Three useful things to know about business analytics

Business analytics can help transform organisations, but only when technical know-how is combined with a supportive data-driven culture

Business analytics is an increasingly in-demand skill in Australia and globally. It is a set of skills and practices that utilise big data, statistical analysis, and data visualisation to help solve business problems, inform strategic and operational decisions, and create organisational change through digital transformation. In an increasingly data-driven world, it’s no surprise that the global big data analytics market is projected to increase by US$172.77 billion from 2020 to 2025, with small- and medium-sized businesses expected to lead this growth. 

But where does the customer fit into this process of data analysis-inspired digital transformation? In a recently published paper, Disciplined autonomy: How business analytics complements customer involvement for digital innovation, Dr Yunfei Shi, lecturer in the School of Information Systems & Technology Management at UNSW Business School, explores the role of the customer in the process of utilising business analytics for digital innovation.

For example, “digital innovation companies often face the dilemma of pursuing mass customisation and following a roadmap to deliver their digital products,” explains Dr Shi. “To help companies address this dilemma, our research examines how companies harness the power of big data using business analytics capabilities in conjunction with customer involvement capabilities to achieve a reasonable balance between the strategic vision and pursuing customisation,” she says.

Specifically, Dr Shi and her co-authors examine the technical skills and the cultural aspects of business analytic capabilities in augmenting a company’s customer involvement capabilities for generating and growing digital innovation.

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In a business analytics culture, leaders empower employees to make their own data-driven decisions to adapt to technological and customer requirement changes. Image: Shutterstock

A data-driven culture is vital to digital innovation

Dr Shi explains that technical business analytics capabilities, or business analytic skills, power customer involvement capabilities via a top-down process. Specifically, these skills integrate internal and external information to generate actionable insights and provide high-level guidance on the strategic road map that digital innovation can follow. For example, through predictive analytics techniques, companies can forecast the trend of technological advancement and customer preferences for innovation.

On the other hand, cultural business analytics capabilities, or simply business analytics culture, power customer involvement capabilities via a bottom-up process. “Specifically, data-driven culture empowers employees with the autonomy to make fact-based decisions to adapt to technological and customer requirement changes,” explains Dr Shi.

So instead of business analytics simply predicting what innovations customers might prefer, employees should be driving those decisions. “In addition, autonomous interactions are driven by the differences in employees’ and customers’ knowledge and capabilities rather than by formal management, which is beneficial for the emergence of innovative ideas,” says Dr Shi.

These mechanisms – a top-down and bottom-up process – ensure a “disciplined autonomy” for companies to engage with their customers to grow digital product innovation, and should be central to decision making, explains Dr Shi.

Read more: How Angry Birds developer uses data analytics to inspire creativity

The findings of her research paper reveal several things about how businesses should approach utilising business analytics. First, technical skills directly improve digital innovation’s market performance (e.g., sales, profit), whereas culture alone does not. 

However, culture demonstrates more substantial complementary effects than skills do when interacting with customer involvement capabilities (and these complementary effects exist when the value of a capability is augmented by interacting with another capability). This means that superior technical skills are necessary but insufficient for innovation. And when interacting with other capabilities, Dr Shi says business analytics culture displays more potent effects in augmenting organisations’ existing capabilities for innovation.

What can businesses do with this information? Given that business analytics culture has stronger effects on increasing organisations’ existing capabilities, fostering a data-driven culture is as vital as developing technical skills in business analytics. This is because the development of a business analytics culture challenges traditional decision-making processes, requiring top management to commit to fact-based decision-making and promote the benefits of leveraging big data to enable digital innovation. Doing so results in new opportunities for innovation.

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Organisations must use human judgement to complement business analytics insights for decision making, says UNSW Business School's Dr Yunfei Shi. Image: supplied

Leaders must support cross-functional teams

The findings also suggest that companies should encourage cross-functional collaboration between business analytics and customer involvement teams to enhance digital innovation. Analytics-based innovation is more likely to happen if business analytics and other functional employees frequently interact with each other because data-driven insights are helpful for the customer involvement team to make informative decisions, explains Dr Shi.

“To facilitate such interactions, leaders must establish and support cross-functional teams involving business analytics staff and customer engagement staff to engage with customers,” says Dr Shi, who adds companies need to develop an organisational mindset of balancing the flexibility of coping with changing needs in the marketplace and the stability of legitimising a vision for growing innovation.

“Leaders should empower front-line employees to make data-driven decisions and provide high-level principles for them to follow when needed,” she says.

Read more: How business analytics and ethical AI create competitive advantages

Effective use of business analytics relies on human decision-making

According to Dr Shi, business analytics present several business opportunities, including:

  • Enables organisations to access information at an unprecedented volume and pace, which helps generate valuable insights for making decisions effectively and efficiently.
  • Transforms many business processes by leveraging the value of big data, for example, customer engagement, supply chain management, human resource recruitment, etc.
  • Transforms traditional decision-making processes where top management’s intuition and experience dominate by empowering employees with autonomy and creativity.
  • Encourages a data-driven culture where organisational members should value and promote insights derived from data analytics.
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Leaders must commit to a business analytics culture that promotes fact-based decision-making and leveraging of big data. Image: Shutterstock

But there are also several challenges. For example, business analytics increases the pace and the intensity of the competition in the industry. As a result, using business analytics to power organisations’ existing capabilities becomes the source of competitive advantage. 

Additionally, businesses also need to be mindful of the ethical use of big data. Although data can be powerful in generating informative insights, companies need to ensure privacy, transparency, and equity issues when using business analytics to make decisions.

Despite its usefulness, Dr Shi warns that business analytics should not be the single source for business decisions because analytic insights focus on significant data patterns and lack contextualised consideration for an individual business case. 

“To mitigate decontextualised insights using business analytics, companies need to use human judgement to complement big-data insights for decision making. Furthermore, data-driven insights can be biased if the input data is not reliable or comprehensive to capture real-world problems," she says.

“For example, evidence has shown that analytical prediction leads to biased decisions across gender, race, and age in allocating healthcare staff and resources during the COVID-19 pandemic. Under these situations, organisations need to keep humans in the loop to ensure the fairness of decision making,” she concludes.

Dr Yunfei Shi is a Lecturer in the School of Information Systems & Technology Management at UNSW Business School. Her primary research area focuses on digital innovation and entrepreneurship. For more information, please contact Dr Shi directly.


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