Turning Data Chaos into Business Opportunity—Together
Let me be real with you for a second. Anyone who knows me knows I tend to join scrappy startups. So why would someone like me join a big, established tech company like Splunk—especially after Cisco bought them?
The answer is simple: we're sitting on the biggest data opportunity in business history.
The AI and Data Explosion: What’s Really Happening?
AI is already woven into our daily work. It helps us answer questions, plan projects, and create content. But here’s the twist: so far, AI has mostly made people more productive. What happens when AI starts working directly with the data machines generate?
Think human data is big? Machine-generated data dwarfs it. In 2020, over 40% of all internet data came from machines. By 2028, we’re looking at 394 zettabytes globally—a mind-boggling number. IoT devices alone will crank out 90 zettabytes, and log data is growing at an explosive 250% per year.
But here’s the real challenge: about 90% of enterprise data is unstructured—think logs, sensor readings, and telemetry. This kind of data grows much faster than the structured data we’re used to, and it’s much harder to analyze. Yet, it’s packed with business value if you can unlock it.
Why This Matters for Your Organization
The Stakes: Competitive Advantage or Missed Opportunity
If you can harness your machine-generated data, you’ll build a competitive moat that’s hard to cross. Right now, only about 45% of Fortune 1000 companies compete on data and analytics. That leaves a huge opportunity for those who get it right.
Companies with strong data strategies are 1.5 times more likely to benefit from AI. Data-driven organizations are 58% more likely to beat their revenue goals. This isn’t just about efficiency—it’s about winning in your market.
The Cost of Inaction
The numbers are sobering:
- 70-85% of AI projects fail to meet expectations.
- 90% don’t deliver meaningful ROI.
- 50% never get past the prototype stage.
- 42% of companies say data issues delay or doom their AI projects.
Why? Because most organizations struggle with data quality, accessibility, and integration. Only 67% fully trust their data for decision-making. Between 60-73% of enterprise data goes unused for analytics, and only 1% of all generated data is ever analyzed.
That’s trillions in unrealized value. If you don’t act, you risk falling behind as competitors use their data to move faster and smarter.
The New Reality: Infrastructure and Operations
The move to cloud, Kubernetes, and microservices has multiplied the amount of data you have to manage. Every interaction in these distributed systems creates more logs and telemetry than ever before. Gartner says 75% of enterprise data will be generated outside traditional data centers by 2025. Edge computing is booming because we need to process data right where it’s created.
But here’s the rub: most organizations spend more time maintaining data pipelines than delivering business value. Data silos, inconsistent formats, and security issues make it even harder for AI to deliver results.
I’ve been there. As a data engineer, I worked with a team of 20 just to keep ETL (extract, transform, load) pipelines running. Even then, we struggled to keep up with the volume and complexity. Today’s data growth makes that approach impossible to scale.
What’s the Solution? Rethinking Data Management
Let’s get practical. Most machine-generated data is just different views of the same event. Network captures, security logs, application logs—they’re all perspectives on what happened in your environment. If you store them separately, you create data silos and extra costs. Worse, many organizations store the same logs in multiple systems, multiplying expenses.
The market for data observability and AI observability is exploding. Why? Because organizations need continuous visibility into their data and AI models to stay resilient and competitive.
How Splunk and Cisco Partner With You
Unified Data Management for the Real World
Splunk’s Data Management platform gives you centralized control over your data pipelines. With tools like Pipeline Builders powered by SPL2, you can filter, mask, transform, and enrich your data before it ever hits storage. This means you can preprocess data through a single pipeline and give your security, IT, and engineering teams more control than ever.
You’ll break down silos, unify log and metrics collection, and tailor data flows to your business and compliance needs. No more paying to store the same data in multiple places.
Lower Costs, Higher Value
Splunk helps you reduce costs by letting you choose how and where to store your data. With flexible filtering and routing, you avoid the trap of redundant storage and processing. You get richer context for analysis, sharper insights, and a lower total cost of ownership.
Accelerate Your AI Success
Remember those AI project failure rates? Splunk tackles the root cause: data quality and accessibility. Our platform ensures your data is accurate, accessible, and structured for AI—so you can move from reactive monitoring to predictive analytics. That means catching issues before they impact your business, not after.
Digital Resilience with Cisco
The Cisco-Splunk partnership brings together best-in-class security, observability, and network intelligence. You get real-time visibility across your entire digital landscape, helping you defend against threats, prevent outages, and deliver seamless experiences. This is especially critical as you connect more people, apps, and devices—and face more risks.
What Does Success Look Like? Real Outcomes
- Operational Efficiency: Companies using Splunk have cut mean time to resolution by 94% and prevented hundreds of hours of downtime.
- Revenue Growth: Data-driven businesses see faster sales growth, lower acquisition costs, and better market penetration.
- Competitive Edge: Real-time data processing lets you spot opportunities and risks before your competitors do.
- Security and Compliance: With unified data management, you reduce risk, ensure compliance, and foster collaboration across teams.
Your Next Steps: How to Get Started
- Assess Your Data Readiness: How accurate, complete, and accessible is your data? This is the foundation for any AI or analytics project.
- Set Clear Goals: Align your data strategy with business objectives and define what success looks like—whether it’s accuracy, speed, cost reduction, or customer satisfaction.
- Build a Data-Driven Culture: Invest in training and resources so your teams can use data effectively. Communicate openly about the transformation to foster buy-in.
- Plan for Scale: Choose infrastructure that can grow with your needs. Cloud services, distributed computing, and modular architectures are your friends here.
- Monitor and Improve: Set up feedback loops to track performance, detect issues, and retrain models as needed. Continuous improvement is key.
Why This Moment Matters
The explosive growth of machine data—with enterprises generating over 400 million terabytes daily and machine-generated data accounting for 40% of internet traffic—combined with AI's persistent 70-85% failure rate due to data quality issues, creates a once-in-a-lifetime opportunity.
Splunk's position at the intersection of machine data analytics and AI, amplified by Cisco's $28 billion acquisition, positions us to solve the fundamental data readiness crisis that prevents enterprises from realizing AI's transformative potential.
This represents more than a technology shift. It's a fundamental reimagining of how enterprises can unlock value from vast streams of untapped machine-generated insights that currently remain unexploited.
Why wouldn't I want to be part of that?
