“Core digitalization” has been a key theme in many industries from quite many years with an objective of “empowering business through Data-Driven success”. Hence, there is always a growing demand of connecting various kinds of data sources for Manufacturing, Quality, Supply Chain, Execution systems and so on. Typically, if we talk about equipment data in manufacturing space, the quantum and complexity is huge: Continuous time series of sensors data, triggered based reporting for deviations in Alarms and Events. Not necessarily, all these data points are collected through single connection methods; however, end state is contextualized in a collated format to enable and bring meaningful and operational related insights. 

There are many integration platforms to solve the digital maturity, but do we have data quality framework and reliability solution to measure the continuity and sustainability of these integrations? Answer is “NO” for us and the same applies to many organizations/industries as well.

This paper presents a detailed framework of building and operationalizing data quality for one of the critical data sources to collect manufacturing equipment data at DRL using the AVEVA PI System, focusing on data acquisition, validation, cleansing, and enrichment.