Given the abundance of data today, organisations are in a good position to make more informed decisions to enhance their operations and drive business growth. According to research firm International Data Corp, they are also aware of the need to be insight-driven as spending on big data and analytics in Asia Pacific (Apac) is expected to reach US$42.2 billion ($57 billion) this year.
Yet, most leaders and employees still rely on their gut instincts for decision-making. Low data literacy and the lack of a data culture are reasons for this.
“Data literacy is reading, analysing and working with data. Qlik’s Data Literacy Report found that only 11% of global employees are fully confident in their data literacy skills. Without data skills, employees often instinctively fall back on their gut feeling when making everyday decisions,” says CK Tan, senior director for solutions and value engineering at Qlik, a data integration, quality and analytics solutions provider.
He continues: “Another reason employees aren’t truly data-driven is the lack of a data culture within their organisation, often because leaders do not lead by example. Our report shows that 45% of C-suite executives frequently make decisions based on gut feeling rather than using data-led insights, and 42% don’t always trust the accuracy of the data available. Without top executives trusting and providing access to high-quality data, employees will not be motivated and empowered to make informed decisions.”
Findings from Qlik's Data Literacy report
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Business leaders facing decision distress
Although relying on gut instincts may work, it could lead to decision distress. This is when the decision-maker regrets, feels guilty, or questions a decision they previously made.
Oracle’s The Decision Dilemma study reveals that 85% of business leaders in Singapore experienced this, and they believe having the right data will enable them to make better and faster decisions, reduce risk and plan for the unexpected.
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“Business leaders are not just hungry for data but the right data. And they believe that having the right decision intelligence can make or break the success of their organisation,” says Chris Chelliah, senior vice president of technology and customer strategy for JAPAC at technology giant Oracle.
However, business leaders in Singapore are inundated with data overload. Nearly two-thirds admit the sheer volume of data and their lack of trust in data has stopped them from making any decision.
The majority (87%) also believe the growing number of data sources has limited the success of their organisations. Seventy-six per cent of business leaders would rather have a robot make their decisions, indicating the decision-making fatigue and struggle they face to become data-driven.
“Business leaders feel overwhelmed and under-qualified to understand the abundance of information. The challenge lies in distinguishing between the signal and the noise in the overwhelming data available. Leaders who are overwhelmed are tempted to throw out consuming (and sometimes conflicting) data and do what feels right. But gut feelings can often be affected by external factors such as emotions,” explains Chelliah.
He adds: “The best decisions are made with a proper understanding of the relevant data. As humans, what we do best is learn, adapt and improve. Cloud, machine learning and artificial intelligence (AI) are all excellent enablers to help organisations collect and analyse data. The increased spending for big data and analytics shows that business leaders recognise the importance of understanding data and making it work for them instead of vice versa. Getting a handle on the data stream at their fingertips is a crucial first step.”
Some findings from Oracle's The Decision Dilemma report
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Analysis paralysis due to siloed data
Be it descriptive, diagnostic, predictive or prescriptive, many types of data analytics solutions are available today. However, business leaders do not believe the current approach to data and analytics addresses the challenges of decision distress and analysis paralysis, which is the inability to make decisions due to overthinking.
More than two-thirds (68%) of the Singapore business leaders in the Oracle survey say the dashboards and charts they get only sometimes relate directly to the decisions they need to make. They also believe most data available is only truly helpful for IT professionals or data scientists.
Siloed data sources are one of the hurdles preventing organisations from becoming data-driven. “Analysing data in different formats on multiple platforms slows the discovery of valuable business insights. A time-consuming data analytics approach forces organisations to rely on historical reports instead of deriving in-the-moment, real-time insights,” adds Tan.
Agreeing with him, Chelliah says: “Disparate and siloed data strategies prevent the collation and analysis of data from various systems. This makes it difficult for businesses to generate actionable, data-driven insights that support more strategic decisions.” He also highlights that outdated systems, inadequate data management practices and human error can hamper data quality, leading to incorrect analysis and wasting time and resources.
Taming the data deluge with technology
As data is set to grow in our increasingly digital world, how can organisations ensure that they are empowered (instead of overwhelmed) by data? One way is by deploying technologies to help them manage the data deluge.
There is no such thing as too much data. The amount of historical data available impacts the accuracy of the predictions, so the more data, the better. Organisations must therefore invest in technology that can handle data volume and generate accurate predictions based on historical data.
Chris Chelliah, senior vice president of technology and customer strategy for JAPAC, Oracle
He gave the example of Singapore Pools, which improved its operations and provided a more stable and secure betting experience by migrating its on-premises applications monitoring system to Oracle Cloud Infrastructure (OCI).
Using the Oracle Cloud Observability and Management Platform, Singapore Pools improved visibility and actionable insights to ease management across all layers of its technology stack. The platform minimises risk, ensures proper and thorough data governance, and reduces management complexity, allowing Singapore Pools to resolve issues in minutes compared to previous hours.
Leveraging AI and machine learning
Tan recommends implementing solutions leveraging machine learning or AI to automate data analytics so leaders and employees can make better decisions more efficiently.
For instance, embedding generative AI content directly in the analytics experience enables organisations to augment analytics with natural language insights, synthesise and combine third-party data into existing data models, and ask questions in real time.
Still, organisations need to put up guardrails when using AI. “While generative AI is a promising new technology that has people excited about possible new waves of creativity and ways of working, there is a possibility of biases sneaking into AI model results. However, with thorough checks, limitations and regulations set in place, generative AI can positively impact all industries and sectors,” says Tan.
He continues: “Developing the right questions is essential to using these tools effectively, as the quality of the responses is directly tied to the quality of the questions asked. Data-informed decision-making — converting information into actionable and verified knowledge to make better decisions — is important, and data integration and analytics can help support it. By analysing data from various sources across the business, teams can better understand their company, customers, and industry, focusing on the most critical issues to get the best possible insights and recommendations from generative AI tools.”
Sharing the same view, Chelliah advises organisations to have compliance teams in place to set clear guidelines for aligning AI and machine learning models. The deployment process should be fully transparent and auditable to reduce any biases introduced into the AI models at any stage.
“We are now in a phase where the barriers between human and machine capabilities are dissolving and becoming more intertwined. Human experts are still very much required to guide and evaluate the decisions made by AI systems. Humans are not getting side-lined in these developments but using the best tools to process large amounts of data while injecting human intelligence,” he says.
Building data skills and data culture
Technology is always said to be only as good (or bad) as its user. Tan believes organisations must ensure their employees have adequate data literacy skills and build a data culture.
Becoming data-first starts with adopting an active data culture that embraces the best of humans and machines for better decision-making. From a people perspective, leaders should promote the benefits of data literacy.
CK Tan, senior director for solutions and value engineering, Qlik
He adds: "Upskilling employees in data literacy — such as pairing analytics experts with business teams — promotes a culture of shared learning where groups can build on one another’s expertise to do more with data."
The Government Technology Agency of Singapore (GovTech) is one organisation that has recognised the importance of creating a data-driven culture and turned to Qlik to help train its public officers in data science.
By giving officers access to learning resources, industry best practices, and the latest data tools, Qlik helped GovTech improve its data literacy levels and built data discovery and visual analytics capabilities within the public sector to meet citizen demands in a growing digital economy in line with Singapore’s Smart Nation initiative.
“As data rapidly grows in volume and complexity, companies should constantly evaluate their data tools and make data literacy skills a prerequisite for employee learning and development. Incorporating data literacy skills into employee performance reviews can ensure that employees constantly maximise value from their organisation’s data,” says Tan.
