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6 Threats to the High Availability of Your Data (and How to Solve Them)

Achieving high availability for data is tough. The best-laid data infrastructures often go awry (sorry, Steinbeck fans) for a variety of reasons, and they bring data availability down with them.

What are those things that can go awry and prevent data high availability? And what can you do to stop or mitigate them?

Let’s take a look at some of the obvious and not-so-obvious high availability problems you may encounter with your data infrastructure.

1. Infrastructure Failure

Let’s begin with what is probably the most obvious source of disruption to high availability for your data: Infrastructure failure. When part of your infrastructure goes down, you are likely to experience a disruption in service, and fail to achieve high availability.

Infrastructure failure can take many forms. It could mean a failed disk or a network switch that has become overloaded. It could involve virtual servers that crash because of problems with the hypervisor that powers them. It could be a bad memory stick that brings down a host server.

In practice, it’s pretty hard to know ahead of time which parts of your infrastructure are at risk of failing. For that reason, the best safeguard against this risk is to build redundancy and backups into your data infrastructure, and enable them via automated failover. Automated failover means (as the term implies) that the backup system takes over automatically when the main system goes down.

Assessing the Financial Impact of Downtime, high availability

2. Infrastructure Overload

Another common problem for high availability for data is infrastructure overload.

It’s possible for the load placed on your infrastructure to become so great that the infrastructure can no longer handle it, and service is disrupted as a result. This could happen if, for example, you attempt to process a sudden influx of new data without having set up new infrastructure to handle it.

The best defense against this risk (beyond not deliberately overloading your infrastructure, of course) is to build scalability into your data infrastructure. This includes not just ensuring that you can set up new infrastructure quickly when you need it, but also thinking about scalability from a big-picture perspective: Will your IT team be able to scale up, too, when it has more data infrastructure to manage?

3. Malicious Activity

It may be obvious that there are bad people out there who want to disrupt your infrastructure’s high availability. Threats like DDoS attacks that originate on external networks can quickly bring your data and other services down.

But external attacks are not the only type of malicious threat. Your availability could also at risk from insider attacks carried out by, for example, a disgruntled employee.

There are some tools you can deploy, like anti-DDoS routers, to mitigate the risk of attacks. Since you can’t know exactly where an attack might originate, however, it is also important to have backups in place so that if data services are interrupted, or data is destroyed, you can restore it quickly from backups.

4. Data Inconsistency

Data that is not available in the format that you need to work with it, and cannot be transformed quickly enough to that format, poses a problem for high availability. Technically, data that exists in the wrong format may still be available, but unless you can transform it as required, it may as well not be available at all.

The best solution to this challenge is to ensure that you have flexible, automated data transformation tools at your disposal. With those tools, you can transform data quickly when you need to move it from one format (like a cloud-based block-level storage system) to another (like an on-premise file system).

5. Poor Data Quality

Data without data quality is not very useful. When your data sets are filled with inconsistencies, redundancies, inaccuracies or other issues, they prevent you from using data effectively, and undercut high availability.

Control this problem by leveraging data quality tools to clean up data sets, and building them into your data management processes. Data quality control shouldn’t be a one-off or periodic process; it should be part and parcel of the rest of your data management workflow.

6. Data Access Problems

Last but not least on our list of threats to high availability for data is data access. If the right users don’t have access to the right data, they won’t be able to do their jobs, and your data may as well not exist at all.

Ensuring that users who should have access to a given body of data while preventing unauthorized access requires careful planning, but it’s essential in order to achieve data security. Build access control into your data infrastructure from the beginning to help streamline your solution to this conundrum.

Make sure to also check out our white paper: Assessing the Financial Impact of Downtime

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