Why AI and Data Management are Tech’s New Power Couple

AI and data management amplify each other’s strengths, taking your data strategy to new heights.

Some things just hit different when they’re together — like Mario and Luigi, or Hot Cheetos dipped in Nutella (don’t knock it till you try it). Think Beyoncé and Jay-Z, or Spider-Man and his multiverse squad. They don’t just work well — they elevate each other. That same energy applies to your AI implementation and your data strategy. Alone, they’re solid. Together? Potentially unstoppable.

Here’s how: AI depends on clean, quality data, so, naturally, a strong and sustainable data management strategy is critical for successful AI initiatives. But the opposite is also true. AI helps fill gaps in your data management practices by automating workflows, improving productivity, and quickly detecting anomalies to keep data accurate and reliable. 

In Splunk’s recent “New Rules of Data Management” report, we articulate how AI and data management complement each other. That said, AI can pose challenges to your data, and we’ll cover that too. Here’s a glimpse into the findings, and how they can benefit your data environment and the future of your business.

Data management elevates AI ambitions

It’s no secret that a robust data management strategy will be a force multiplier for any current or future AI rollout. A whopping 85% of respondents said their successful AI models were driven by clean, quality data, thanks in no small part to their solid data management strategy. 

In fact, survey participants reported significant benefits of a strong data management strategy across numerous categories. The overriding theme is that AI’s success hinges on the accuracy of its data. 82% said a careful data strategy improved the accuracy of their machine learning models, while 74% reported that their data management efforts reduced bias in the data sets from which AI models learn. 

Accurate data allows organizations to generate accurate insights, creating a foundation of trust in reliable outcomes. Accordingly, 85% of respondents said that their data management strategy provided enough volume and variety of data to generate valuable insights, while 81% noted that their data management strategy made it easier to explain how AI models arrived at their conclusions. 

Across security and observability, 81% of organizations said they leveraged insights from related tools to enhance AI model training and performance. Specifically, 64% reported using cross-domain data to improve AI-driven anomaly detection and incident response. 

Clearly, a strong data management strategy provides a solid foundation for AI success. By implementing data management best practices, you not only lay the groundwork for effective AI, but also ensure that it can scale and adapt over time. 

AI gives data management a boost

It’s not a one-way street. Just as AI benefits from data management, data management receives a significant boost from AI. Nearly all survey respondents (98%) agreed that AI had made their data management strategy easier, with a third (33%) saying it made it significantly easier. 

No surprise, AI provided the most value by performing routine, administrative functions. 73% of respondents said AI helped enhance data quality by automating repetitive tasks, thereby accelerating productivity and freeing up analysts to focus on more critical activities that drive the business. These tasks range from report generation and documentation to data entry and detecting threats such as malware or phishing. 

Beyond the mundane, respondents also reported success leveraging AI for innovation. Fifty-nine percent of respondents reported that AI helped with data discovery, by scanning large data sets to identify patterns, trends, and anomalies. This has proven crucial in detecting evasive threats, reducing alert fatigue, and uncovering critical business insights amid the data noise.  

AI imposes (a few) roadblocks

Like any new initiative, AI comes with growing pains, particularly for organizations already struggling with complex data environments. 

Many organizations are finding that AI can actually make data integration more difficult, complicating the already challenging task of managing massive, fast-growing data volumes.

While AI offers major advantages for data management, it also adds complexity and volume to existing stockpiles of data. The good news is that these problems are not insurmountable. Organizations with a solid data strategy will likely be well equipped to integrate AI, or any new technology, without fear of disruption or losing control of their data.

Ultimately, data management and AI are inextricably linked, each enhancing the effectiveness of the other. The accuracy of your AI is directly related to the quality of data it can access. At the same time, AI uplevels data management by automating processes, expanding visibility, improving detections, and optimizing storage. 

The old adage, “Garbage in, garbage out,” still applies. Good, clean data leads to better AI performance and superior outcomes. Organizations that invest in both will realize benefits that can supercharge their data-driven decision-making and take their AI initiatives — and business goals — to the next level.

To learn more about the impact of AI on your data management strategy, download The New Rules of Data Management report.