Business Knowledge

Applied Frameworks for Business Analytics Success

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Business Analytics Data Science Decision Making Data Analysis

Summary

Business analytics is the practice of using data to drive strategic decisions. While the tools and algorithms can be complex, success in business analytics often comes down to following a structured process. Applied frameworks provide a roadmap for analytics projects, ensuring that they are well-defined, methodically executed, and focused on delivering business value. This guide explores popular frameworks like CRISP-DM and OSEMN that can help managers lead analytics initiatives effectively.

The Concept in Plain English

Imagine you want to build a house. You wouldn’t just show up with a pile of bricks and start stacking them. You’d follow a blueprint and a clear set of steps: lay the foundation, build the frame, add the roof, and so on. Business analytics frameworks are the blueprints for data projects. They provide a structured, repeatable process to guide you from a vague business question (e.g., “Why are our sales down?”) to a clear, data-driven answer and action plan. Following a framework prevents you from getting lost in the data and ensures that your analysis is actually tied to a meaningful business outcome.

Key Applied Frameworks for Business Analytics

  1. CRISP-DM (Cross-Industry Standard Process for Data Mining): This is one of the most popular and robust frameworks. It breaks down a data analytics project into six iterative phases.

    • Business Understanding: Define the problem you’re trying to solve.
    • Data Understanding: Explore the data you have available.
    • Data Preparation: Clean, format, and transform the data for analysis.
    • Modeling: Apply statistical techniques and algorithms to find patterns.
    • Evaluation: Check if your model actually addresses the business problem.
    • Deployment: Put the solution into practice.

  2. OSEMN (Obtain, Scrub, Explore, Model, iNterpret): Pronounced “awesome,” this framework is popular among data scientists and focuses on the hands-on steps of a data project.

    • Obtain: Gather the data from various sources.
    • Scrub: Clean and process the data (often the most time-consuming step).
    • Explore: Visualize the data to find initial insights and patterns (Exploratory Data Analysis).
    • Model: Build a statistical or machine learning model.
    • iNterpret: Interpret the results and communicate them as actionable business insights.

How to Apply It (Step-by-Step using CRISP-DM)

Let’s say a retail company wants to reduce customer churn.

  1. Business Understanding: The goal is to identify customers who are likely to stop shopping with the company and understand why. The success criterion is to develop a targeted intervention that reduces the churn rate by 10%.
  2. Data Understanding: The team gathers data from the CRM (customer purchase history, demographics) and website analytics (browsing behavior, login frequency). They identify data quality issues, such as missing values.
  3. Data Preparation: The team cleans the data, merges the different sources, and creates new variables (e.g., “days since last purchase,” “average purchase value”). This becomes the master dataset for modeling.
  4. Modeling: The analytics team builds a predictive model (e.g., a logistic regression or a decision tree) that assigns a “churn risk score” to each customer based on their attributes and behavior.
  5. Evaluation: The model is tested on a holdout dataset. It correctly identifies 80% of customers who actually churned. The team concludes the model is strong enough to be useful for the business.
  6. Deployment: The model is integrated into the company’s marketing automation system. Customers with a high churn risk score are automatically sent a special offer or a personalized email to encourage them to stay. The results of the campaign are monitored.

Risks and Limitations

  • Garbage In, Garbage Out: No framework can save a project that is based on poor-quality or irrelevant data. Data preparation and understanding are often the most critical phases.
  • Business Problem First, Data Second: A common mistake is to start with the data instead of a clear business problem. This leads to interesting findings that have no real business value. Always start with “Business Understanding.”
  • Frameworks are Guides, Not Straitjackets: These frameworks are iterative and flexible. You will often need to loop back to previous steps. Don’t treat them as a rigid, linear process.
  • The Last Mile Problem: The best model in the world is useless if it’s not deployed and its insights are not used to drive action. The “Deployment” phase is where value is ultimately created.
  • The Four Types of Business Analytics: Understanding whether your problem requires Descriptive, Diagnostic, Predictive, or Prescriptive analytics is part of the “Business Understanding” phase.
  • Data Governance: A strong data governance practice is essential for ensuring the data quality needed for any analytics framework to succeed.
  • A/B Testing: A common method used in the “Deployment” phase to evaluate the effectiveness of an analytics-driven intervention.