With the recent advances of big data and machine learning technologies, there has never been a better time for developing telecom data products. However there are various challenges associated with researching and developing telecom data products at scale. A good telecom data product can only be prototyped after proper data research, which includes many steps, such as data cleaning, data aggregation, data modeling, and data interpretation. All steps must be tightly coupled with domain knowledge and may be iterated for multiple rounds. After a data product is prototyped, it needs to be carefully engineered and developed and constantly reevaluated and retuned. This tight coupling of data knowledge, domain knowledge, and business knowledge and strong dependency to new operational and business data makes the development of good telecom data product extremely challenging, especially with the strict regulation of telecom data.