Or, quite simply, make sure your log management system delivers real-time notification of known and unknown indicators of future outages.
But what assurance do you have that your log management system can not only make sense of the billions of logs of machine activity that it produces daily, but can analyze its “findings” and alert IT to impending issues in real time? That's the job of the system's analytics engine, which has the multitasking role of monitoring logs in real time, monitoring OS and application performance, detecting anomalies, and identifying root causes.
In retail, of course, it's critical to collect and manage data from custom marketing and sales applications. That's just good business. But the greater goal is to collect and analyze logs from all of the “stacks” in your IT systems. Those include industry-specific applications, storage servers, operating systems, ERP systems, web servers, and open-source applications – which all contribute to a composite picture of IT activities for both real-time and historical analysis. It's worth noting that point solutions – such as for scraping logs, creating scripts or developing applications – are not alternatives for real-time log management.
Logs help retailers improve their operational posture
A retailer's handling of machine logs also says a lot about its operational posture. A strong operational posture is built from an even stronger security posture – one built on the proposition that security breaches can endanger not just the retailer's consumers but can jeopardize the retailer's very existence.
Machine data is absolutely critical to strong security, not just as a warning of a breach or hack, but it also contains evidence and attack vectors of previous breaches and attempted attacks. In a retail organization, security is table stakes, and should therefore be a non-issue.
With security provisions established, the attention then goes to an operational posture built on knowing exactly what's going on with every piece of hardware and every application on the network, and even more – composite stats on network and server loads and other critical indicators.
Here are just a few such indicators: a failed hard drive, a load balancer failure, a software exception, an unreachable database, or a rise in network latency.
Cloud-based log management
Log management, like other enterprise applications, has gravitated to the cloud over time. Today, on-premise and cloud models are in use across enterprises, but two attributes of the cloud model can benefit retailers, and in two very different ways:
Cost avoidance: In a cloud model, capital expenditures are eliminated, and costs for retailers are geared to the seasonality of retail sales. Because the cloud eliminates capex for on-premise equipment and software, retailers can balance their expenses by opting for usage-based fees.