In today’s data-driven economy, organizations must learn lessons from episodes such as Singapore’s erroneous wage support payouts during the COVID-19 lockdown.

It was recently reported that S$370 million in wage support was wrongly paid out to companies under Singapore’s Job Support Scheme (JSS) to help businesses and employees tide over pandemic uncertainties.

The anomalies were first discovered in November 2020, and investigations finally traced the payments back to errors in the compilation and processing of company reopening dates, which were used to determine the payout amount.

This may come as a surprise, but lapses in data – in this case, company reopening dates – is inevitable, and it is critical for organizations to know how to manage their data effectively.

With the volume of data rising exponentially every day, how should governments and organizations navigate today’s data-driven economy, and how could we prevent such major lapses in the future?

For some answers to these questions, DigiconAsia sought out the views of Gary Chua, Chief Operating Officer, Asia Pacific & Japan, Syniti.

Gary Chua
Chief Operating Officer, Asia Pacific & Japan, Syniti.

What is the definition of bad data?

Chua: Bad data comes in many forms and, generally, it is when data is unfit for its intended purpose of use. As a simple example, take your personal contact list in your mobile phone – there is a high chance that it is not 100% correct. 

This might be a mild inconvenience for you but if you consider organizations that deal with large volumes of data daily such as those in emergency health services or the financial sector, bad data could determine whether a life is saved or whether someone gets paid.

Poor data quality can affect organizations in many ways, ranging from financial losses, reputational damage, regulatory compliance issues, to operational inefficiencies, among others.

How does an organization know whether the data they have is good or bad?

Chua: The tricky thing about data quality is that organization only really realize how bad their data is when its consequences are felt. Take the recent example of the errors in the Singapore Job Support Scheme payout. The impact was huge – S$370 million – and all because of bad data.

Look at it this way – bad data is inevitable unless you proactively do something about it. Good quality data requires a conscious effort to maintain. Fortunately, there are technology tools available these days to assess the quality of data and ensure that it is well-governed.

How can governments and organizations ensure and maintain good data quality to prevent errors such as the above?

Chua: 70% of digital transformation initiatives fail because of bad data. In order to beat the odds, organizations need to adopt a proactive approach to managing its data.

There are 3 key things to consider:

  1. One of the main causes of bad data is human error in data entry, so organizations need to make sure the people that are handling the data day to day are properly trained.
  2. It is important to implement effective processes and governance, so that the right check and balances are in place.
  3. Due to the exponential growth of data, the effective use of technology is becoming increasingly critical. Having an automated system can significantly minimize the time and effort it takes to manually investigate and fix bad data. This frees up valuable resources to focus on other important activities that is core to the business, resulting in significant value in both bottom line and growth for the business.

More information about the erroneous JSS payouts:

How long companies in specific sectors were paid an enhanced subsidy depended on how long they had to remain shut during the lockdown period in Singapore.

The mistakes occurred in the processing of applications submitted by companies in the construction, marine and process sectors. These companies had to get permission to restart projects, and would include its subcontractors, clients and other partners in its applications.

Due to the compilation error, the dates for the restarting of the projects were taken as the reopening dates for all the companies linked to the applications. As a result, some companies were deemed to have been closed longer than they actually were, and received higher wage subsidies.

To prevent future mistakes, Singapore’s Ministry of Trade and Industry has worked with the Ministry of Finance, the Ministry of Manpower and the Inland Revenue Authority to fix the processes and put in additional checks to detect possible errors, such as engaging an external auditor to conduct thorough checks on the reopening dates used in the computation of JSS payouts.