In today’s digital age, data is more important than ever. Businesses rely on data to make decisions, improve operations, and serve their customers. However, managing data can be a challenge. It can be spread across multiple systems and databases, and keeping it accurate and up-to-date, especially when it comes to “Master Data,” can be difficult.

According to a 2018 report from Gartner, organizations can spend an average of over $12 million every year due to poor data quality. This has likely gone up over the years. Apart from the cost of managing bad data, poor data quality can lead to suboptimal decisions, resulting in lost leads and missed market opportunities. However, measuring success (or failure) related to master data and defining appropriate Objectives and Key Results (OKRs) can be challenging. Most organizations look at the quality of data, which can be further broken down into consistency, completeness, reliability, accuracy, and freshness of the data. But even if we have a way to measure these aspects, how do we quantify the problem to define key results? In my opinion, preventing issues caused by bad master data will help keep things in check and ensure that we do not encounter problems down the line. The earlier we can catch these issues, the lower the cost. We can do this by monitoring lead indicators.

Introduction

Are you looking to accelerate your backend development process through code generation automation? In this blog post, we will explore how ChatGPT can help expedite your development workflow. We will focus on a specific scenario related to ecommerce and delve into the details of creating backend services using FastAPI and PostgreSQL. Additionally, we’ll cover the deployment of the database and APIs to local Docker containers.