Data Quality

CHAPTER 9Best Practices to Realize Data Quality

INTRODUCTION

Chapter 8 discussed the fact that the first six best data quality practices are related to data capture. This chapter will discuss the next four data quality best practices on data integration. Today, business data is rarely in one system in one format. It is often in many systems in varied formats. For example, if a telecom company needs a complete customer view, the data must be combined from many systems such as ERP, CRM, web traffic, marketing software, websites, IoT sensors data, and even data from agents and partners. If an oil company needs a complete vendor view, the data must be combined from many systems such as ERP, procurement, websites, credit rating data, bank account data, and even product catalog data from vendors. Data integration brings together data gathered from different systems into one common format that can be accessed from one unified source. From the analytics perspective, the pool of data integrated in the integration process is often collected in the common unified system called the data warehouse. But data can also be integrated from different transactional systems into one common transactional system to further improve operations and compliance processes.

The primary objective of data integration is to quickly and consistently achieve a complete view of enterprise-wide data. However, data integration is extremely time consuming and expensive and organizations report that poor data integrations ...