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Rick is a well experienced CTO who can offer cloud computing strategies and services to reduce IT operational costs and thus improve the efficiency. He guest blogs at Oracle, IBM, HP, SAP, SAGE, Huawei, Commvault, Equinix, Cloudtech. Lauren E Nelson – VP, Research Director at Forrester, Cloud Analyst, MIT Ultimate Coach.
Overfitting your data refers to creating a complicated datamodel that fits your limited set of data. On the other hand, underfitting your data refers to the missing parameter, which can provide a transparent and impartial outcome. For instance, e-commerce sales go spaced out during festivals and holidays.
Example: An online retailer moves its e-commerce application from an on-premises IBM WebSphere server using Java EE to AWS for better scalability and performance. The replatforming involves rehosting the application on AWS Elastic Beanstalk migrating the database from IBM DB2 to Amazon RDS for PostgreSQL.
Example Scenario: Data Aggregation Tools in Action This example demonstrates how data aggregation tools facilitate consolidating financial data from multiple sources into actionable financial insights. Loading: The transformed data is loaded into a central financial system.
Data visualizations are no longer driving revenue: Everyone from Google to Amazon now provides low-cost or no-cost visualization tools that drive down the perceived value of data visualizations. Users are coming to expect sophisticated analytics at little or no cost. Amazon Amazon is the leading e-commerce site.
For example, in an e-commerce application, predictive analytics can help anticipate spikes in traffic during specific events or seasons, allowing the team to scale server capacity accordingly. This prevents over-provisioning and under-provisioning of resources, resulting in cost savings and improved application performance.
They need to ensure that cost allocation rules and calculations are applied properly, that transfer pricing records are fully auditable, and that price-based forecasts can be adjusted over time as rates, rules, formulas, and data change. Getting it wrong can be costly.
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