How to Ask AI the Right Questions - NextBrain AI

The initial and often most difficult phase of a predictive modeling project is transforming a business need into a predictive model.

Typically, this involves extensive discussions between business leaders and data professionals to develop a common understanding of the problem and establish a shared language for describing it.

Defining concepts such as “customer churn” can be intricate and involves making precise distinctions based on available data.

Crafting the Predictive Questions

NextBrain AI offers an efficient method to translate business needs into specific predictive questions that models can answer. Unlike the traditional lengthy conversations between data professionals and business stakeholders, the platform helps define the main challenge effectively by suggesting the right questions to ask your data. A well-formed predictive question is essential, acting as the guiding star for navigating existing data and reaching business objectives.

The Importance of a Good Predictive Question

An effective predictive question is specific, detailed, and focused on forecasting future outcomes using historical data. For instance, questions like “Which active customers are likely to churn in the next 30 days?” or “Which patient is most likely to become diabetic?” are forward-looking and based on historical data, providing insights that can inform proactive actions.

Building an Effective Predictive Question

Manual Approach

Define the Objective Clearly: Specify the future outcome of interest related to a strategic decision.
Granularity: Focus on a specific subject, such as a customer or a campaign.
Precision: Define subjects and outcomes precisely, e.g., “customers who joined our service in the last day.”
Time Horizon: Specify the prediction timeframe to prepare the data and choose the right modeling approach.

Using AI-based data analytics

With the predictive tools that NextBrain AI offers based on your business data AI will help you craft a state-of-the-art predictive question. This approach incorporates all necessary elements, translating them directly into the predictive model by defining the data used for training.

The Difference between AI and BI Questions

Temporal Orientation

BI Questions: Retrospective, analyzing past performance to understand the present.
AI Predictive Questions: Future-oriented, forecasting future outcomes based on past data patterns.

Actionability

BI Questions: Provide insights into past trends and performance metrics.
AI Predictive Questions: Offer forecasts that inform proactive strategies, such as targeted retention efforts or preventive health measures.

Complexity and Tools

BI Questions: Use traditional statistical analysis tools, focusing on a few key variables.
AI Predictive Questions: Require sophisticated analytical tools and machine learning models to handle multiple dimensions and identify predictive patterns.

Data Requirements

BI Analysis: Flexible with data’s historical scope and depth.
AI Predictive Analytics: Requires extensive, structured, and relevant historical data for accurate modeling.

NextBrain AI automates data preparation and feature selection, ensuring the necessary complexity and accuracy for predictive models.

BI QUESTIONS

Which factors influence customer churn?

Which symptoms indicate patients’ diabetes diagnoses?

AI PREDICTIVE QUESTIONS

Which of my customers will churn next month?

Which patient will get diabetes in the future?

Advancing Your Business with Predictive Questions

Predictive questions provide foresight, enabling organizations to anticipate changes, adapt strategies, and make informed decisions about the future. Unlike BI questions that analyze past events, predictive questions illuminate future possibilities.

NextBrain AI simplifies defining these questions, making the transition from BI to AI seamless and effective. Schedule your demo today to uncover the insights AI can reveal from your data.