In today’s Tech Pulse, gain insight into how:

• Robust data infrastructure forms the backbone of effective AI, highlighting why Salesforce’s $8 billion Informatica acquisition is reshaping enterprise AI strategies.

• Data readiness remains a major hurdle for GenAI, with 60% of leaders unsure their data can support the competitive edge they expect GenAI to deliver.

• Scalable, modular data architecture empowers organizations to future-proof their analytics, ensuring alignment with business goals without over-complicating infrastructure.

Each of these articles is penned by members of Forbes Technology Council, key luminaries shaping the future of technology leadership.

Grab your coffee, and let's dive in!

Reliable Data: The Unsung Hero of AI Success

AI may grab the spotlight, but it all hinges on what happens behind the scenes: robust data infrastructure. Companies eager to leverage AI often neglect the critical groundwork required to unify and manage data—a mistake that can derail even the most promising initiatives.

Salesforce’s $8 billion Informatica acquisition underscores how data integration can make or break enterprise AI strategies.

Here’s why building strong data foundations is non-negotiable:

🧱 Data Silos Are the Enemy: Fragmented systems hinder AI’s ability to provide context and insights.

⚙️ Integration Fuels Context: AI thrives on real-time, unified data streams—a must for effective decision-making.

🔓 Watch for Vendor Lock-In: As ecosystems scale, balance speed and simplicity against long-term flexibility.

🔧 Open Source = Freedom: Invest in open systems to keep control, avoid dependencies, and maximize scalability.

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Is Your Data-AI Strategy Ready for GenAI? Most Aren’t

A recent survey of 170+ business leaders reveals a critical gap: 79% believe GenAI will deliver a competitive edge by 2026, yet 60% doubt their data is ready to support it. This disconnect highlights why so many GenAI projects stall due to unprepared data foundations.

With 80% of enterprise data unstructured and ungoverned, companies need to build a stronger data strategy to unlock GenAI’s full potential.

Here’s all you need to know:

🔍 Unstructured Data, Big Problem: Most data lacks organization, making reliable AI results nearly impossible.

🏗️ Start With Data Management: Invest in structuring, governing, and preparing your data to make it usable by AI.

🧠 Prioritize AI Literacy: Upskill teams to interpret AI outputs and align use cases with business goals.

🎯 Define Clear Use Cases: Focus on solving key challenges, like customer experience or operational efficiency, with structured, high-quality data.

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Future-Proofing Data Architecture: Smarter Strategies for Tech Leaders

Scaling data infrastructure is a challenge for many organizations. Leaders often adopt trendy tools, like real-time pipelines or serverless computing, without aligning them to the business use case, causing inefficiency and inflated costs.

By focusing on strategy, not hype, executives can build scalable, flexible analytics architectures that drive long-term value.

Here’s how to approach it:

🛠️ Use Business Needs as Your Guide: Let use cases dictate architecture. Don’t adopt “real-time everything” unless it’s actually needed.

🚀 Serverless isn’t a Cure-All: Great for agility but not ideal for compute-heavy or long-running tasks. Balance with hybrid models.

🧩 Embrace Modularity: Build flexible ecosystems with fit-for-purpose tools like Apache Kafka, Airflow, or Snowflake.

📊 Logging > Dashboards: Structured, consistent logs improve debugging, performance tuning, and compliance.

🤖 Leverage AI & Low-Code: Amplify team impact by automating infrastructure management and streamlining development.

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Wrapping Up

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