizards and templates developed for specific kinds of business analyses, and to interpret the results for the benefit of other line-of-business (LOB) users.

In effect, they are being developed into specialists whose expertise sits between that of the data scientists and the business users.

BENEFITS OF CITIZEN DATA SCIENTISTS

The key benefit to organisations of this level of expertise is that it minimises the burden on their resources, by having their trained data scientists develop models around advanced data analytics, machine learning and algorithmic business, then make them available to the LOB managers and users who need them to make better decisions.

And the advantage to the average business user is the ability to use these smart data discovery tools to derive insights from advanced analytics, without needing to possess traditional data scientist expertise.

A further significant benefit to the individual citizen data scientist is the opportunity it provides for them to enter the world of data science from their day job, where they are subject matter experts. What's more, both the company and the individual benefit from this process - it becomes a retention strategy whereby the individual increases his or her value to the company.

When implementing a citizen data scientist strategy, companies must, of course, identify the right tools for specific usages. Tools meant for use by data scientists are not meant for citizen data scientists, simply because the learning curve would be too steep. On the other hand, in order to carry out their role, citizen data scientists do need a tool that does advanced analytics, not a simple data visualisation tool.

The advent of the citizen data scientist era boosts the potential for businesses in Asia to digitalise more rapidly and make productive use of the technology and tools available today.

To help nurture this new generation of talent, tech pioneers are supporting their development through a growing set of powerful self-service tools that put capabilities such as app development, artificial intelligence and machine learning within the reach of almost anyone.

THE LOW-CODE SOLUTION

One way of doing this is through low-code - by automating manual tasks that are difficult and time-consuming, low-code frees up people to do things that are much more important.

It allows citizen developers to easily create, test and deploy applications without having to rely on an IT team that is probably buried in application development backlog.

Low-code development fuels innovation by empowering staff to turn ideas into enterprise-grade applications in minutes and on their own - helping businesses to go digital smarter and faster.

With a low-code platform in place, enterprises can solve their own or their customers' business problems and address opportunities independently, without time-consuming special training. They can innovate faster with disruptive products and services that exceed customer expectations. Feedback can be captured quickly and new versions deployed in the marketplace with no delay.

The predictive analysis of Big Data is rapidly becoming an indispensable business tool, but its real value lies in applying the insights gained to the business, so as to achieve a competitive advantage for the organisation. And the best way to make this happen is for highly trained data scientists and their citizen data scientist colleagues to work together, delivering a collaborative, company-wide focus on data-driven strategies.

The citizen data scientist is here to stay.