Master of Social Sciences in Communication Thought Leadership Series

Big Data and decision-making: Balancing offence with defence

Master of Arts in Arts and Cultural Entrepreneurship Thought Leadership Series

Dr Kokil Jaidka, Assistant Professor | 3-min read

7 September 2021 — For many centuries, humans were dependent on biofuel as a means to survive. Mankind was consistently on the lookout for a more and more efficient energy source that could sustain our strife towards progress. But around the 1980s, humans invented the internet – a piece of technology that has arguably revolutionised the way we think about progress, fuel, and information.

The internet kindled human creativity in how information can be collected, stored, transferred, and manipulated as a veritable fuel for social and industrial progress. Big Data is the new oil – an inexhaustible source of information that flows ceaselessly, accelerates incessantly, and defies destruction. More data generally means more growth and better decisions by businesses and policymakers. However, the processes involved in generating decisions from information is not so straightforward. To succeed in a digital economy, it is increasingly necessary for individuals and businesses to adapt to the new paradigms in data management, analytics, and communication. There are rising concerns about the need to balance the `offence’ or the exploitation of big data with a strategy that also considers its `defence’ or its integrity, access, and privacy. Let’s explore the potential opportunities and challenges involved in the Big Data economy one by one.

1. How Big Data is collected and stored 
The Big data deluge has led to the need for special executives who focus primarily on managing torrents of data. An effective data management strategy should invoke rules that minimise risk, such as by developing rules for data privacy, standards for data sources, and transfer channels that ensure data integrity. However, two major challenges to effective Big Data storage is data theft and data leaks. Cross-industry studies suggest that 70% of employees have unauthorised access to data (Dallemule & Davenport, 2017).

2. How Big Data is analysed

A data value chain should ideally facilitate an in-depth analysis of revenues and customer insights, by integrating different data sources related to the research objectives of revenues, profitability, and customer satisfaction. However, Dallemule and Davenport (2017)1 report that although analysts spend 80% of their time simply preparing their data, less than 1% of the organisation’s data is actually used in making decisions. Why is there such a discrepancy? Data is often duplicated or fragmented, which renders it unsuitable for insights and decision-making. This largely happens when there is no overall strategy to coordinate the responsibilities of data stewards and data integration touchpoints.

3. How Big data is reported

Once analytical insights are obtained, they need to be reported in ways that support managerial decision making. Effective reporting is often done through visualisations and interactive dashboards, thereby supporting managerial decision-making. Telling stories with data is core to many businesses, from marketing to research analytics, and from business to sports. But a cluttered or confusing chart can lose people's interest and obscure the central story in the data. At this point, an effective communication strategy is needed to identify the right data, the right visualisation, and the right story to answer business questions. Keeping up with the digital economy, therefore, requires thought leaders who are well-versed with the opportunities and the challenges that come with big data.

The demand for innovative media professionals with technical skills in data analytics, user interface and user experience is expected to grow (SkillsFuture Singapore, 2018)2. Future leaders should consider both the opportunities and challenges in various industries and roles, such as product intelligence, digital marketing, customer relationship management, forecasting and strategic communications, to design and implement effective strategies for managing and communicating actionable insights from proprietary and public data.

References:


 

[1] DalleMule, L., & Davenport, T. H. (2017). What’s your data strategy. Harvard Business Review, 95(3), 112-121.

 
  • NUS SCALE
  • News
  • Big Data and decision-making: Balancing offence with defence
07 September 2021