IN TODAY’S increasingly complex business environment, data-driven decision-making has become a critical factor for maintaining a competitive edge.
By using data analytics to gain deeper insights, businesses can respond quickly to changes, anticipate challenges, and seize new opportunities. This ability to harness data for strategic decision-making not only drives better outcomes, but also helps organisations stay agile and ahead of their competitors.
Against this backdrop, companies are increasingly turning to advanced Big Data analytics, business intelligence (BI) tools, and predictive modelling to extract valuable insights into market trends, consumer behaviour, and operational efficiencies.
One of the most significant trends shaping data analytics today is the rise of artificial intelligence (AI) and predictive analytics. These tools enable businesses to progress from conventional, retrospective reporting to generating proactive, data-driven insights.
“The shift from traditional backward-looking reporting to forward-looking insights with predictive analytics powered by artificial intelligence and machine learning enables companies to forecast financial outcomes, drive business insights, and make better-informed decisions,” says Ng Yao Loong, chief financial officer at the Singapore Exchange (SGX).
Echoing this sentiment, Manik Bhandari, EY Asean AI and data leader, adds: “Business executives can leverage data to help them plan the future – this moves decision-making based on gut feel to being fact-based, hence improving the consistency and explainability of the decisions made.”
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Enhancing operational efficiency
AI is also helping to make data management processes more efficient by significantly reducing the manual processes traditionally involved in organising and analysing data. “Many organisations have enormous amounts of data, but being able to get the data to align and drive insights is difficult and often requires many levels of data cleansing and manual workarounds,” explains Joe Bisco, managing director, APAC head of strategy and business management at State Street.
At SGX, integrating data analytics has allowed the company to reduce manual interventions and streamline financial processes. “Aspiring to deliver real-time analytics means we have to redesign processes that reduce the need for manual intervention, multiple hand-offs, and data reconciliation,” says Ng. This shift enables the SGX finance team to respond more quickly and decisively in increasingly volatile markets.
Overcoming integration challenges
Despite the advantages of employing data analytics, companies still face challenges when integrating these tools into their existing operations. One of the main hurdles is ensuring data quality and seamless integration from multiple sources.
Ng notes that legacy systems and data silos can make it difficult to create a unified data repository. SGX moved from a legacy system to a cloud-based data warehouse to help with this effort, but the process of streamlining financial data processing is ongoing.
Bhandari suggests involving users early in the process and ensuring top-level support to significantly enhance the adoption of data analytics tools across the company. “Integration can happen smoothly from a technical perspective but if the end user does not know how to change or adopt the new ways of working, then the work is not delivering the value expected,” he says.
Importance of ethical data use
As businesses increasingly rely on data analytics and BI tools, ensuring ethical use of data has become an important consideration. Data protection is key to ensuring ethical decision-making to build trust with customers and stakeholders. To achieve this, companies will need to put in place robust encryption and resilient procedures to safeguard information and maintain data integrity.
And as AI tools continue to evolve, ethical considerations are likely to become more important. However, Bisco expects that AI adoption will follow a similar path as other rapidly integrated technologies – advancing quickly but with careful consideration and deliberate implementation.
There is also the danger of potential biases in data interpretation, which will require companies to ensure that their data-driven insights are mined in a fair and transparent manner.
Best practices
For companies just beginning to invest in data analytics and business intelligence, adopting the right strategies from the outset is critical for long-term success. One key recommendation is to start with a clear road map and a phased approach. Bhandari suggests that companies focus on identifying high-value areas that can provide immediate impact.
“Start small, with one or two use cases, show the value, and scale the investment. The use cases around working capital analytics are highly recommended as they free up cash for the business and can be a quick win,” he says. By focusing on early successes, companies can gradually build their capabilities and expand to more complex data initiatives.
Upskilling employees to more effectively use data analytics tools is another important step for companies to take when embarking on their data-driven journeys. Says Ng: “Training your team is essential – they need to be data-literate to interpret and use the insights effectively.”
Companies that embrace data analytics and BI tools will be well-positioned to capture new opportunities and navigate the challenges of a fast-paced market. By fostering a data-driven culture and integrating advanced technologies, businesses can stay ahead of the curve and drive sustained growth.
This series features some members of CPA Australia, which marks 70 years in Singapore, sharing strategic insights on business, finance and accounting
