From source to analytics: A farm-to-table approach to data governance
Farm-to-table conjures up images of fresh produce delivered directly from the garden to the kitchen. A foundational part of this concept is knowing where the food is grown, where it’s traveled and if it’s safe to eat.
As it turns out, a similar approach can be applied to data governance. Farm-to-table is one way IT experts can frame how they’re governing data and evaluating the tools and technologies necessary for meeting compliance requirements and use cases like anti-money laundering or credit card fraud.
As data and IT professionals think about the “data lifecycle” – from origin of data to delivery in next generation analytics platforms – they must understand the importance of a strategic approach to data governance through security and data quality practices. This ensures data arrives fresh and clean for advanced analytics and can be easily tracked.
Perhaps the most important consideration for farm-to-table food is deciding if it’s safe to eat. So too is confirming the security of data for your organization.
The requirements for data availability and security are becoming more complex and broad thanks to the data explosion of the digital era. Organizations are forced to review their data availability and disaster recovery policies covering global data centers on-premise or in the cloud to plan for unexpected outages.
Companies must implement strategies and technologies to guarantee data is secure, available 24/7 and protected. For highly regulated industries such as banking or insurance, security is more important as data must satisfy specific, stringent regulatory compliance requirements. Yet for all industries, meeting privacy, confidentiality and compliance requirements on the growing sets of data is critical and challenging.
The General Data Protection Regulation (GDPR) and similar regulations require companies to implement best practices around data encryption, masking and anonymization to ensure their data lakes, data-as-a-service and data marketplace environments are as managed and secure as possible.
Securing data in the most optimized, scalable and efficient way possible while meeting SLA requirements and providing real-time insights is a key challenge. Organizations must prioritize selecting data products that are flexible and easy to integrate with security frameworks.
Just as consumers want to know if their food is fresh, so do organizations need to know their data is trusted and fit for purpose. As data flows in and out of the data lake, businesses must understand the sources of and relationships between data and set up business rules to continuously measure quality.
