Customer Data Platform with Data Governance
Customer data, which goes into a customer data platform (CDP) has PII information. Hence, different types of data have different rules for how it should be processed. In the US, an average data breach costs companies $7.9B annually[i]. In fact, it is crucial that a CDP has the highest data governance capabilities possible because a CDP operates in real-time, including providing personalized recommendations and integrating (via APIs), and sending the data to third party tools for more in-depth analytics and to inform customer engagements.
Now more than ever, with data protection and privacy regulations like the EU’s GDPR and the California Consumer Privacy Act, making third-party data dependent strategies is even more challenging, and we need robust data governance framework for the CDPs. Hence, allowing the data and tools to be connected by poorly maintained integrations “run wild” increases the likelihood of data leakage. Given the sensitivities associated with customer data, it’s critical to have a useful data governance framework.
Data Governance in CDP — A Business Case:
It is essential is define data lineage and usage policies to the customer while collecting data. Hence, a necessary feature of a CDP should be to classify, protect and control the data when it is being integrated.
This is achieved by performing cleansing, profiling, and segmentation operations on the incoming data. Infrastructure is usually created to perform these operations[ii], which comprises of the following steps:
1. Flag restricted data types by Data Labels: This is essential as this enables the organizations to stay compliant with all the data regulations and policies. There are many kinds of data that have different types of data protection laws applied to it. The infrastructure will automatically flag them into these various labels. Some examples of such data labels include:
a. Restricted Label: Data related to personal and identifiable information such as name, physical address, web address, etc.
b. Contract Label: Data related to other contractual obligations that restrict its usage
c. Sensitive Label: Data related to information that can be sensitive in nature
2. Configure usage restrictions for the labeled data: Once data has been flagged and labeled, conditions for the use of that data can be created so that the operations can run smoothly, consistently, and in real-time according to the conditions specified.
3. Simplify data policy management: Data governance rules and regulations frequently change. Hence the way each label is treated should be defined and should be capable of dynamically being changed.
4. Enforce data compliance: Once data is labeled, and usage policies are defined, each data is treated the way it is defined in terms of data usage and data share during integration with third-party tools. When a policy violation does occur, a notification is triggered.
Central Control of Data Governance in the CDP:
1. Access the data all at once: The data can be stored and used in multiple tools and analysis. Hence Data governance should be centrally managed, updated, and followed. Therefore, an excellent compliant CDP would be able to access, modify or delete user data from all the tools at once.
2. Automate using Consent Management feature: the entire data governance process by a Consent Management Platform, and this is better than changing the process manually every time. The platform would have live monitoring capabilities to troubleshoot bugs, granular filters and controls to determine what data gets routed where, and platform tools to expose data integrity issues and ensure that downstream systems are receiving clean and consistent data.
Way Forward:
A data governance solution in CDP is essential as the data protection laws and regulations make it imperative for organizations to protect all personal information. Further, a CDP should regularly be reviewing, refining, and improving its security practices. A good data governance policy ensures the customers trust the company’s brand and creates a sustainable and continuously improving customer database. Ultimately, to use customer data effectively, a good analytics infrastructure is required that is well connected to the third party tools to deliver personalized, multi-channel experiences to customers.
REFERENCES:
[i] https://digitalguardian.com/blog/whats-cost-data-breach-2019
[ii] The framework was inspired by this Article: https://www.cio.com/article/3571111/providing-data-governance-for-real-time-marketing-campaigns.html
