Only data-driven companies can compete in the era of digitization. In the increasingly complex world of data, enterprises need reliable pillars. Reliable data is a critical factor.
As data is becoming a core part of every business operation the quality of the data that is gathered, stored and consumed during business processes will determine the success achieved in doing business today and tomorrow.
Data quality management is a set of practices that aim at maintaining a high quality of information. It goes all the way from the acquisition of data and the implementation of advanced data processes, to an effective distribution of data. It also requires a managerial oversight of the information you have. Effective DQM is recognized as essential to any consistent data analysis, as the quality of data is crucial to derive actionable and — more importantly — accurate insights from your information.
While the digital age has been successful in prompting innovation far and wide, it has also facilitated what is referred to as the “data crisis” of the digital age — low-quality data. But before starting anything:
Data quality can be defined in many different ways. In the most general sense, good data quality exists when data is suitable for the use case at hand.
This means that quality always depends on the context in which it is used, leading to the conclusion that there is no absolute valid quality benchmark.
Nonetheless, several definitions use the following rules for evaluating data quality:
· Completeness: are values missing?
· Validity: does the data match the rules?
· Uniqueness: is there duplicated data?
· Consistency: is the data consistent across various data stores?
· Timeliness: does the data represent reality from the required point in time?
· Accuracy: the degree to which the data represents reality.
· Orderliness: The data entered has the correct and required format.
· *Auditability: *Data is accessible and its possible to trace introduced changes.
Usually it is not hard to get everyone in a business, including the top-level management, to agree about that having good data quality is good for business. In the current era of digital transformation, the support for focusing on data quality is even better than it was before.
However, when it comes to the essential questions about who is responsible for data quality, who must do something about it and who will fund the necessary activities, then the going gets tough.
Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue.
Who starts good ends even better? I am pretty sure that on your data journey you came across some courses, videos, articles, maybe use cases where someone takes some data.
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This session on Top 10 Data Analytics Tools and Techniques will give you a brief understanding of top tools present in the market of data analysis.
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