Log data is big data! But that’s not why it’s such a big deal. Log data can be really useful if you know what to do with it — which is where log analysis and analytics comes in.
Let’s take a look at this valuable activity, starting with what log data can tell us and moving into how we can use analytics to inform business practices.
(This article was written in collaboration with Muhammad Raza.)
What log data can show
Log data is data that machines generate. It comes from a variety of sources including software applications, network nodes, components and data center servers, connected devices and sensors, consumer online activities and transactions.
Because of all the places that generate it, log data contains useful information around:
- The behavior of the system
- End-user activities and habits
- Machine performance and patterns that contain insights on potential incidents, events and outcomes in the future
Basically, log data can tell you a lot — if you know how to understand it. This potential value compels business organizations to aggregate, analyze and act upon log data for insights to better understand the technologies. After all, these technologies govern both the business operations and the end-user experience.
How log analytics works
We can consider log analytics one part of data analytics, but for many organizations, analyzing logs can be complicated. In order to make sense of the logs generated by a given technology system — aka log analytics — you need two important pieces of information:
- The workload of the computing interactions/activity
- The environment in which these interactions occur
To understand what happened during a particular computing activity, all the relevant entities involved in that activity need to be described, including systems, devices and users. Further, you’ll need an identifier, such as a specific TCP/IP protocol, to identify the workload or network request.
Once this information is captured at a large scale and in real-time (which is what log tools do), you can monitor the network and use this information to analyze logs and identify potentially anomalous, unusual behavior.
Let’s look at this another way…
When a user or software performs an action on the system, all the different parts of the system keep track of what's happening and what the system looks like at that moment. More precisely, each part of the technology system involved performs a specific sequence of steps. At each step, information is collected and recorded about:
- The current state of the system
- The computing request that was made
- The new state of the system after the request has been processed
In other words, every action that a user or software makes on a technology system generates a log of information about that action and its effects on the system. These logs are metadata, or data about data. This metadata, when looked at together, has information that includes things like:
- The time certain action(s) occurred
- What part of the system was involved (networking protocols)
- Any errors that might have occurred
If these log files contain all the relevant information — including information that is not predictable, like unexpected system behavior — then you can use modern log analytics tools to analyze the logs. For example, tools can be useful for understanding what went wrong in the past. They could even help predict what might happen in the future!
(Know the difference between logs & metrics.)
Challenges with log analytics
Of course, that’s the ideal situation for logs and the overall goal behind log management. But for many, that ideal is tampered by inherent challenges: Log data volume grows exponentially and rapidly. And it contains sensitive information regarding mission-critical technology systems and users.
The solution here, then, is operating a data platform that can guarantee two vital pieces:
- Efficient data pipeline processing for real-time log data streams.
- The ability to ingest, analyze and store at scale large volumes of structured, unstructured and semi-structured log data assets.
Use cases for log analytics
OK, OK. So, logs can be useful, got it. Now let’s look at some actual ways to use log analytics that can improve your business operations.
Real time monitoring & observability
It's common for IT operations and incident management teams to use log analytics to monitor and respond to patterns of anomalous behavior. These teams rely on data to make important decisions about things like:
- Workload distribution
- Network traffic controls
- Resource management
- Incident containment
To facilitate all this, log data is stored in centralized repositories, like data lakes and lakehouses, along with other relevant data sources. It’s then analyzed in real-time using third-party log analytics tools. Sometimes, the data needs to undergo an ETL process to align with the requirements of these tools.
This approach to data management allows for real-time monitoring and observability applications, helping teams respond to issues more quickly.
Who’s involved: The cybersecurity team
Outcome: Clear, holistic view of your security posture
Together with security information and event management (SIEM), cybersecurity log analytics use logging information to build a comprehensive view of the overall security posture of your systems:
- The log data typically contains information on user login details and system activities resulting from a computing interaction between machines and users.
- A SIEM tool may deploy additional endpoint agents to collect relevant information from points of interest within the network.
This information is fed directly to threat and vulnerability databases, as well as cybersecurity log analytics tools.
Who’s involved: E-commerce retailers, wholesalers and teams who support them (sales, web teams, marketing, etc.)
Outcome: Business intelligence
The e-commerce industry writ large uses log analytics to track and analyze how users interact with online services. These companies are interested in understanding the customer journey and purchasing process.
Clickstream analytics may collect data ranging from clicks and page views to device browser session data and cookies for targeted advertisement, product ranking, product pricing and UI/UX design changes. The goal of clickstream analytics is to optimize conversion rates, inventory and product sales.
The next step after log analytics
At the next stage of log management following these log analytics activities, engineering teams enter the reporting and feedback stages:
- At the reporting stage, the insights generated from log data, actions taken and the resulting business outcomes are evaluated by tech leads and business decision makers.
- The later feedback stage may involve activities to redefine, improve and optimize the metrics, data collection and analytics process.
As you can see, logs form the basis for many business operations.
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