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.
Unraveling the Mystery of Data Lineage: The Story Behind Your Data
Data lineage is commonly defined as the family tree of data; it is the record of what was done to the data from the time it was generated up to the time of reporting. The origin issues refer to the need to understand how data exists in its raw form and how it has been processed and utilized, and these are important aspects for purposes of establishing the trust of the clients. However, data lineage is not fully utilized by many organizations, mainly because of the perceived difficulties that accompany it.
The Importance of Data Lineage:
Traceability: The identification of the sources of data is useful because it facilitates tracking of data in the identification of errors or anomalies for correction.
Accountability: Data lineage helps when data goes through several steps of processing because it maintains an accurate record of all changes.
Compliance: As for compliance-focused regulations such as GDPR and CCPA, data lineage responsibility becomes critical for demonstrating compliance and preserving data security.
Trust: Data lineage also brings about confidence to the stakeholders since the handling and processing of their data is put into full view.
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