Many companies expected data to empower them, but instead, it has led to a situation where data storage has become more of a burden than a benefit. Businesses have spent significant amounts of money over the last ten years amassing data in the hopes that it will be useful someday. However, a large portion of this enterprise data remains unused, sitting idle and losing value over time.

We are now at a critical juncture where companies must take action regarding their neglected data to avoid becoming irrelevant. The Age of Intelligence is approaching rapidly, with AI playing an active role in all aspects of business operations. Autonomous systems are increasingly driving decisions and reshaping how businesses function and compete.

This transformative shift necessitates companies to completely rethink their data approach. Successful companies in this new era will not only gather data but will also construct an interconnected intelligence framework that facilitates collaboration between human insights and autonomous systems. These leading companies will embrace new principles regarding data management, transforming it from a passive asset into a dynamic strategic resource.

The new data principles can be categorized into three main areas: strategy, investment, and operations. Strategy involves positioning data to gain a competitive edge by stressing connectivity and real-time adaptability. Investment focuses on establishing essential capabilities like unified data platforms and widespread data literacy. Operations detail how strategies and investments convert into actionable insights and seamless cooperation between humans and AI systems.

Here are some key insights into these new rules:

1. Neural Networks Over Filing Cabinets
Traditional data storage treated information like files in cabinets – well-organized but isolated. A significant portion of enterprise data remains underutilized, leading to missed opportunities, heightened risks, and poor decision-making. In the Age of Intelligence, companies must handle data differently.

For instance, a global fintech company adopted a unified data architecture that supports billions of transactions daily, enabling real-time fraud detection and instant credit decisions. This interconnected neural approach to data encourages rapid innovation, facilitating swift decision-making that was unimaginable with traditional siloed data methods.

In contrast, legacy institutions often struggle with fragmented data structures resulting from mergers, acquisitions, and changing regulations. Data fragmentation isn’t just an IT issue; it poses a competitive weakness or even an existential threat known as “data debt.” Similar to financial debt, accumulating data debt gradually hinders agility and increases vulnerability.

Digital-native companies without this burden can outperform traditional players by utilizing their interconnected data effectively.

2. Velocity Trumps Volume
Having more data doesn’t necessarily translate to an advantage. In the Age of Intelligence, the competitive edge comes from converting data into actions and decisions faster than competitors can do so. Companies excelling in real-time “data in motion” outperform those stuck with stagnant “data at rest.”

A prominent fast-food leader leverages real-time analytics to personalize customer experiences, streamline operations, and swiftly adjust menus and marketing strategies based on immediate insights. This agility showcases how rapid data processing differentiates businesses in highly competitive markets.

Similarly, a major U.S.-based bank relies on real-time velocity to promptly detect fraud, ensuring security and trust for its clients.

3. Integrated Data Ecosystems
Companies must think beyond their boundaries in the Age of Intelligence; orchestrating data across ecosystems becomes crucial for success.

Leading organizations don’t thrive by hoarding proprietary information but by managing unified data ecosystems spanning their entire value chain, including partners, suppliers, and customers.

For example, a multinational retail chain established a responsive supply chain integrating supplier and store information along with weather forecasts and local demographics. This real-time ecosystem helps optimize inventory management, pricing strategies, and staff allocation while significantly reducing waste and enhancing customer satisfaction.

4. Empowering Autonomous Systems
As autonomous decision-making systems become more prevalent, establishing trust in data becomes fundamental.

A global payment card services company processes nearly one billion transactions daily using unified data architectures for precise autonomous fraud detection. Trust here is not merely a compliance requirement but a competitive advantage enabling innovation.

Industries such as healthcare, finance, and entertainment increasingly rely on autonomous systems; hence embedding trust deeply within operational processes becomes paramount for success.

5. Facilitating Human-AI Collaboration
Future-ready organizations won’t be entirely automated; they will excel at integrating human judgment with AI-driven insights.

For instance, combining real-time delivery information with drivers’ local knowledge optimizes routes efficiently improves operational efficiency significantly enhances customer experience by saving 100 million miles annually compared to individual efforts alone.

Businesses need to design robust data strategies promoting seamless collaboration between humans’ strengths and intelligent systems for optimal outcomes.

Are You Prepared for Success?
This new era requires fresh perspectives; your competitors aren’t just accumulating vast amounts of unutilized data—they’re constructing interconnected intelligence networks facilitating rapid autonomous decisions across ecosystems.

Reltio has extensively researched these challenges over the past decade while devising essential principles capable of transforming enterprise-level data into your strategic asset. Is your organization equipped to thrive in the Age of Intelligence?