HKEJ Column

Professor Yulin Fang and Professor Xiaojie Zhang

5 March 2025

With the deep integration between big data and artificial intelligence (AI) today, data has become a core asset for corporate development. Continued technological breakthroughs in and the extensive application of AI have catapulted the value of data to unprecedented heights. The vast majority of companies are striving to derive valuable business insights from data analytics.

According to a study by Fortune Business Insights, the estimated market size of data analytics, valued at US$41.05 billion in 2022, is expected to reach US$279.31 billion by 2030, with an average annual growth rate of 27.3% between 2023 and 2030. This goes to show the rising importance of data analytics teams.

Persistent challenges in the age of data

By extracting key information from a sea of data, an outstanding data analytics team can provide a company with robust support for strategic decision-making, resulting in enhanced operational efficiency and a strengthened competitive edge in the market. Through in-depth analytics of sales data, enterprises can get a precise grasp of market demand and to optimize product planning. Through data mining of user behaviour, companies can achieve personalized marketing while boosting user satisfaction and loyalty.

However, data analytics-team management is beset with problems nowadays. Despite injecting substantial resources into big data, AI, and machine learning, many companies have not been able to gain sustained commercial value from their investments. A report by research institution Gartner reveals that more than half of Chief Marketing Officers are dissatisfied with their company’s marketing data analytics team.

The reason for the above phenomenon is that big data analytics involves a complex system. Its success relies not only on the collection, storage, and management of data assets and the use of appropriate analytics tools but also on an efficient information interaction model and a synergistic mechanism among team members. Given the multitude of interwoven factors, including data, analytics tools, team operations, and corporate environment, any single issue can result in the failure of the entire analytics project. It would be advisable for enterprises to have a viable management strategy in place to facilitate the effectiveness of their analytics team and maximize data value.

Pivotal agenda for building an analytics team

As a matter of fact, companies are not at a dead end amid impediments to big data analytics. The solution is to form an expert data analytics team to unlock the potential of big data and maximize business value. Seven vital points are outlined below.

1. Consolidating data foundation and safeguarding data quality (see Note 1). This involves ensuring diverse and reliable sources, as well as instantaneous data updating for the analytics team. Data analytics encompasses multiple aspects, such as transactions, user behaviour, and market movements, thereby laying a solid foundation for subsequent data mining. When devising a scientific data analytics process and a quality management system, it is essential to establish data quality monitoring indicators and regularly evaluate data quality. This system is conducive to maintaining data accuracy and consistency.
2. Profound operational alignments and precise identification of business requirements (see Note 1). The analytics team should be in close collaboration with operations departments and be fully aware of their needs in order to provide targeted data analytics and solutions. The team must be properly positioned within the company to enable it to partner closely with operations departments. Through data collection, collation, and analysis, the analytics team can support the day-to-day business operations and offer the company tailor-made data analytics services. This can be accomplished by acquiring an in-depth understanding of the operations departments’ workflows and needs.
3. Establishing a clear communication mechanism and refining the management structure (see Note 2). It is crucial to map out a scientifically sound team management structure, where a well-established communication framework is indispensable. A dedicated data management department should be set up to coordinate data collection, collation, and analytics, delineating the responsibilities of each department engaged in the data management process. Good communication among departments helps to prevent responsibility shirking and work process duplication, thus enhancing work efficiency. Only through effective communication can all departments perform at their best, enabling optimal resource allocation and ensuring the smooth implementation of data analytics projects.
4. Shaping transactive memories and strengthening coordination among team members (see Note 2). Since members of an analytics team each possess unique expertise and knowledge, it is necessary for them to thoroughly understand each other’s strengths to foster fruitful collaboration in the complex process of data analytics. In a comprehensive data analytics project, when the data collection members recognize the advantages of the data sources, the analytics members are well versed in various methods, and tasks are properly divided through a transactive memory system, the team will progressively upgrade its overall performance.
5. Stimulating creative integration and empowering corporate development (see Note 3). With abundant data sources, multiple analytics methods, and business knowledge, an analytics team will be equipped to implement creative integration. Take analytics on market conditions and competition, for example. Data on market dynamics and competitors alone will not suffice. This information should be creatively integrated with the company’s distinctive features and strategic goals to formulate a unique marketing strategy for its business.
6. Boosting knowledge management and facilitating collaborative creation (see Note 4). A high-impact analytics team is like a treasure trove of knowledge, with a wealth of professional expertise and data analytics skills accumulated from long-term practice. A sound knowledge management system is the key to this treasure trove, harnessing the team’s knowledge for efficient integration and innovative co-creation. Collating and archiving data analytics experience from a series of projects can develop an “intelligence toolkit”, paving the way for team members to draw inspiration and insights from it while continuously increasing professional capabilities. For new members, this undoubtedly serves as a “green channel” for integrating into the team, providing a shortcut to understanding the work mode and necessary knowledge, thereby empowering them to contribute to the team as quickly as possible.
7. Leveraging advanced technology and enhancing collaboration efficiency (see Note 3). An analytics team must be proficient in deploying various project management tools, such as Grantt charts, and the Kanban management system, to suitably chart project schedules, clarify duty allocations, monitor project progress, enhance the team’s collaboration efficiency and execution capabilities. This approach facilitates stable advancement and stimulates business development for the company. During the collaboration process, coordination techniques, e.g. real-time communication tools and online document-sharing platforms, should be used to break down communication barriers and achieve real-time sharing of information and synergy of efforts. With the geometric growth in generative AI technology, data analytics teams should strive to integrate it into their workflows, facilitating synergies between humans and generative AI to enhance efficiency and creativity.

Converging knowledge and action for a boundless future

Looking ahead, big data analytics technology is set to demonstrate more rapid growth and bring endless opportunities and possibilities for enterprises. In the face of this technological surge, promoting collaboration to refine the management coordination mechanisms of data analytics teams and build outstanding teams will enable all parties involved to fully tap into the value of big data. These concerted efforts will boost AI-driven efficiency, ushering in a new era of advancement.

Note 1: Zhang, X., Tian, F., Fang, Y., and Shen, H. “How to Promote Business Analytics Project Effectiveness: A Cross-disciplinary Bibliometric Analysis”. Industrial Management & Data Systems, under 1st round of revision.

Note 2: Fang, Y., Neufeld, D., and Zhang, X. 2022. “Knowledge coordination via digital artifacts in highly dispersed teams”. Information Systems Journal 32(3): pp. 520–43.

Note 3: Zhang, X., Fang, Y., Zhou, J. and Lim, KH. 2025. “How Collaboration Technology Use Affects IT Project Team Creativity: Integrating Team Knowledge and Creative Synthesis Perspectives”. MIS Quarterly, forthcoming.

Note 4: He, W., Hsieh, JJ., Schroeder, A., and Fang, Y. 2022. “Attaining Individual Creativity and Performance in Multi-Disciplinary and Geographically-Distributed IT Project Teams: The Role of Transactive Memory Systems”. MIS Quarterly 46(2): pp. 1035–72.