Premium Products – Understanding Your Data

The Importance of Knowing Your Data

When assessing your data through the lens of the tools and products you’re using, it’s essential to understand that data first. This foundational knowledge allows you to get the most value from the tools in your stack and to use them correctly.

You need to know what each tool offers, what data it works with, and how you’re currently using it. This informed understanding helps ensure you’re taking full advantage of the premium features you’re paying for, particularly in products like ITSI or Enterprise Security (ES) whether that value is business insight or improved security coverage.

At Apto, we talk frequently about taking a “left-to-right” approach when it comes to data and tool usage. Too often, we see customers investing in premium tools without a solid grasp of either the tool’s capabilities or the dataset they intend to apply it to. The result? They ingest everything, get overwhelmed and are left unsure about how to use the tool or worse, why it’s even in their stack at all.

The smarter route is to take a step back. Understand your data, identify your goals and clarify the use cases for each tool. This makes setup, configuration and daily usage far more effective and intentional. Ensuring you receive the expected value from your investment.

Splunk ITSI: Mapping Data to Services

Let’s take Splunk’s premium product ITSI as an example. One of its key functions is to help you model your business data into what it defines as “services”, these represent crucial business functions that you’d want to monitor and be alerted about if they fail.

Take, for instance, a car park service. When modelling this service in ITSI, there are several key data points to consider to monitor it effectively.

Who + What?

Using the left-to-right approach, start by identifying who is interested in the service and what information they need:

• Customer Services: Are customers satisfied? Is the car park full? Will they be receiving complaints?
• Finance: Is the car park profitable? Are payment systems working? Are income targets being met?
• Operations: Are all parking spots functioning? Are there blockages or queues affecting flow?

By understanding these roles and what they care about, clear use cases begin to emerge. A customer service agent might use ITSI to track satisfaction metrics. A finance manager might monitor daily income. An operations team might look at spot availability and throughput issues. This ensures everyone interacting with the tool has a purpose and that the tool is populated with the data that matters to them.

When + Where?

Next, consider when and where the tool will be used. Are users proactively monitoring the data, or are they reacting to alerts?

Take Splunk’s Enterprise Security (ES) as another example. In this case, usage often depends on threat alerts. Are analysts using it reactively: responding to alerts and investigating threats? Or are they using it proactively: looking at trends, trying to predict incidents, or carrying out threat hunting?

Understanding when a tool is used and where it fits into daily workflows helps define what kind of data should be included, how it should be presented and what kind of functionality should be prioritised. Again, this all circles back to a deep understanding of your data.

How?

Finally, consider how users will interact with the data.

It’s easy to feed in every available data point, but if the data isn’t formatted correctly, or isn’t the right data, you can end up with clutter that provides no real insight.

Think again about the car park example. When modelling this as a service within ITSI, ask yourself:

• Is the data being shown on a central screen in a communal area, giving at-a-glance updates on throughput?
• Is it part of someone’s morning routine to review predicted revenue?
• Are alerts being sent to operational staff when occupancy hits a certain threshold?

By understanding how users engage with the data, you can tailor the system to deliver the right insights at the right time.

Conclusion

Ultimately, maximising the value of premium tools like ITSI and ES starts with understanding your data. Who is using it, what they’re trying to achieve, when and where they interact with the tools, and how the data should be presented are all crucial factors.

Taking the time to step back and map these considerations carefully can mean the difference between a tool that confuses and one that empowers.