What are the main challenges and best practices for scaling data backup and recovery?

1 Challenge 1: Data growth

Data backup and recovery can be a challenge due to the sheer amount of data that needs to be protected and restored. This data growth can be caused by business expansion, regulatory compliance, or customer demand, and can lead to a longer backup window and recovery time objective (RTO). Additionally, it can consume more storage space and network bandwidth, which can increase the costs and complexity of the backup and recovery infrastructure. Furthermore, it can increase the risk of data loss or corruption due to human error, hardware failure, or cyberattacks.

2 Best practice 1: Data reduction

Data backup and recovery can be scaled in the face of data growth by applying data reduction techniques, such as deduplication, compression, and archiving. These techniques can help you save storage space and network bandwidth, reduce the backup window and the RTO, and improve the performance and efficiency of backup and recovery processes. Deduplication eliminates redundant copies of data across different backup sets or locations, while compression reduces the size of data by removing unnecessary or redundant information. Archiving moves infrequently accessed or obsolete data to a lower-cost storage tier or a separate system.

3 Challenge 2: Data complexity

Data backup and recovery can be complicated by the increasing complexity of data, which can be caused by data diversity, data dependency, and data granularity. Data diversity refers to the variety of data types, formats, and sources that need to be backed up and recovered. Data dependency refers to the interrelationships and dependencies among different data sets or components that need to be preserved and restored. Data granularity refers to the level of detail or specificity that needs to be backed up and recovered. This complexity can lead to increased complexity in backup and recovery, more errors due to incompatible or inconsistent data formats, and more risks from data breaches, leaks, or theft due to inadequate or inconsistent data security, encryption, or access control.

4 Best practice 2: Data classification

Data classification is one of the best practices for scaling data backup and recovery in the face of data complexity. This involves data inventory, which is identifying and documenting all the data sources, types, and formats that need to be backed up and recovered, as well as their locations, owners, and dependencies. Additionally, data valuation assigns a value or priority to each data set or component, based on its importance, sensitivity, or frequency of use. Lastly, data policy defines and enforces the backup and recovery rules, standards, and procedures for each data set or component, based on its value or priority. Applying data classification techniques can help simplify and streamline the backup and recovery processes, reduce errors and risks, and optimize resources and costs.

5 Challenge 3: Data scalability

A third challenge for scaling data backup and recovery is the ability to adapt to the changing data needs and demands, which can be influenced by various factors such as data availability, performance, and reliability. This can pose several problems, including increasing the backup and recovery demands, challenges, and trade-offs. For example, more backup and recovery capacity, frequency, or speed may be needed to meet service level agreements or customer expectations. Additionally, dynamic or unpredictable data workloads, volumes, or patterns, such as peak periods, spikes, or bursts, must be dealt with. Finally, the backup and recovery costs, benefits, and risks must be balanced, such as the storage efficiency, the recovery time, or the data security.

6 Best practice 3: Data automation

Data automation is one of the best practices for scaling data backup and recovery in the face of data scalability. This includes data scheduling, which automates backup and recovery tasks based on predefined or dynamic triggers, such as time, event, or condition. Data orchestration automates the backup and recovery workflows across different data sources, types, or levels. Additionally, data monitoring automates the backup and recovery performance, quality, and compliance by collecting and analyzing data metrics, logs, or alerts. Data automation can help you scale your data backup and recovery processes, improving data availability, performance, and reliability, while reducing human intervention, errors, and risks.