Strategic data literacy is the antidote to the artificial intelligence (AI) hype. Big data is providing a paradigm shift in how we understand complexity, make decisions and anticipate change.
Dr Alex Antic, the recently appointed Faculty Head of AI Strategy at UNSW Canberra, first encountered big data while looking at techniques on how to better understand customers in the insurance industry.
“We were looking at how to leverage new techniques designed to work on big data effectively and better understand our customers,” he says. “Around the same time, Amazon, Netflix and other big companies were using predictive analytics and customer behaviour modelling to guide their strategic directions.
“So the change was there. It became clear that data was no longer just a by-product of operations. It was becoming a strategic asset. Big data isn’t just about data, it’s actually a paradigm shift in how we understand complexity, make decisions and anticipate change.”
Fast-forward 15 years and Antic says big data now proliferates — and is characterised by the “five Vs” — Volume, Velocity, Variety, Veracity and Value.
The five Vs of big data
- Volume — Processing massive amounts of data
- Velocity — Real-time data generation and processing
- Variety — Structured, semi-structured or unstructured data such as text, audio, video, processing Internet of Things data
- Veracity — Quality of data
- Value — Insights for decisions that can be derived from the data
“You’ve got real-time data about customers. There’s customer sentiment, media reviews from the marketing perspective, that’s paramount to making decisions. You’ve got geospatial mobility data from mobile devices and other sensors embedded all over the place.
“You’ve got data around cyber threat intelligence — global security feeds, supply chain information telemetry. There’s ESG and climate risk data from satellite imagery. Now you’re getting a lot of AI-generated insights from unstructured data from emails, documents and phone call transcripts.”
Harness big data
Antic says to harness the value of data that “listens, watches and learns, analyses social media and gives a panoramic view of risk and opportunity”, decision-makers must have a strategy that aligns the data to the broader goals of the business.
“It’s not something done in isolation. The data and analytics are there to support a business goal or problem you want to solve.”
He suggests businesses start with small use cases to pilot the use of data and alignment with board oversight and operational execution.
The founder and CEO of Sydney software company Pioneera, Danielle Owen Whitford MAICD, recalls how her own personal journey with burnout helped realise the potential of big data to identify solutions for her company by developing Coach Indie. This proprietary AI identifies signs of team stress, productivity shifts and workplace risks.
“I could see the warning signs, but my company didn’t,” she says. “The whole objective was, can we help someone in real time, to see they need support in real time? We obviously needed data to be able to do that objectively. I knew it was possible, but was delighted the first time I saw our customers escalate an issue up to the board with solid, reliable data points the team was able to use.
“As a result, that team got some resolution. They were able to get resources they needed and to do better planning and scheduling. It had a strong operational impact for that leader and their team.”
Make evidence-backed decisions
Lisa Claes, CEO of Cotality International (formerly Core Logic) — whose organisation digests billions of data points across the property life cycles of 14 million Australian and New Zealand properties — is also a strong advocate on using big data to augment intuition-driven decision-making with scenario-based, evidence-backed insights.
“At Cotality, we see big data and its responsible manipulation not just as an operational tool, but as a strategic asset,” she says. “One that helps boards anticipate change, sharpen competitive positioning and align long-term vision with market realities.
“Strategic data literacy is the antidote to AI hype. Boards that understand the provenance, bias and limitations of their data are far better equipped to evaluate where AI adds value — and where it introduces risk. In this sense, data governance isn’t just about compliance. It’s about ensuring AI is aligned with your strategy, not driving it blindly or being consumed in a tactical or piecemeal manner.”
Keep it real
In addition to alignment with strategy, data governance and accountability also encompass quality, privacy and ethics.
Antic says board directors must understand the basics of AI to ably test the veracity of the data. “Even though boards don’t need to be data scientists and AI professionals, they must be data-literate. The right questions will unlock the right insights.”
Claes says the full potential of data has not yet been released in the boardroom. “We’re still in the early innings of using data to truly elevate strategic thinking. As AI matures and integration improves, data will increasingly shift from reporting the past to helping boards simulate the future, test hypotheses and shape more adaptive, long-term strategies.”
Challenge assumptions
In order to maximise the potential of data in the boardroom, Claes points to building data fluency, embedding data into strategic and operational cycles, as well as appointing board-savvy non-executive directors or advisers who can challenge assumptions, spot patterns others might miss and advise on the procurement of data sets.
Owen Whitford believes data-driven decision-making by boards now extends the remit of board directors to have a “data conscience” — to make evidence-based decisions that benefit a wider range of stakeholders beyond shareholders.
“The AI we have now, not just in terms of genAI, but all the predictive AI and other tools, allows us to get a better prediction of the future and we can move towards more personalisation, more contextualisation. That again allows us to make stronger decisions.”
All three agree that boards which do not embrace big data for decision-making risk making ill-informed decisions.
“What generative AI has done is to speed up our requirement to digest information and make decisions quickly and exponentially,” says Owen Whitford.
“The technology is moving at such a fast rate that if you’re not making decisions quickly, you’re being left behind. In the rush to make decisions quickly, if you don’t have the right data to make those decisions, you’ll not just be left behind, you’ll go in the wrong direction.”
Practice resources — supporting good governance
AICD’s Policy team supports members with guidance on governance issues, including:
- Directors’ Guide to AI Governance
- AI Governance for Directors Webinar Series
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