NTT DATA Business Solutions
Robert Frey | June 14, 2023 | 4 Minutes

Why Data Archiving is Important for Today’s SAP Environment

Learn about the top 5 drivers for data archiving and what questions you need to consider in order to improve the performance of your SAP environment, optimize costs, and more.

Learn about the top 5 drivers for data archiving and what questions you need to consider

Why Data Archiving is Important for Today’s SAP Environment

For some, the idea or suggestion of data archiving can be unsettling. We are so used to having lots of information at our fingertips. It feels like a comfort to have several years of data that can be referenced whenever needed. Thus, when the subject of archiving production orders or sale orders arises, it is often met with resistance. We believe this information is not just a want, it’s a need. We may need that sales order or production order from 10 years ago.

However, in today’s SAP environment, there are several drivers that compel us to revisit the subject of data archiving:

  • Cost optimization: Archiving data can help reduce the cost associated with storing large volumes of unnecessary data. By archiving less frequently accessed data, we can reduce the storage requirements, leading to substantial cost savings. This enables us to allocate our IT budget more efficiently and invest in other critical areas of our business.
  • Improved system performance: The size of the database grows rapidly with each passing year, which can impact the performance of our systems, such as slower response times and increased downtime during maintenance activities. By alleviating the burden on our active systems, applications will run smoother and faster while enabling us to increase productivity and improve customer satisfaction. For example, reports and month-end tasks will not get bogged down and customer orders will get processed without unnecessary delays.
  • Regulatory compliance: By archiving data, we can implement more effective data governance practices. Archiving enables us to define clear data retention policies, identify data owners, and establish rules for data access and deletion. This promotes data quality, accuracy, and integrity while minimizing data redundancy and reducing the chances of unauthorized access.
  • Business intelligence and analytics: Archiving allows us to efficiently manage and access historical data so that we can perform trend analyses, identify patterns, as well as drive strategic planning, optimize supply chain operations, and capitalize on our competitive advantages.
  • Future system upgrades and migrations: Having a well-structured data archive greatly simplifies upgrades/service packs as well as migrations to SAP S/4HANA. By reducing the data volume that needs to be transferred or upgraded, we can accelerate overall project timelines while reducing the complexity and associated costs. In particular, the transition to SAP S/4HANA presents an opportunity to optimize data storage and improve system performance moving forward.

As we delve into the concept of archiving, it is essential to recognize that this is not solely an IT decision. It primarily requires the involvement of the business or data governance to drive the decision, while IT executes the plan. For example, here are some questions to consider:

  • What do we truly need all this data for?
  • How much is it costing us to keep this data?
  • What information is critical, as opposed to just data?
  • What is the difference between “long-term” reporting and “short-term” reporting?
  • What is our retention policy?
  • Do we need this information later or can it be deleted?
  • What information is required for reporting?

To clarify some definitions:

  • Data Archiving: Reducing the size of a database by selecting large volumes of data that are no longer required.
  • Archive: Refers to data that has been archived and can be retrieved at a later time for reference.
  • Deletion: Refers to data that is no longer required and can be permanently deleted.

Many companies have not established retention policies. A retention policy outlines the governance rules for data retention periods. After the data’s designated lifespan, should the information be retained in an archive, or can it simply be deleted? Retention periods are often measured in years. For example, finance data needs to be kept for 7 years to comply with GAAP record retention policies. After 7 years, what should happen to this information? Should it remain online, or should it be moved to an archive? What about non-financial information? Does it have a shorter life, such as 4 years? Additionally, information like system logs, EDI transactions, or temporary system movements may only be relevant for a few months before becoming data occupying valuable storage space. Reporting also plays a critical role in retention policies since some reports rely on specific information. Consider moving less critical information to a data warehouse for long-term reporting purposes.

Now that you have these ideas in hand, how do you get started? First, consider the drivers leading you towards data archiving. Set goals based on these drivers and determine what you want to accomplish with data archiving. Then, conduct an assessment of your system to identify areas with the highest data usage and storage. This assessment will highlight where the largest amount of data resides in the database.

With a good plan in place, you can start to recognize better system performance, cost reduction from reducing the size of database and cleaner information in the database.

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