Professor Marta Indulska and Dr Ida Asadi Someh predict how analytics and artificial intelligence could shape the future of business.
Data comes with big potential for businesses. There’s no denying it. But your analytics is only valuable if you have the right people, processes, and increasingly, the capabilities to work effectively with AI to evaluate, interpret, and act on insights.
Dr Ida Asadi Someh and Professor Marta Indulska are on the frontline of business analytics, ensuring the next generation of professionals know how to:
- formulate high-value analytics problems
- build high-quality and AI-ready data assets
- analyse data with the right approach and in a responsible manner.
- work effectively with AI systems to build systems that support decision making.
Because walking blind into the world of data, and now AI, means you’ll either misunderstand what you see, trust what you shouldn't, or fail to see what matters at all.
The age of algorithms
Data is now omnipresent. From fridges to watches, more items are getting connected to the web every day. We’re immersed in the Internet of Things, and increasingly in environments where AI is embedded into everyday products and services, tiptoeing towards the Internet of Everything.
“Social media use and increasing digitalisation of work and society continue to contribute to an ever-increasing volume of data,” says Marta.
But it’s not just the sheer amount of data that’s unlocked the door to modern business analytics.
That data has also become easier to access than ever before (in some cases, it’s available to buy). Computing has advanced to analysing big data in real time. And technological developments have given us affordable off-the-shelf tools that make analytics accessible to any business – no coding required.
More recently, these same advances have enabled a new generation of AI systems that are not only analysing data, but generating content, supporting decisions, and interacting directly with users through conversational interfaces.
While generative AI and machine learning were tough investments to justify for businesses before, the barriers to entry have now largely disappeared, leading to rapid experimentation and adoption across industries.
“You can think of AI including agentic AI as evolving along analytics-driven technologies into a core capability for how organisations operate and make decisions,” says Ida.
“Our daily experiences are increasingly determined by AI: the shows Netflix recommends, the music Spotify suggests, and what we see on our X and Facebook feeds, but also the way we search for information, create content and complete everyday work.”
- Ida Asadi Someh
“The traditional intelligent technologies relied on the human decision-makers to set the business rules, and the systems were tightly coded and developed to represent those rules. With AI, algorithms learn from data and can adapt the user experience on the go, increasingly in interaction with human users who guide, prompt and evaluate their outputs."
“These models are being applied to real business processes or to create products and services for customers.”
Marta agrees the future of business analytics is intrinsically tied to AI.
“We’re now seeing a shift in analytics maturity from predictive to prescriptive, and increasingly towards systems that can support or even execute decisions in collaboration with humans,” she says.
“We’ll continue to see an increase in data creation, and tools for data profiling and analytics will get more user-friendly and accessible. AI more broadly, will also become more user-friendly, accessible and embedded into data curation and analytics work.”
“Many companies are now actively exploring AI, but the challenge has shifted from interest to meaningful adoption and impact,” she says.
“The main reason for this is the novel challenges that AI represents, like the horror stories of bias and the lack of transparency about the inner workings, which affect uptake and trust.”
When analytics and AI go wrong
Responsible use of business analytics isn’t just crucial for making the most of your data. It also protects your company from potential problems or even ruin.
Marta warns against the following pitfalls businesses sometimes trip into with data.
- Repurposing old data for new uses – “Data quality is relative to the purpose it was originally collected for, so reusing data for originally unintended uses needs to be done with due care.”
- Blindly hoarding data – “Even if the data was of high quality at time of collection, it can become out of date if not properly managed. Businesses need appropriate data curation processes, and they need to focus on identifying data that’s relevant to their decision-making.”
- Garbage in, garbage out – “Organisations often overestimate the quality of their data and are eager to jump into deriving insights. A significant amount of effort and cost needs to go into data cleaning.”
- Overreliance on AI outputs – “AI systems can produce results that appear highly convincing but may be incomplete or incorrect. Human oversight remains critical to validate outputs, particularly in high-stakes decisions.”
- Using AI without appropriate governance – “As AI tools become more accessible, organisations risk deploying them without clear accountability, transparency or safeguards.”
Perhaps the most dangerous risk, though, is failing to keep customer data secure. When a customer’s details get exposed or hacked, the hit to their trust can be irreversible. And then you’ve got the legal and financial penalties to deal with.
“Businesses have a responsibility to appropriately manage and secure customer data. Any failures can result in reputational damage as well as substantial fines and regulator undertakings.”
- Marta Indulska
As AI expands upon the abilities of business analytics, it also elevates the risks for businesses who take ethical shortcuts.
For example, algorithms built with prejudicial rules can amplify or create racial biases (like the infamous case of Amazon’s hiring algorithm excluding people of colour). Some algorithm models are too probabilistic or not transparent in how they work, which ultimately leads to negative outcomes. And as automated services get better at matching our usual preferences, they sometimes cross the line into limiting our choices.
Ida believes these challenges are exacerbated by the fact most people have limited knowledge of how algorithms impact their lives. So, when slip-ups occur – whether they’re minor inconveniences or downright discrimination – they carry the extra fear of the unknown with them.
“Solving the ethical issues around using AI is complex, and the answer is going to be different from context to context,” says Ida.
The overall solution comes in two parts:
- technical education around analytics, machine learning and AI
- ethical education on how to build fair and transparent models that benefit organisations, individuals and society.
Finding a balance between technical know how and ethical know why is tricky. It’s a classic case of thinking about whether we should use the technology, not just whether we could.
This is why ethics underpins UQ’s Master of Business Analytics.
“The program teaches data management skills and best-practice approaches to analysing and visualising data,” says Marta.
“But there’s also a strong focus on ethical and responsible use of data as well as data-driven innovation and digital transformation of business.”
With this program's fresh lens on AI and analytics, Marta and Ida are hopeful for a future with business analytics professionals who are socially aware and can guide the development of ethical and explainable algorithms. They envision a world where all citizens:
- know what AI is and how it impacts them
- participate actively in its development and improvement
- have the option to opt out if they want to.
Only time will tell if this is the future we’re heading towards.
Discover UQ's Master of Business Analytics Explore all of UQ's postgraduate study options
