Some Australian organisations, which either feature large data science teams or are born digital with a data-driven culture, have advanced analytics capabilities (such as undertaking predictive and prescriptive analytics). For example, dedicated data science teams in marketing will build neural network models to predict customer attrition and the success of cross-selling and up-selling. However, most organisations that use data in their decision-making primarily rely on descriptive analytics.
While descriptive analytics may seem simplistic compared to creating predictions and running optimisation algorithms, descriptive analytics offers firms tremendous value by providing an accurate and up-to-date view of the business. For most organisations, analytics – which may even be labelled as “advanced analytics” – takes the form of dashboards; and, for many organisational tasks, understanding trends and the current state of the business is sufficient to make evidence-based decisions.
Moreover, dashboards provide a foundation for creating a more data-driven culture and are the first step for many organisations in their analytics journey. That said, by strictly relying on dashboards, organisations are missing opportunities for leveraging predictive analytics to create competitive advantages.
Analytics gaps and challenges
Despite the importance of analytics, firms are at different stages of their analytics journey. Some firms utilise suites of complex artificial intelligent technologies, while many others still use Microsoft Excel as their main platform for data analysis. Unfortunately, the process of obtaining organisational value from analytics is far from trivial, and the organisational benefits provided by analytics are almost equalled by the challenges required for successful implementation.
My colleague Prof. Richard Vidgen recently undertook a Delphi study to reach a consensus on the importance of key challenges in creating value from big data and analytics. Managers overwhelmingly agreed that there were two significant issues. The first is the wealth of issues related to data: assuring data quality, timeliness and accuracy, linking data to key decisions, finding appropriate data to support decisions and issues pertaining to databases.
The second set of challenges pertains to people: building data skills in the organisation, upskilling current employees to utilise analytics, massive skill shortages across both analytics and the IT infrastructure supporting analytics, and building a corporate data culture (which includes integrating data into the organisation’s strategy). While issues related to data quality are improving, the skill gap and lack of emphasis on data-driven decision making are systemic issues that will require radical changes in Australian education and Australian corporate culture.
Business analytics trends
Although there are many interesting trends in terms of the advancements of analytics – like automated machine learning platforms (such as DataRobot and H2O), the greatest challenge with analytics and AI is going to be ensuring their ethical use.
Debate and governance around data usage are still in their infancy, and with time, analytics, black-box algorithms, and AI are going to come under increasing scrutiny. For example, Australia’s recent guidelines on ethical AI, where AI can be thought of as a predictive outcome created by an algorithm or model, include:
- Fairness. AI systems should be inclusive and should not involve unfair discrimination against individuals, communities or groups.
- Transparency and “explainability”. There should be transparency and responsible disclosure to ensure people know when they are significantly impacted by an AI system.
- Contestability. When an AI system significantly impacts a person, community, group or environment, there should be a timely process allowing people to challenge the output of the AI system.
- Accountability. Those responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled.
Achieving these goals with standard approaches to analytics is a challenging enough endeavour for organisations, due to the black-box nature of analytics, algorithms and AI. However, decisions driven by algorithms and analytics are now increasingly interacting with other organisations’ AI, which makes it even more difficult to predict the fairness and explainability of outcomes. For example, AI employed by e-commerce retailers to set prices can participate in collusion and driving up prices by mirroring and learning from competing AI’s behaviours without human interference, knowledge or explicit programming for collusion.
Addressing business analytics issues
As predictive analytics and AI will fundamentally transform almost all industries, it is critical that organisations adapt ethically. Organisations should implement frameworks to guide the use of AI and analytics, which explicitly incorporate fairness, transparency, explainability, contestability, and accountability.
A significant aspect of undertaking ethical AI and ethical analytics is optimising and selecting models and algorithms that incorporate ethical objectives. Typically, analytics professionals often select models based on their ability to make successful predictions on validation and hold-out data (that is, data that the model has never seen). However, rather than simply looking at prediction accuracy, analysts should incorporate issues related to transparency. For example, decision trees, which are a collection of “if-then” rules that connect branches of a tree, have simple structures and interpretations. They are highly visual, which enables analysts to easily convey the underlying logic and features that drive predictions to stakeholders.
Moreover, business analytics professionals can carefully scrutinise the nodes of the decision tree to determine if the criteria for the decision rules built into the model are ethical. Thus, rather than using advanced neural networks which often provide higher accuracy to models like decision trees but are effectively black-boxes, analysts should consider sacrificing slightly on performance in favour of transparency offered by simpler models.
Sam Kirshner is a Senior Lecturer in the School of Information Systems and Technology Management at UNSW Business School and is a member of the multidisciplinary Behavioural Insights for Business and Policy and Digital Enablement Research Networks.
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