Data Quality Assurance In Enterprise Environments: Is Data the New Oil? (Part 1)
In 2017, an article in The Economist was published under the title: “The world’s most valuable resource is no longer oil, but data.”
The statement, like many similar ones, is quite provocative. It can be debated, as many have done (e.g., here and here). However, it is absolutely certain that one of the key factors for business success is the quality of a company’s data assets and the information base built on them—in other words, data quality or information quality.
As the aforementioned article highlights, information about a company’s operations, customers, and product portfolio is only valuable if it is accurate, comprehensive, reliable, and up-to-date. In other words, its quality must meet the informational needs of salespeople, analysts, and decision-makers. The demand for high-quality data underpinning business processes becomes more pronounced in various situations. Below, we outline scenarios where data quality is crucial and can significantly impact the profitability of enterprises.
Data Cleansing and CRM
The central element of an enterprise’s information architecture is typically the data warehouse. It aggregates and consolidates data from core business systems—transactional systems—and external sources to store them in a way that meets business needs. Decision-support tools, which automate reporting, enable quick ad-hoc analyses, and uncover hidden patterns in business data, are built on top of this data warehouse. The central data warehouse also serves as a cornerstone for enterprise-level CRM solutions.
Practical experience shows that the corporate data asset is often not of sufficient quality to build a data warehouse or implement a CRM system. Even if the data quality is adequate (or at least acceptable) for individual core business systems, this does not guarantee that it will suffice for the data warehouse and CRM system. These systems typically rely on multiple data sources, and the differing structures, content issues, and redundancies in these sources can jeopardize the usability of the data warehouse and CRM systems.
These problems are often only partially and qualitatively known to corporate experts. The real severity and potential consequences of these issues typically become evident in the later phases of data warehouse building and CRM implementation projects. By this point, the options for corrective actions are highly limited, and their costs are exponentially higher compared to addressing these issues in advance.
Centralized Customer Master Data (MDM)
A centralized customer master database is essential as it consolidates customer identification data from all related systems, including their specific identifiers within those systems. Before setting up a customer master database, a comprehensive data cleansing process is required to identify and merge duplicates within and across systems. Detected duplicates are assigned a master identifier, and the source system data is integrated into the centralized customer master database along with it. This allows users to identify which records in the individual systems correspond to the same customer.
Data Migration
With the rapid pace of IT development, enterprises and organizations often aim to replace existing product or data management systems with more modern solutions. Implementing new systems inevitably involves migrating data from legacy systems. During this migration, similar data quality issues arise as those encountered during CRM or data warehouse implementation. To ensure successful migration, the data must be prepared and any quality issues, such as data content errors, duplicates, and redundancies, must be addressed. If insufficient attention is paid to improving data quality, the effectiveness of the newly implemented system can be significantly reduced, and business areas may feel that the application does not meet their expectations.
In our next post, we will continue to list areas where data quality management can make a big contribution to corporate profitability. Until then, please find a brief description of our data quality assurance solutions here.
Perhaps your company has already considered how to improve the quality of its data assets? Why not talk about this over a good cup of coffee?
