The research aims to understand how data-sharing affects employee productivity and decision-making using a qualitative Malaysian Administrative Modernisation and Management Planning Unit (MAMPU) case study. The finding shows that MAMPU developed two (2) datasharing portals; one (1) is for open datasharing, and the other is for internal and restricted data or Malaysian Government Central Data Exchange (MyGDX) that links data-sharing from various agencies in the public sector. The research found that data-sharing could potentially influence employee productivity and performance as data are valuable resources that could help employees effectively perform their jobs. This research also found that the absence of data-sharing can restrict the efficiency and effectiveness of the employees in acting fast and taking action to improve and enhance their operations and responsibilities. The finding also shows that data-sharing, especially internal data-sharing, consists of much critical and impactful data that are not mostly and openly shared. Thus, relying only on open data without internal data will affect the effectiveness and completeness of decision-making. Quality data is highly critical as low-quality data will cause ineffective decision-making quality. Therefore, more quality decisionmaking requires open and essential data from internal data-sharing as it may have more valuable data critical for sound decision-making. In addition, the result found that employee skills and analytical capabilities are more important than just having sophisticated and/or automated tools, as without human capabilities and skills, the effectiveness of data-sharing and the quality of the data shared may be affected. However, both sophisticated and/or automated tools are very useful for affecting decision-making quality and work effectiveness. The research also highlights the importance of data-sharing practices in driving innovations, as many data-driven opportunities can be made possible with data sharing.

Today’s business environment is characterised by fierce competition, technological advancement, and the big data revolution; organisations need to act fast and utilise the resources efficiently and effectively to capitalise on the opportunity and make effective decisions fundamental for the organisation’s survival and sustainable performance (Klus & Müller, 2021). In the era of big data eruption, digital and information technology have become the nexus of the industrial revolution; thus, organisations need to invest, adapt, and respond to this quickly and accurately to capture market share (Ghobakhloo & Fathi, 2019). Big data explosion creates voluminous data-sharing, which is pivotal for an organisation’s success. Unsurprisingly, data-sharing has become the building block of the big data revolution. Studies have found that data-sharing has caused a significant impact on the operation and quality of the business in terms of improved services, enhanced organisation performance, increased innovation, and quality of decision-making (e.g., Santoro et al., 2019; Chatterjee et al., 2021), it also advances research and benefits society (Darch & Knox, 2017). The lack of data-sharing and big data analytics capabilities can hinder the significant effect of accurate predictive values (Dubey et al., 2019), thus hampering the decision-making quality and directly reducing the organisations’ performance and competitive advantage, innovation as well as employee productivity.

Although data-sharing is fundamental for organisational success and crucial for advancing the big data industry (Janssen et al., 2017), not many organisations are willing to share data openly (Welch et al., 2016; Santoro et al., 2019). Some share only certain data and only for internal use (Ghasemaghaei, 2019). Researchers pointed out that organisations are reluctant primarily due to various reasons, such as lack of trust due to no sophisticated tools to deal with privacy data management, property rights, and intelligence data, as well as lack of big data analytics capability, causing organisations to be careful with open data-sharing (Wimmer et al., 2016). While most organisations (public and private) understand the crucial role of data-sharing in improving their predictive values of decision-making accuracy, which is important for performance effectiveness, many are still prudent and insecure but just satisfied with sharing data internally between departments within the organisations. Some may share data with direct collaborators and important partners to improve their business operations but are restricted to the area directly dealing with the collaboration work. This creates a loop about whether internal data-sharing has the same effects as open data-sharing regarding the predictive value of decision-making on organisation and employee effectiveness. Studies on open data-sharing have exhibited the positive impacts of data-sharing on decision-making quality, innovation, performance, and competitive advantage (Bedini et al., 2014); however, there was a lack of studies on the effect of internal data-sharing on similar outcomes.

Therefore, the research objectives (ROs) of the study are:

Among the leading impacts of data-sharing is its effectiveness in bringing positive outcomes and profit to organisations (Mohamed Ali, 2021), leading to increased performance. Thus, organisations cannot ignore the significant role of performance in its survival and effectiveness as to date, with the highly competitive business environment and digitalised economy, there is a growing pressure that compels organisations to utilise big data analytics to achieve competitive advantage and gain the market share (Wang, 2018). With the current phenomenon, organisations cannot just rely on traditional performance achievement but need to increase innovation and possess analytical capability aligned with the recent technological development of big data and a digitalised economy to achieve better and more sustainable performance.

Undoubtedly that the quality of the decision-making determines the success of the organisation’s overall performance (Ghasemaghaei et al., 2018). Big data through data-sharing helps organisations, managers, and employees create values that contribute to quality decision-making. Many organisations have started to believe they would incur a loss if they do not use big data analytics to get accurate data for business decisions and capture this vast market share (Chatterjee et al., 2021). Using big data analytics could improve real-time decision-making and contribute to overall employee and organisational performance (Loukis et al., 2019). In the public sector, for example, data also plays an important role in making better-informed decisions across public sectors, contributing to sound policymaking (Mohamed Ali, 2021).

Data-sharing refers to the disposition or preservation of data for public access with the purpose of providing access for reuse (Chawinga & Zinn, 2019). It is also called open data or a deliberate effort to make raw data fully available for public access (Dong & Li, 2017; Chawinga & Zinn, 2019). Data-sharing facilitates research and transferring into values through data reuse, making data interoperable (Logan, 2021). Data-sharing differs slightly from knowledge-sharing or information-sharing as both typically refer to the exchange or sharing of knowledge or information among employees in the organisations they acquired or created (Gibbert & Krause, 2002; Rutten et al., 2016). Hogel, Partboteeah, and Munson (2003) define knowledge sharing as the exchange of experience, information, skills, tacit information, and explicit information among employees. Other studies, such as Wiewiora, Trigunasyah, Murphy and Coffey (2013), define knowledge sharing as expert insights, while Wagner (2008) defines it as the act of making knowledge available to others (either within the firms or outside the firms). Similarly, as defined by Xiao, Zhang and Basadur (2016), information sharing refers to members (employees) delivering information to each other. Therefore, what makes data-sharing different from information-sharing or knowledge-sharing is that data-sharing is more comprehensive and includes raw data, primary or secondary data, facts, knowledge, evidence, and experience. It goes beyond the academic world (Hulsen, 2020). Therefore, it can be understood that knowledge sharing, and information sharing are part of data sharing.

In Malaysia, most private sector organisations focus on internal datasharing to improve their systems, enabling data-sharing between departments before sharing the data with other organisations (Malaysian National Data-sharing Policy, 2019). Although open data-sharing has a huge potential, many are concerned about the protection of the market share, the cost and benefit of data-sharing, the ambiguity of datasharing that could violate personal rights and the development of a sustainable data-sharing model. Some prefer internal data-sharing for following ISO/IEC 27000 (international organisation for standardisation in terms of data security) which ensures the security of customers’ and partners’ personal data. It is very important for organisations to also classify their data-sharing into open data and/or confidential/private to ensure data security. Following ISO/IEC 27000 will protect the data and thus make it safe to be shared openly (Mohamed Ali, 2021).

Data classification allows the storage and processing of the data into categories (Saravanan & Sujatha, 2018). This will increase the organisation’s data security/data protection. Data classification can also help the organisation conform to their internal data protection in accordance with ISO/IEC 27000 of data security. Studies (e.g., Tankard,2015; Kitsios et al., 2023) have indicated that data classification could enhance data-sharing security, thus improving the organisations’ data-sharing policy.

The NVivo automated text analysis results based on the written reports shows that MAMPU is involved in government open data-sharing, where MAMPU links data from government agencies to an open data platform called data.gov.my. MAMPU is also involved in internal data-sharing through MyGDX portal for a more restricted/internal data-sharing, which is shared through the Application Programming Interface (API) upon request from the data users and must be approved by the data owners. In general, the result indicates the importance of open and internal data-sharing in the government agenda. Figure 1 displays the word cloud result from NVivo analysis.

The results from NVivo automated text analysis support that data-sharing affects employees’ performance and productivity. Based on the word cloud generated from the analysis indicates that the words “impact, performance, effectiveness, effectively, efficient, quality, improving” are linked to internal and open data-sharing.

The results of the interview with MAMPU’s data experts and policymakers about the effectiveness of data-sharing practices on employee performance/productivity show a mixed outcome. This is mainly because MAMPU, as a central agency, acts to link data from agencies into an open data-sharing portal (i.e. data.gov) and for internally restricted data on MyGDX. MAMPU does not collect data directly from the data owners/sources. Therefore, MAMPU does not have the actual statistics to confirm that data-sharing has a significant positive impact on employee work outcomes (i.e. performance/productivity). For instance, the data expert states that:

However, according to other experts, sharing data among employees can positively impact on their performance and productivity. Data-sharing can make their jobs easier by increasing work effectiveness. The data helps employees solve problems, reduce errors, and enhance the quality of their work, leading to high-value outcomes.

In this case, the expert indicates the importance of internal data-sharing regarding the database by referring to the case involving Bantuan Rakyat 1 Malaysian (BR1M), Bantuan Perihatin or e-kasih under the Lembaga Hasil Dalam Negeri Malaysia (LHDN). For example, all data regarding the receivers of BRIM, Bantuan Perihatin and e-kasih in the system in LHDN are collected internally and not directly from the public. For example, LHDN collects the data from the Jabatan Pendaftaran Negara (JPN) and other agencies and decides the receivers’ suitability before LHDN channels the funds to them. Some verifications, validations, and data updates from the data owners/sources were performed later and from time to time to improve the service. This example shows that internal data-sharing could affect performance/productivity positively. In addition, the data-sharing portal developed by MAMPU links data from various agencies on one (1) platform as in open data (data.gov) and restricted/internal as in MyGDX, which provide data that are accurate, reliable, and highly operability thus help the agencies or employees, including the policymakers to perform their tasks and responsibilities more accurately, with minimum errors, thus helping employees to achieve better performance and increase productivity.

The results from the NVivo software indicate that data-sharing, in general, is vital for decision-making. To understand the findings better, the interview content was analysed more thoroughly.

The results indicate that the quality of policy development, especially for policymakers, relies heavily on the accuracy and quality of data collected. The decision-making process and policy outcomes will suffer if the data is inaccurate or of low quality. To mitigate this, policymakers take the initiative to gather accurate information by visiting relevant agencies to better understand of the issue and learn from past experiences. Seeking advice and guidance from the Chief Government Security Officer (CGSO) or legal advisers also helps policymakers gain comprehensive knowledge before developing policies. According to one (1) of the policymakers:

Version 2.0 of the government’s open data-sharing developed by MAMPU added a data dashboard and data catalogue which is considered useful for decision-makers as a reference prior to in-depth investigation. Thorough checking of data-sharing from MyGDX can also further enhance decision-making quality.

The policymaker in this study also believes that accurate and realtime data-sharing between government agencies could improve the efficiency and effectiveness of many important decisions. For example, the recent increase in prices of commodities like fish, chicken, and local rice could have been avoided if the Kementerian Perdagangan Dalam Negeri, Koperasi dan Kepenggunaan (KPDNKK) had shared and monitored their data to predict the sudden price rise caused by shortages. With the help of reliable data-sharing, the Minister or top management could make fast and accurate decisions to direct agencies under the Kementerian Pertanian dan Keterjaminan Makanan Malaysia (KPKMM), such as the Department of Veterinary Services (DVS), to issue more licences for chicken breeding and farming, request the Federal Agricultural Marketing Authority (FAMA) to monitor rice production, and the Lembaga Perikanan Malaysia (LKM) to supply more fish. By sharing data between all agencies, the Kementerian Perdagangan Dalam Negeri (KPDN) could predict and forecast shortages and surpluses of essential food supplies, and order relevant agencies to increase and monitor the supply, thus preventing shortages and stabilising prices. This could also help avoid conflicting statements from different agencies regarding shortages, surpluses, and the prices of supplies.

The result from the NVivo software shows some findings regarding the relationship between quality, and sophisticated and automated tools in data-sharing (see Figure 4). However, it is not clearly outlined whether data quality is as important as or better than sophisticated and automated tools’ usage in affecting decision-making quality. Thus, to answer this research objective clearly, it is important to look at the three (3) main factors:

All the experts interviewed acknowledge the critical importance of having comprehensive and voluminous data-sharing to determine the quality of the data collected. The policymakers believed that data quality could be determined based on comprehensive/voluminous data-sharing. Without voluminous data-sharing, policymakers may find it difficult to determine the quality of the data gathered from the data-sharing. According to one (1) of the experts, both voluminous and high-quality data are very important as they will have a high impact. This kind of data could cover a variety of aspects, and the analysis will also show accurate and comprehensive coverage that is useful to many people and organisations. The data expert states that:

However, the expert also warned that highly voluminous data may include irrelevant data which can create noise in the decision-making. This noise will distract the policymakers and/or decision-makers from focusing on the real cause of the main issue to solve, this may affect the decision/policymaking efficiency and effectiveness.

The MAMPU has developed two (2) data-sharing portals, namely data.gov.my and MyGDX, that enable data-sharing from various agencies on a single platform for open data and for restricted data. However, it is the complete responsibility of the data suppliers/providers (i.e. agencies) to ensure the quality of the data they provide. As the agencies collect data from sources (or data owners), they must ensure the accuracy and the integrity of the data. The agencies conduct several verifications before data warehousing and data-sharing. Especially for restricted data-sharing in MyGDX, the data is encrypted as it is confidential and restricted. Thus, only the data users who receive approval from the data owners or the data suppliers will be given an API to access that data. Even for open data, it is highly important to have high data quality, especially in an open data portal (data.gov.my). The portal provides the data dashboards/data visualisation and data catalogue, which need high-quality data to provide accurate and useful dashboards. The open data-sharing portal version 2.0 provides high frequency, high granularity and high impact data, resulting daily views of more than 20, 000. Thus, it is highly essential to have high-quality data.

Based on the interview outputs, data-sharing involves many automated tools to improve its quality. Most of the agencies, especially those that share data via MyGDX, use tools for their data-sharing. Even without specific tools, the agencies share data using API technology, and use the Secure File Transfer Protocol (SFTP), which is a network protocol for securely accessing, transferring and managing large files and sensitive data. As the data expert states that:

Raw data or data from original sources often contain mixed categories of information that may be useful, but also confidential and private. Due to the Personal Data Protection Act (PDPA) and the requirements of the CGSO, agencies are not allowed to share data without first classifying it according to the relevant categories. Data classification is essential for sharing data and for data visualisation or analytics. The classification process should be done by the agencies themselves as they have a better understanding of their data and can ensure that the classification is done accurately. According to the data expert:

According to the experts interviewed, analytical skills are crucial for employees as tools alone cannot compensate for human skills. Employees with strong analytical skills not only perform their jobs effectively but also help organisations save costs, as they can replace the need for expensive tools. The data expert interviewed emphasised the importance of analytical skills for employees, as they are essential for maximising the use of sophisticated tools. The expert also agrees that sophisticated/automated tools are useful for policymaking, if the development of that policy requires voluminous or comprehensive data; thus, the ability or analytical skills to use the software and analyse the data are crucial to own. However, the data quality does not depend on the ability/analytical skills of the employees. The quality of the data must be guaranteed and verified from the start of data creation. Thus, this policymaker asserts that high-quality data are the data that are correct, valid, and unquestionable. Thus, data-sharing should be based on the principle of “single source of truth,” which means that data-sharing from various agencies should be consistent with data from the original source. For example, all data about individual identities must be similar to the data that are kept or shared at the JPN, especially regarding names, identity cards, addresses etc. JPN is the single source of truth if the agencies require confirmation about the data related to individual identity. The expert in the interview states that:

The analysis using the NVivo software shows an overall influence of data-sharing on innovation, thus answering research objective four (4). However, the word cloud below does not indicate the specific recommendations for data-driven innovation in improving performance. Thus, this research resorts to the content analysis method to derive more insights to answer this research objective.

The findings are summarised into three (3) parts:

For the data to be valuable or have business value, it needs to be shared. Through data-sharing, the data creates values that are beneficial for employees, businesses, and society. There are many ways organisations can use data to improve performance. First, through data-sharing, organisations can have data on their employees’ productivity, whether they need training and what kind of training. Employees can also monitor and forecast their own performance if organisations practice data-sharing related to HR practices. For organisations, data-sharing through predictive analytics and foresight could help them take precautionary steps and develop strategies to prepare for future challenges and be ready to face the problem when it comes. Data-sharing also helps decision-makers, policymakers, leaders, and managers to make accurate and informed decisions for problem-solving. Second, through data-sharing, the organisation can increase its business values, for example, by creating applications. Without data, applications may not be useful to the business. However, with data-sharing, many applications can be developed to benefit the business, create value, and help the people and society. Organisations can not only sell their products or services and reach more customers, but earn additional income by displaying advertisements and links and by charging fees from other vendors and individuals. Data-sharing also provides real-time data, which is required by many people, including businesses and society in general. This not only helps organisations but also boosts other businesses and generates more opportunities for data-sharing.

The expert recommended that data management should primarily follow the Government Wide Reference Architecture (GWRA) for effective data-sharing practices. The GWRA ensures that the standard of data usage in the delivery of government services is uniform. It can be used as a foundation for providing digital services to society. In-depth knowledge of GWRA Data is essential to determine the value of data in government service data-driven smart initiatives, data-sharing and data analytics. GWRA provides a framework for categorising of Government Data based on the service owner/service provider and the legislation that allows data to be stored by the government. It also ensures data security in inter-agency data-sharing such as government open data-sharing and MyGDX. Therefore, GWRA should lay out the data architecture or structure before data-sharing for effective data-sharing practices.

The government’s MAMPU agency conducted extensive research on data management practices in various countries before developing an open data-sharing portal and MyGDX. These portals are widely regarded as best practices and are often used as a reference by other countries, particularly in the ASEAN region. The World Bank has also recognised their effectiveness. Despite this, MAMPU continues to seek out even better practices, as stated by the expert:

Version 2.0 of the open data-sharing portal is more user-friendly, complete with a dashboard and data catalogue to help people make analytical interpretations and make informed decisions. Most countries such as Denmark, Singapore, South Korea, Australia, the US, and the UK are still using portals similar to Malaysia’s public sector open data’s old portal version 1.0. Thus, it can be an opportunity for ASEAN Member States to learn from Malaysia’s public sector data-sharing.

Another best practice is change management. According to the experts, change management should be regarded as an important aspect, as without change management, employees and organisations or governmental agencies would not be aware of the latest data management practices. They would not be motivated to improve and update the knowledge and technologies in line with time. Change management increases employees’ willingness to learn and study new ways of doing things and helps in enhancing data management and analysis. Exploring and driving the public sector’s data-sharing to become the best practice also needs highly competent employees and strong adherence to the standard set, including ensuring data creation is bias-free. Thus, the best practice is to have a trusted central agency that monitors and improvises the data-sharing technology and services to ensure the smooth performance of data-sharing practices. Having MAMPU as the central agency that provides ICT services to the agencies, safeguarding their trust and confidentiality of the data shared, and acting to mediate data-sharing from various federal agencies in Malaysia can be considered as the best practice to follow.

There are a lot of potential opportunities for data-driven innovation. For example, MAMPU has conducted a competition programme called CHiPTA or Challenge on Innovation and Problem-Solving through Technology Advancement. CHiPTA is a combination of an Open Data Hackathon and a Mobile App Hackathon aimed at developing talent community creativity in developing innovative solutions and creativity by using the latest technology-based application development. From this CHiPTA, a variety of innovations were created, and a lot of potential innovation can be derived if more data-sharing were available. The only thing that restricted the innovation was the insufficient data-sharing. Regarding CHiPTA, the expert states that:

According to the expert, many organisations possess data, but do not share it openly. Public sector agencies, for instance, have access to a variety of data but are usually kept private. This leads to a lack of innovation. Data-sharing is essential for fostering innovation, as it encourages people to think about possible ways to improve the situation. In the context of the Fourth Industrial Revolution (4IR), which includes technologies such as robotics, artificial intelligence, machine learning, and big data analytics, data is crucial.

The expert also suggests that sharing data can accelerate innovation, particularly when it comes to confidential data that was previously restricted. MyGDX allows for more data-sharing, which in turn can drive data-based innovation, resulting in the creation of new business operations and applications. However, for data-driven innovation to thrive, it needs to be backed by leaders and legislation. Currently, many individuals and organisations are unwilling to share their data without legal protection. MAMPU has developed two (2) data portals that employ technologies to safeguard data security and improve data-sharing practices. MAMPU is also addressing the confidentiality issue, which has led to people refusing to share data, by developing the MyGDX portal. The MyGDX portal requires data classification from agencies and uses technologies such as API and SFTP to maintain data confidentiality, thus increasing data security and confidence among people and organisations to share their data. Opportunities for data-driven innovation are significant when people start sharing data. Data-sharing can drive innovation in various areas like agriculture, education, and business operations, aligned with the 4IR.

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