In today's data-driven world, organizations manage vast amounts of data. As enterprises expand and grow business functions, there's corresponding linear growth in operational data. This encompasses both master data and transactional data. While master data might change less frequently than transactional data, implementing data governance practices and defining data retention policy is crucial to maintaining data integrity and ensuring systems operate with accurate, necessary, and up-to-date data.
A data retention policy lets you define what and how far back data needs to stay in the transactional processing (OLTP) and outgoing data management systems. While this practice is not limited to OLTP and outbound data, the data retention policies differ from system to system and are not necessarily the same across all the enterprise functions. For instance, promotional data in retail may be needed as far back as three years to support data analytics and audit functions, whereas the order management system (OMS) may hold the orders history for just one year.
A well-defined data retention policy is crucial for ensuring compliance with legal and regulatory requirements, optimizing storage costs, and mitigating security risks. However, striking the right balance between retaining data for business needs and minimizing potential liabilities can be a complex task.
For the rest of this blog post, we will focus on data retention policies in OLTP and outbound data management systems.
Online Transaction Processing (OLTP) systems are the backbone of any enterprise, capturing transactional data in real time and dealing with data generated via day-to-day business operations. This data is critical for daily functions, but its value diminishes over time. Examples of transactional data include Basic Master Data (BMD), Extended Master Data (EMD), and pure forms of day-to-day operations like typical banking transactions of withdrawal (Debit) and deposits (Credit). The increasing volume of master data and transactional data can negatively impact system performance, resulting in slower transactions and degraded user response times.
While the application or data architect may implement measures to improve overall system performance, data retention is a key consideration for maintaining the health and stability of the transactional system. Lastly, it's essential to establish a retention policy that aligns with business needs and legal requirements.
Key considerations for OLTP data retention:
Outbound data refers to the data that is shared within the enterprise across different systems and with external parties, such as customers, partners, and vendors. This data can be in various forms, including emails, documents, reports, any incoming or outgoing data. Managing outbound data retention is challenging due to the lack of control over how the data is used and stored by external parties. However, organizations can still implement measures to mitigate risks and ensure compliance.
In the context of this article, we will discuss outbound data elements produced by the OLTP applications and what the retention policies are for outbound data publication.
Key considerations for outbound data retention:
By implementing a well-defined data retention policy and following best practices, organizations can effectively manage their OLTP and outbound data, ensuring compliance, optimizing costs, and mitigating risks.