As a growing number of companies are looking to build out and leverage artificial intelligence solutions across their organization, they're often delayed due to poor data quality that exist across their business operations. This quality deficiency prevents them from proceeding with their intended AI rollout. Once AI is fully implemented, it can improve data quality throughout a company. Being faced with data quality issues forces a company to shift priorities and resources from implementing AI to fixing these quality shortcomings before they can proceed. This means extensive time delays, allocation of resources, and a slow draining of the AI budget.

In this special guest feature, Wilson Pang, CTO of Appen, offers a few quality controls that organizations can implement to allow for the most accurate and consistent data annotation process possible. Wilson joined Appen in November 2018 and is responsible for the company's products and technology. Wilson has over seventeen years' experience in software engineering and data science. Prior to joining Appen, Wilson was Chief Data Officer of CTrip in China, the second largest online travel agency company in the world where he led data engineers, analysts, data product managers and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering in eBay in California and provided leadership to various domains including data service and solutions, search science, marketing technology and billing systems. Wilson obtained his Masters and Bachelor's degrees of Electric Engineering from Zhejiang University in China.

In this special guest feature, Wilson Pang, CTO of Appen, offers a few quality controls that organizations can implement to allow for the most accurate and consistent data annotation process possible. Wilson joined Appen in November 2018 and is responsible for the company's products and technology. Wilson has over seventeen years' experience in software engineering and data science. Prior to joining Appen, Wilson was Chief Data Officer of CTrip in China, the second largest online travel agency company in the world where he led data engineers, analysts, data product managers and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering in eBay in California and provided leadership to various domains including data service and solutions, search science, marketing technology and billing systems. Wilson obtained his Masters and Bachelor's degrees of Electric Engineering from Zhejiang University in China.

The practice of Design Patterns is most popular in Object-Oriented Programming (OOP), which has been effectively explained and summarized in the classic book "Design Patterns: Elements of Reusable Object-Oriented Software" by Erich Gamma and Richard Helm. "A software design pattern is a general, reusable solution to a commonly occurring problem within a given context in software design. It is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations. Design patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system." For data science, many people may have asked the same question: does data science programming have design patterns?