Capability unlocked: Data interpretation and storytelling

Accounting and finance professionals now operate in an environment defined by accelerated technological change, rising scrutiny, regulatory complexity and shifting workforce expectations. 

Technical expertise remains essential, but it is no longer sufficient on its own.

This six-part series of articles shines a spotlight on the critical capabilities professionals must unlock to remain relevant, trusted and future-ready. 

Unlock data interpretation and storytelling

Data is one of the most valuable corporate assets in the digital economy, but it is almost meaningless without interpretation. 

As organisations become increasingly data-driven, the ability to interpret data and translate it into clear, compelling narratives is becoming a core capability for finance and accounting professionals.

Data interpretation describes the process of analysing data to understand what it reveals. This may include identifying patterns, relationships and insights from charts or raw datasets to help inform decisions. 

Data storytelling is the next important step — it describes the skill of communicating those insights and explaining what the data means to help bridge the gap between technical analysis and strategic action. 

Ghim Siong Ow, director of data science at RSM Singapore and a member of CPA Australia’s Singapore divisional Digital Committee, says valuable data insights start with a clear understanding of management’s requirements. 

“You need to know what they are trying to measure and which KPIs matter to them,” he says. “Once those KPIs are clear, it becomes much easier to determine whether the available data can support those objectives, and if the data does not exist yet, then you need to find a way to capture it.

“At that point, the challenge becomes translating raw data into management KPIs through a clear and logical process,” adds Ow. 

“Successful storytelling always starts with understanding management’s objectives.”

Why are data visualisation and storytelling important for the future?

Effective data interpretation requires an understanding of context — where the data comes from and what it is measuring. It also requires the critical thinking skills to question assumptions and avoid misleading conclusions. 

Recognising patterns or trends in the data is also important, as is the ability to communicate what these trends reveal.

Effective data storytelling can help connect financial outcomes to business activities by explaining what data reveals about customer behaviour or market conditions. This can help organisations make more informed strategic decisions that improve financial performance. 

This skill can also help cut through the complexity of environmental, social and governance reporting by highlighting progress in areas such as carbon reduction, workplace diversity or community impact. 

Data storytelling can also turn complex artificial intelligence (AI) outputs into clear, meaningful insights by connecting it to real business outcomes such as improving efficiency, identifying risks and predicting customer behaviour.

What do data interpretation and storytelling skills look like in practice?

Numbers are important to finance professionals, but explaining their impact to non-financial stakeholders is vital to effective data storytelling.

“Most people in the business do not actually care about the numbers themselves — they care about the impact,” says Kate Norris, founder and CEO of Blue Box Data Storytelling. 

“The strongest finance professionals understand the commercial reality behind the data. They know what each department is trying to achieve and can connect the numbers directly to those operational goals.”

Norris recalls presenting financial information, such as annual leave charts and budgets, to a legal team that politely listened but did not appear to engage with the numbers.

“Then at the end I said, ‘There is 25 per cent of the year left, but you only have 15 per cent of your budget remaining, so you will need to cut back if you want to fund upcoming initiatives’.

 

"Management does not want to see 100 insights. They care about the key KPIs. They want to know things like, ‘How much revenue are we generating compared to last year?’, ‘Where are the shortfalls?’ and ‘What needs attention right now?’"

 

“That was the moment they paid attention,” Norris adds. “I realised people do not respond to numbers alone — they respond when you clearly explain what those numbers mean for their reality, their priorities and their decisions.” 

Ow adds that it can be hard to identify risky or unusual activity just by looking at huge volumes of transaction data.

“Our role is to understand what risks auditors are actually looking for and then present the data in a way that allows them to quickly identify where they need to investigate further,” he says.
For example, Ow and his team might look for recurring transactions with unusually round numbers. 

“Once we know the risk indicators, we can build dashboards or charts that highlight those transactions immediately,” he says. 

“For every KPI or risk indicator we measure, there is usually a corresponding chart, table or visual insight, along with a narrative explaining why it matters and what action may need to follow.” 

What can happen if this skill is lacking?

Data interpretation and storytelling require objectivity. Norris says poor interpretation can result in misleading conclusions and weak decision-making.

“One of the biggest mistakes people make is trying to force data to fit a story they already want to tell,” she says. “We sometimes call that a ‘data cameo’ — the data makes an appearance, but it is not really driving the narrative.”

The key is to start with the data, adds Norris. “Explore it and let the insights emerge before you begin building the story around it.”

 

"Most people in the business do not actually care about the numbers themselves – they care about the impact. The strongest finance professionals understand the commercial reality behind the data. They know what each department is trying to achieve and can connect the numbers directly to those operational goals."

 

Ow adds that data quality plays a key role in effective interpretation. “You need to ensure the data is as clean and reliable as possible before you begin analysing it.”

Norris adds that bias can be difficult to avoid completely, especially because organisational politics and personal assumptions can influence interpretation. 

“Having someone else review your thinking can also help identify blind spots or unintended bias in the analysis.”

The importance of data interpretation and storytelling

Data analysts often explore information from many different angles, which can result in hundreds of insights, charts and findings. While this can ensure analysis is technically rigorous, it risks overwhelming your audience. 

“Management does not want to see 100 insights,” Ow says. “They care about the key KPIs. They want to know things like, ‘How much revenue are we generating compared to last year?’, ‘Where are the shortfalls?’ and ‘What needs attention right now?’ 

“So, the challenge is identifying the findings that are most relevant and impactful for management, then selecting the data points and charts that best support that story.” 

How can you build data interpretation and storytelling skills?

Building these skills begins with a clear understanding of management’s requirements. Ow speaks with his management team regularly, which allows him to develop an understanding of their priorities and pain points.

“Once you understand those focus areas, you can start preparing and consolidating the right data to support decision-making,” he says.

Ow recommends focusing on the most important question first and then using the data to answer it clearly.

“If revenue is down, for example, we ask why,” he says. 

“If the answer is that the economy is not doing well, then we ask why, and build a narrative around that. We keep asking ‘why’ until we get to the root cause. At RSM, we use the ‘five whys’ approach, which involves continuously questioning and using data to support the narrative at each stage.”

Norris adds that effective data storytelling requires delivering a clear message.

“At the end of a presentation, someone should be able to summarise what you said in a single sentence,” she says. “If you are not clear about the core takeaway, your audience won’t be either.”

Capability-building tip

“I believe the simplest charts often tell the clearest and most unbiased story. If you need to slice and dice the data across too many dimensions, it can become overly complicated and difficult to explain. In many cases, the best insights come from the simplest visualisations.”

Ghim Siong Ow, RSM Singapore

 

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