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data source assessment

Assessing What Matters with a DI Data Source Assessment Workshop

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As organizations rush to deploy next-generation technologies, they are hitting a roadblock: their own infrastructure. In a recent TDM Survey, 61% of participating data management professionals listed data quality as a top challenge, and 57% indicated data integration issues. With the rapid adoption of AI, we need to ensure metadata is secure and managed. Gartner predicts that through 2026, organizations will abandon 60% of AI projects due to insufficient data quality.

This is why you’ve invested in a data pipeline platform to gain control over your data, control costs, improve data quality, and route logs to the right destinations. When you consider a massive Splunk environment, you may wonder where to begin. There may be thousands of source types, miles of legacy configurations, and no clear idea of what’s actually providing value to users. We’ve seen organizations staring at a mountain of data, paralyzed by the fear of breaking a critical dashboard or wasting license bits on “dark data” that no one has searched in years.

We recently discovered in a large Financial Institution over 1600 data sources hadn’t been searched a single time in the last 30 days!

Imagine the savings and performance gains from simply not processing data that no one is looking at.

We’ve realized that the most successful deployments don’t start with technical plumbing – they start with an educated conversation to cover the bases. This has led us to the development of the Data Source Assessment Workshop.

Value-Driven Approach

Think of the Workshop as a tactical reconnaissance mission for your data. It’s a focused, one-day session designed to identify exactly how your data pipeline should be built and implemented. You need to assess what makes the most sense for your data architecture, your performance, and your business goals.

The main use case is prioritizing sources for pipeline onboarding, but the benefits go much deeper. We use this time to formulate a structured path for the rest of your integration, ensuring you aren’t just “moving the mess” but actually optimizing it. We want to capture every opportunity to control volumes and streamline how data hits your environment.

Peering Into the “Dark Data”

You can’t manage what you can’t see. To facilitate this workshop, we use our specialized Data Assessment App. This isn’t just another series of dashboards; it’s a highly specialized approach that taps into your logs and metrics to give us a granular view of your data’s lifecycle.

Finding the “Quick Wins”

During the workshop, we don’t just look for the biggest data source and call it a day. While high volume is a factor, we’re also looking for the best time-to-value.

The “perfect” first data source to move through your data pipeline is one that has high volume (to show immediate impact) but low dependency on downstream knowledge objects. Say if a source is only used by two users and one dashboard, it’s much easier to validate after the move than a source used by 500 users and 50 apps.

By finding this balance, we create a prioritized list that allows us to build a parallel pipeline, validate it quickly, and show ROI to your stakeholders without disturbing the production environment.

Workshop Outcomes

We don’t want you to walk away with just a fancy list. The outcome of a Data Source Assessment Workshop is a comprehensive plan of attack. We gather the contextual “tribal knowledge” that isn’t always documented.

  • Validation Plan: We establish what “success” looks like and the stakeholders involved in supporting and validating the data.
  • Routing and Connectivity: We identify exactly how data comes in and what firewall rules, ports, or load balancers are in the path.
  • Configuration Parity: We review and collect relevant configurations so we can replicate and improve them within your data pipeline.
  • Reduction Strategy: We decide how we’re going to trim the fat. Are we dropping whole events? Are we dropping specific fields? Can we aggregate events?

Conclusion

At the end of the day, a successful data pipeline implementation isn’t just about the technology, it’s about the data. By taking the time to perform a Data Source Assessment, you’re moving from a “guess-and-check” method to an educated, data-driven strategy. You’ll identify unused data, find your highest-value sources, and create a clear roadmap that minimizes risk and maximizes your investment. It’s about knowing what you didn’t know and turning that insight into action.

Ready to stop wandering in the dark and start optimizing? Let’s get your data assessed and your onboarding streamlined.

Discovered Intelligence Inc., 2026. Unauthorized use and/or duplication of this material without express and written permission from this site’s owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Discovered Intelligence, with appropriate and specific direction (i.e. a linked URL) to this original content.

ansible for splunk

Ditch the Deployment Server: Why We Used Ansible for Splunk in a Secure OT Environment

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Have you ever tried to manage a net-new Splunk deployment across dozens of isolated gas plants while staring down an aggressive six-week deadline?

We recently partnered with a major gas extraction company to do exactly that. In their highly secure Industrial Control Systems (ICS) and Operational Technology (OT) environments, you can’t just “hope” your configurations stick; you need a process that is repeatable, version-controlled, and bulletproof.

When the network is locked down tighter than a bank vault, standard Splunk config-management doesn’t just not work — it becomes a security risk. Here is why we moved away from a traditional Splunk Deployment Server setup and leaned into Ansible to get the job done.

OT Challenge: Navigating the Purdue Model

Managing data in a standard IT environment is (mostly) straightforward. But our customers’ environment follows the Purdue Model—a network architecture of increasingly secured rings designed to protect critical assets like pumps, manufacturing tools, and sensors.

While the Purdue Model is great for security, it’s a bit of a nightmare for traditional Splunk management. Level 1 and 2 are incredibly locked down. Using Splunk Deployment Server (DS) would require punching holes in firewalls to allow forwarders to “phone home” for updates.  This is forbidden.

We faced a choice: introduce a new management technology that might trigger security red flags, or leverage the tool already in place. Since the customer already had Ansible “plumbed” into those secure OT layers for other tasks, it became our tool of choice for orchestration.

Why Infrastructure as Code (IaC)

When you’re onboarding nearly hundreds of GB of data per day across network devices, servers and appliances, manual configuration is a recipe for disaster.  We’ve all seen “configuration drift”—that slow, silent divergence where systems move away from standard configurations over time.

By using Ansible, we gained three critical advantages:

  1. Idempotency: We can run the same playbook ten times, and it will only make changes if the target state isn’t met. No accidental overwrites.
  2. Cross-Platform Consistency: We used the same playbook logic for both Linux and Windows hosts; the automation handled the heavy lifting.
  3. Tag-Based Flexibility: We utilized Ansible tags (like site14 or windows_uf) to handle different physical locations and server roles without needing separate “Server Classes” for every tiny variation.

Mapping Splunk Concepts to Ansible

If you’re comfortable with Splunk, the jump to Ansible is shorter than you think. We essentially re-mapped familiar Splunk architecture to Ansible equivalents:

Splunk ConceptAnsible ImplementationDescription
Deployment ServerAnsible Control NodeThe central “source of truth” running our playbooks.
Deployment ClientInventory HostEach forwarder (UF/IF) is defined in a YAML inventory file.
Server ClassesHost Tags and GroupsWe use tags like linux or uf to target specific systems.
Deployment AppsRoles & Files StructureApps are managed in Git and pushed to targets via playbooks.

What We Learned

Even with the best automation, a tight six-week turnaround like this had its “gotchas.” Here are two lessons that could save you time on your next project:

1. Splunk ARI and GDI Dependency

We were tasked with setting up Splunk Asset and Risk Intelligence (ARI). A key lesson: don’t start the ARI “polish” until the Getting Data In (GDI) is 100% finished. ARI relies entirely on the quality and consistency of your data inputs. If you’re still tweaking data inputs a week before the project ends, ARI dashboards can break. Finish the data onboarding first; the intelligence layer comes second.

2. Permissions and Ownership

Automation is only as good as its permissions. For Linux targets, we had to ensure a Splunk user was consistently defined across all sites to avoid ownership errors upon file delivery. On the Windows side, we found that using the local administrator account for the Ansible connection was the most reliable way to ensure the Splunk service could be restarted remotely after a configuration change.

Conclusion: Focus on the Plumbing

Building a massive Splunk environment in six weeks reaffirmed to us that agility requires automation. By replacing the traditional Deployment Server with an Ansible-driven process, we created a system that is secure enough for the Purdue Model and repeatable for future expansions.

Whether you’re dealing with isolated gas plants or a complex cloud-hybrid stack, having a version-controlled “source of truth” for your configurations is what can save the project.

Ready to modernize your Splunk environment? Contact Us to learn how our experts can help you automate your secure Splunk environment.

Discovered Intelligence Inc., 2026. Unauthorized use and/or duplication of this material without express and written permission from this site’s owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Discovered Intelligence, with appropriate and specific direction (i.e. a linked URL) to this original content.

Splunk asset and risk intelligence

Splunk Asset and Risk Intelligence – a CAASM Solution for Splunk

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Image credit: https://www.splunk.com/en_us/products/asset-and-risk-intelligence.html

At the recent Splunk .Conf in Las Vegas a couple of weeks ago, we were able to get a detailed demo of Splunk’s new and exciting Splunk Asset and Risk Intelligence (Splunk ARI) security solution. What a great solution and one that is much needed within their security solution portfolio. Splunk ARI falls into a category of products known as CAASM – Cyber Asset Attack Surface Management. In this post, we dive a little deeper into what CAASM is, why it is critical tool for your organization and how Splunk ARI can help.

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Splunk Data Integration – Getting Data Out of Splunk

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There are several ways of integrating Splunk within your environment or with your cloud service providers. In this post, we will outline some of the many methods you can use to get data out of Splunk. In a related post, we outline some of the many ways to get data into Splunk. Read more

Splunk Data Integration – Getting Data Into Splunk

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There are several ways of integrating Splunk within your environment or with your cloud service providers. In this post we will outline some of the many methods you can use to get data into Splunk. In a related post, we will outline some of the many ways to get data out of Splunk. Read more

Maintaining Data Visibility across the Cloud and the Ground

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The move to cloud provided services (cloud) has meant a significant shift in where data is generated and stored. No longer is all data generated within the internal boundaries of a company’s own network and data centre (ground). This presents a challenge when it comes to maintaining data visibility and intelligence gathering capabilities – especially from a security and risk perspective. In this post, we will examine several different high-level scenarios and the impact on data visibility of each. Read more

Don’t Neglect Big Data Integration

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In the excitement to implement a big data platform like Splunk or Hadoop, many enterprises put data integration on the back-burner or figure it can be ‘worked out’, once the platform is in place. However, data integration is a key part of a successful big data intelligence strategy and must be given appropriate consideration. Read more

The Incredible Hunk – Splunk Analytics for Hadoop

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Splunk recently announced a new offering, called Hunk. This is essentially a tool that allows for the exploration, analysis and visualisation of data in Hadoop, using the powerful Splunk interface and search engine common to their Splunk Enterprise offering. Read more