Transforming data into action used to require business analytics. Now it only requires the bridge from ‘data exists’ to ‘data action.’ AI has compressed this gap, while simultaneously elevating the required level of analysis. This business analytics with AI shift has changed everything.
Practitioners and organizations trying to locate the boundary between analytics and AI will learn, to their dismay, that there is no such thing. The two functions merge into a single discipline, and the analysts, managers, and teams working within this merge are highly sought after.
Business Analytics Merges with AI
Traditional business analytics primarily provided structured reporting such as dashboards with historical performance and an explanation of performance. It answered ‘what happened’ and, with great effort, ‘why it happened.’
Business analytics with AI provides the following:
- Transition from description to prediction and recommendation: AI models provide summaries of performance and provide recommendations beyond performance descriptions and predictions.
- Reporting cycles with reporting that is continuous: Traditional analytics cycles revolve around reporting (weekly, monthly, quarterly). AI-based analytics can be deployed to provide reporting and analysis on a continuous basis, thus providing real-time detection of anomalies and opportunities.
- From Human-run Queries to AI-assisted Exploration: Natural-language interfaces and AI copilots allow analysts (as well as now non-analyst stakeholders) to query data conversationally. This shift has implications for both who can access insights and how quickly they can access them.
While this shift has implications for the speed of accessing insights, it does not eliminate the need to access insights in an analytical manner. In fact, it may increase the control an organization has to determine what insights to ingest and how to operationalize those insights and decisions. Hence, pursuing a business analytics with AI course can help professionals understand
A Strategic Framework for Combining Analytics and AI
There is a common structure, although not named as such, that organizations that realize value from the combination of business analytics and AI follow:
1. Model after the Decision
The most common failure is not due to a lack of technical skill; it is due to the starting point being “what can AI do with our data?” as opposed to “what decision are we trying to improve?” When structuring a framework for a situation or problem, it is important to start with the business problem and work backwards to how analytics and AI provide an answer to that problem.
2. Distinguish Description, Prediction, and Prescription
Not every problem requires a predictive model, and similarly not every prediction is required to have a prescribed action. Balancing the describe, predict and prescribe framework allows AI to be tackled where it provides the highest value.
3. Include human review at decision, not data, points
Analysis assisted by AI is extremely rapid, but there still needs to be a human review of the analysis, not the data, to ensure they understand the model behind the analysis, the confidence level, and its blind spots before they decide to take a recommendation.
4. Consider model/data drift as an ongoing analytics function
The patterns in traditional analytics frameworks and approaches were assumed to be pretty constant. With AI, we need to be monitoring potential model drift, at which point the patterns it learned earlier are no longer representative of the present, which means an element of analytics that didn’t exist before has to be maintained.
5. Optimize insight-to-action time
The primary advantage of business analytics with AI is shrinking the time between the generation of an insight and its transformation into action. Analytics teams need to be closely linked with the decision-makers to accomplish this, as opposed to being in a separate reporting function.
What This Means for Analysts and Aspiring AI Business Analysts
The creation of new AI business analyst positions is indicative of this change. The role is in the middle of applied AI/ML and traditional business analysis, and as such, bridges the gap with a mix of the following:
- The fundamentals of analytics and statistical reasoning, along with business domain knowledge, are foundational and will never become obsolete. AI’s prevalence actually increases their importance.
- Analysts need to understand how basic predictive and generative AI models work, so they can evaluate model outputs. This includes understanding AI model limitations to avoid being too trusting of model outputs.
- Analysts need to understand how to use workflow accelerator tools in a responsible way. This requires a level of comfort using AI that helps with data exploration, data querying in natural language, and automating reporting.
- Analysts need to understand how to bridge the gap between the information a model outputs and stakeholder needs. This challenge exists and becomes more important as AI outputs become more complex to interpret by non-technical stakeholders.
It is difficult to define a pure data scientist or a pure business analyst for these roles. This has made structured upskilling for this intersection of AI and analytics the new preferred career path, over treating it as an additional skill.
Deciding Between a Business Analytics and AI Course
Focusing on business frameworks rather than tool suitability is what separates a business analytics with AI course (or business analytics and AI course) from a pure AI course. A couple of things to consider when deciding on a course:
- Does it cover business tool suitability in the context of real-world problems? Teaching AI tools with no context to a real-world business problem produces analysts that are technically sound but struggle to understand the impact of their work.
- Does it cover the entire analytical process? Dashboarding and predictive modelling courses are useful; however, they lack context within the complete framework of descriptive, predictive and prescriptive analytics.
- Does the course involve working with real or simulated business data? The use of real-world messy data will most likely be better for preparing candidates for the actual business environment compared to the use of well-organised academic data.
- Does the course include working with model assessment and model limitations, and not just the model-building aspect? One important aspect of an analyst’s job would be to evaluate the recommendations that AI models create and make informed decisions about the recommendations.
The Summary
Business analytics with AI isn’t just a software upgrade. It fundamentally redefines the analyst position. Analysts must now be adept at articulating the right set of business questions, alongside the ability to work with predictive and prescriptive models.
In addition, the closer an analyst can get to the threshold of insight and execution, the more valuable they will be. With automated software insight generation, a significant emphasis in this new era of business analytics will be on the relationship between insight and execution. Those who develop these skills are the candidates most likely to get the AI-enabled business analyst job within the next few years.
For analysts and managers interested in building these skill sets through a structured business analytics and AI course, look at the Data Analytics and Business Analytics with Generative AI Program for a better understanding.
Also Read: The Role of Business Analytics in Sustainable Business Practices
