The DRHB Framework: How a viral meme led to a new lens on data driven business insights
By Sanjay Kala, Head of Consulting – SAS India
In the heart of the Indian hinterland, amidst the rustic charm of a small village, two characters from a popular Hindi OTT series exchange a few lines that have since resonated far beyond the original context “Dekh Raha Hai Binod” (translated as ‘Are you seeing this, Binod?’). This seemingly simple dialogue, shared over cups of steaming chai, has become a cultural touchstone—a viral meme representing everything from everyday observations to sharp social commentary.
The power of this phrase lies in its simplicity, distilling the essence of keen observation in a world that often overlooks the obvious. As “Dekh Raha Hai Binod” became a staple in digital communication, used to point out everything from the humorous to the profound, it also serves as an inspiration for a fresh approach to analytics—enter the DRHB (Dekh Raha Hai Binod) Framework. Just as the meme underscores the importance of recognizing what’s right in front of us, the DRHB framework is designed to bring critical insights to the forefront of business operations. It’s about transforming raw data into actionable intelligence, making the unseen visible, and driving business outcomes with clarity and precision.
The DRHB framework for analytics
D – Data collection and preparation: The first step in the journey is akin to gathering the elements of the story. Here, data is collected from various sources—databases, APIs, and third-party providers. This raw data is then cleaned and prepped, much like a storyteller refining the narrative, ensuring it’s ready to be transformed into valuable insights
R – Research and development: Next comes the phase where curiosity drives discovery. Through Exploratory Data Analysis (EDA), patterns and trends are unearthed, setting the stage for model selection. It’s like choosing the right lens through which to view a scene—picking the appropriate machine learning (ML) or AI models to reveal the hidden layers within the data.
H – Hypothesis testing and validation: This stage is about rigor—training and testing the models to ensure they perform as expected. It’s where the theory is put into practice, and every assumption is validated. Like the careful crafting of a plot twist, this phase ensures that the insights are not just interesting, but also accurate and reliable.
B – Business integration and deployment: Finally, the story is ready to be shared. The insights derived from the data are integrated into business processes, deployed where they can make a real impact. This is the moment when observation meets action, and the once-invisible patterns in the data begin to drive tangible business outcomes.
Use case example: Real-time transaction fraud detection
To illustrate the power of the DRHB framework, let’s consider a real-world application in banking—real-time transaction fraud detection.
Data collection and preparation: Imagine gathering transaction data, including timestamps, amounts, merchant details, and customer demographics. This data is then meticulously cleaned, prepped, and transformed to include features like transaction frequency, average spending, and geolocation.
Research and development: Exploratory analysis reveals patterns in fraudulent transactions, such as sudden large purchases or suspicious geographical shifts. Models like random forests and neural networks are trained, and features are engineered to detect anomalies in transaction behavior.
Hypothesis testing and validation: Models are rigorously tested using metrics like precision-recall and AUC-ROC, with a focus on minimising false negatives. They’re validated through cross-validation and real-time testing, ensuring they’re robust and reliable.
Business integration and deployment: The fraud detection model is then integrated into the bank’s transaction processing system, deployed on a cloud platform for scalability, and continuously monitored to ensure it adapts to evolving fraud tactics.
The DRHB framework, much like the viral meme that inspired it, serves as a reminder that often, the most profound insights come from simply paying attention. By structuring the process of analytics from data collection to business integration, the DRHB framework ensures that enterprises can not only observe but also act on the critical insights hidden in their data. Whether it’s detecting fraud or driving innovation, this approach transforms the ordinary into the extraordinary, just as the phrase “Dekh Raha Hai Binod” has done in the world of digital communication.
