A Data Product Lifecycle Framework for Transforming Enterprise Analytics into Monetizable Data-Driven Products
Keywords:
Data Products, Analytics Monetization, Lifecycle Framework, Machine Learning, Data Quality, Predictive AnalyticsAbstract
The greater emphasis on data-driven decision-making has prompted organizations to scale and commercialize internal analytics products. Nevertheless, a number of organizations do not have a drift toward utilizing analytical assets as a commercial solution in an organized approach. The study designs a Data Product Lifecycle Framework to combine data engineering, analytics development, product management, deployment, adoption measurement, and monetization plans. A quantitative research methodology is followed using a synthetically generated dataset that includes 5,000 records and 15 attributes that are connected with the performance of data products, customer interactions, data quality, and revenue performance. The analysis is performed using Python, including data preprocessing, exploratory data analysis, feature engineering, class balancing by using the SMOTE algorithm, and classification by machine learning. Accuracy, precision, recall, F1-score and ROC-AUC are used to compare Support Vector Machine (SVM), Gradient Boosting Machine (GBM) and K-Nearest Neighbor (KNN) models. Findings indicate that GBM offers the best predictive ability having 88% of accuracy and 0.931 ROC-AUC. The results indicate that data quality, customer engagement, and retention are crucial factors of successful analytics monetization.
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