Assessing What Matters with a DI Data Source Assessment Workshop
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.
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