In an era where data is heralded as the new oil, there’s an inconvenient truth that many organizations are just beginning to confront: it is therefore important to realize that not all data is equal. With the increasing digitalization of the economy and an imperative to increasingly rely on data in products and services, the focus has been traditionally on the sheer amount of data that can be gathered to feed analytics, provide clients with personalized experiences, and inform strategic actions. However, without this policy to embrace data quality and data lineage, even the strenuous data collection would result in disastrous results.
Let us take an example of a general merchandising retailer chain that, to sustain and overcome its competitors, started a large-scale acquisition-based customer loyalty campaign with help of their gigantic data warehouse. High expectations of the initiative and great investment to make it work reached a deadlock when the issue was revealed: the data behind the plan was unreliable. The promotions of the retailer were wrong since the wrong customers were being targeted, and this eroded the trust of the customers.
This is not an unusual case. In fact, all these issues will sound very familiar in most organizations, yet often with no realization regarding potential hidden costs in the form of poor data quality and a lack of understanding in terms of data lineage. If data is to become a true strategic resource, then organizations have got to go beyond what appears to be mere numbers and down traceability of data. Only then can they establish the much-needed trust in today’s world to answer the diversified needs of the customers and the regulating bodies.
The Hidden Truth About Data: It’s Only as Good as Its Quality
The question is: Who would not want to work with data? The truth is that data is full of errors, inconsistencies, and inaccuracies. Data quality is an issue that ultimately touches upon the decision-making process, organizational compliance, and customer trust. Let’s consider the following:
For instance, consider a marketing team working on creating a marketing campaign that was based on customer information that might have been entered incorrectly or not updated for several years. The result? Incorrect targeting, resource expenditure, and perhaps the antagonizing of clients. It therefore underlines the significance of sound data—a factor that is relevant both in making decisions and in customer relations.
Key Elements of Data Quality:
Accuracy: The data used should be accurate and depict the true worth and facts.
Completeness: All necessary data should be included without any gaps, i.e., all important data must be there with no breaks in between.
Consistency: Data should not only be uniform with all the systems and reports of the company, but also the format used should be uniform.
Timeliness: Data should be in real-time, and this data should be accessible whenever it is required.
Validity: The attributes should be of the right format and within the right range.
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