The business world is facing technology disruptions that will redefine business models and strategies. To remain relevant, businesses have been encouraged to embrace digitalisation and transform their business models to utilise technological advancements. Among these innovations are data and analytics, which have become the heart of technological advancement applications. Digitalisation transforms the scale, quality and processing of data. This transformation then produces vast quantities of data that businesses can use to implement data analytics to optimise decision-making processes. Therefore, businesses need to understand generated data and know the ways to utilise advanced technologies to convert data into powerful business strategies.

In Singapore, small and medium-sized enterprises (SMEs) are a vital part of the economy, contributing to about half of the GDP and two-thirds of employment. SMEs are also an integral part of the vibrant ecosystem of Singapore’s industries. With digitalisation transformation, SMEs in Singapore are expected to implement data analytics to gain a strong position in the market.

To support digital transformation among SMEs, the Singapore government has put in place various supportive measures, such as the SMEs Go Digital programme, to help SMEs accessing global markets to transform themselves digitally. Despite these supportive measures, SMEs still seem to lag behind the large companies in big data analytics adoption.

SMEs often encounter challenges in choosing the appropriate technology or getting their staff involved in implementation. SMEs also face certain limitations, such as a lack of understanding of data analytics, cultural barriers and intrinsic conservatism, and a lack of inhouse expertise. While SMEs support the country’s economy through stimulating economic growth, increasing employment, and expanding exports, their adoption rate of data analytics remains lower than that of the large companies.

We (Singapore Institute of Technology), together with RSM Singapore and Institute of Singapore Chartered Accountants, conducted a study to understand the adoption of data analytics among Singapore SMEs. We examined their readiness and technological capability to adopt data analytics, the extent of analytics tasks embraced, the perceived “usefulness” of data analytics, and their reasons for adopting or rejecting data analytics.


A total of 575 SMEs, spanning commercial and professional services, engineering services, and food and beverage services, took part in a questionnaire survey conducted between November 2018 and April 2020. Over 60% of the respondents held senior leadership positions (for example, C-suite, director, manager, head of department, financial controller) in their respective organisations. The majority of the SMEs surveyed had less than S$25 million in annual turnover, and staff strength of fewer than 50.

Of the SMEs surveyed, 72% did not have designated full-time staff to perform data analysis. More than 50% reported having outsourced this function to meet their organisations’ IT needs, while 45% did not have the intention to send staff for data analytics training due to the cost and time required for such training (Figure 1).

Figure 1 Respondent demographics

Non-data analytics adopters

From the survey responses, 34.6% of the SMEs were classified as non-data analytics adopters as they had not adopted any data analytics in their organisations and had no intention to do so in the future (Figure 2).

Despite agreeing that implementing data analytics might provide some business value, especially in the area of enhancing staff productivity, these SMEs were sceptical that data analytics would generate real monetary savings for their organisations. They also reported the lack of IT infrastructure support, limited financial resources, and concern over data protection and privacy, as top potential deterrents for their organisations to adopt data analytics (Figure 3). Many of them were familiar with only spreadsheet and database as tools of data analytics, suggesting a lack of understanding and awareness of more advanced data analytics tools.

Likely data analytics adopters

A second group among the respondents, at 35.1%, were classified as likely data analytics adopters (Figure 2). They have not adopted data analytics in their organisations but had indicated that they were likely to embrace the technology in future. Compared to the non-data analytics adopters, this group appeared to be better equipped with technological capabilities and the knowhow to utilise more advanced database management systems and basic programming languages to manipulate data in uncovering trends and insights, as well as provide reports and interpret findings.

When asked what factors would eventually persuade them to implement data analytics (Figure 3), the group reported that performance expectations, effort expectancies, management support and government support would increase their intention to implement data analytics. Individual attributes of user experience, task usefulness and technological capabilities did not appear to influence their intention to adopt data analytics.

Data analytics adopters

A third, and final, group of respondents, at 30.3%, were classified as data analytics adopters (Figure 2) as they have already embraced data analytics in their businesses, using varied tools from basic software such as MS Excel to intermediate and advanced software such as SAS and Tableau. However, this group had not explored more advanced techniques such as text analysis, social media analysis, global positioning systems analysis and location tracking analysis (Figure 3). These SMEs saw business value in adopting data analytics to reduce their operating costs, enhance staff productivity, and provide better customer service.

Figure 2 Types of adopters

Figure 3 Why SMEs do not send staff for training


It was found that adopting and implementing data analytics presented a challenge to all three groups of SMEs. For the non-data analytics adopters and likely data analytics adopters, besides government financial support, more needs to be done to entice them to consider adopting data analytics. One possible idea is the sharing of success stories and real-life examples about how potential monetary benefits can be achieved with data analytics adoption. As success stories spread among the SMEs, more of them may be encouraged to consider embracing data analytics as part of their business model.

Another idea is the setting up of a streamlined funding-support framework. Although government financial support is readily available – a crucial catalyst to encourage data analytics implementation and minimise the implementation cost burden – SMEs face difficulty tapping on these grants. For some financial assistance schemes, SMEs may need to provide upfront funds to implement digitalisation transformation before they are able to take advantage of the schemes. Resource-scarce SMEs, therefore, could benefit from a streamlined funding-support framework.

Other key drivers that would encourage SMEs to adopt data analytics include having a good and easy adoption implementation plan, and a high certainty of realising better firm performance after implementing data analytics for their organisations.

As these SMEs are open to learning about how data analytics can benefit their organisations, having a shared services platform that these SMEs can tap into could be very useful in keeping costs down. Such a platform can be set up with consultants, professional bodies, government agencies or institutes of higher learning, to provide different levels of data analytics capabilities ranging from basic to advanced.

Basic implementation refers to the use of databases and spreadsheets to arrange the vast quantities of data that businesses can use to conduct decision-making optimisation analyses. Intermediate implementation refers to the ability to produce data visualisation and reporting. Advanced implementation refers to the ability to use advanced analysis tools such as social media analysis, text analysis, location tracking analysis and global positioning systems analysis. SMEs can select the appropriate level of implementation to suit their needs.

The data analytics adopters, too, faced challenges to adopt more advanced data analytics tools. They believed that data analytics business values could be realised through a good system design that suited their business structure and model, as well as through quality data analytics software. However, their lack of understanding of the benefits of advanced technology and their inability to manage the potential uncertainty that may occur when implementing such technology might result in hesitation to adopt more advanced data analytics tools and techniques.

In addition, given their limited IT resources and skills, they were concerned about data security and privacy. While a data analytics environment was starting to emerge in their organisations, the respondents felt that data analytics might not support cross-functional or company-wide decision processes. Hence, a lack of synchronisation or a missing systematic data analytics framework within their organisations is apparent.

A supporting platform to help SMEs design a data framework to ensure seamless analysis flow will increase the chance of realising the business value brought about by data analytics and eventually, monetary benefits for the organisations. Supporting platforms to enhance knowledge and skills could include awareness seminars on compliance with the Personal Data and Protection Act, as well as technical workshops on the use of advanced data analytics tools.


In conclusion, our study’s findings suggest that more can, and should, be done to boost data analytics adoption among SMEs in Singapore. This study is timely, as Singapore seeks to involve more SMEs to make the most of digital technologies to improve operations and generate new revenue.

The full report, titled “Data Analytics Adoption in Singapore SMEs 2020”, is available for download.

Associate Professor Koh Sze Kee (Principal Investigator) and Assistant Professors Arif Perdana, Desi Arisandi and Lee Hwee Hoon are members of the Singapore Institute of Technology research team for this project. The research team acknowledges SIT student research assistants Victoria Ng Mei Shu, Tan Sheng Yi Timothy, Ahmad Hashim Bin Suleman and Darrell Lim for their research support.