Alex Galbraith | CTO | Cloud Services | SoftwareOne | mail me |
Will your business still be alive in 10 years? Many will not. The pace of change is leaving even Moore’s Law behind. Nvidia’s Artificial Intelligence (AI) hardware supremo, Jensen Huang, recently claimed that AI capability has increased a million-fold in the last decade. He also suggested this growth may continue at the same pace.
To say established businesses face disruption would be an understatement. But is it hopeless? Absolutely not. While the future is uncertain, the direction is clear. It requires technology leaders to look ahead, think boldly and start iterating toward their vision today. Success lies in defying gravity and surviving disruption.
Today’s data decisions, tomorrow’s market relevance
Many businesses will live or die based on their effective use of data. Customer insights, service quality, time to market and even the core proposition of many companies will increasingly depend on analytics, automation, and generative AI fueled by data.
If your business is not moving in this direction, the competition certainly will. Our research shows that within two years, 79% of businesses plan to make significant progress in transforming processes and customer interactions with AI. With this in mind, leaders must plan the infrastructure, architecture, culture and governance required to fully leverage data assets.
No concept illustrates this urgency better than “data gravity”. Coined by David McCrory over a decade ago, it is more relevant today than ever. The key insight is simple: today’s decisions about data create path dependencies. These paths can hinder the adaptation businesses need to remain competitive. This is the hidden challenge of defying gravity and surviving disruption.
The cautionary tale of Heritage Retail
Consider a hypothetical example from the last wave of digital disruption. Heritage Retail was a household name in retail for many years. Despite this success, it began losing market share to cloud-native online retailers with lower costs, greater agility, and better customer insights. Revenue was shrinking, and executives knew they had to act.
The vision was bold. Heritage Retail wanted to transition from a monolithic, on-premises infrastructure to a nimble, cloud-native ecosystem. This shift would improve customer experience and unlock advanced analytics.
The initial stages of the project seemed promising. However, as the project touched core operations, the crippling effects of data gravity and inertia became painfully clear. Their decades-old CRM and sales data warehouse contained petabytes of information. Because it was the operational fulcrum, business-critical apps such as the e-commerce platform and supply chain management system were tightly bound to it. Years of ad-hoc integrations and data feeds created a tangled and poorly documented web of dependencies.
In data gravity terms, the mass of this data meant moving one piece of the puzzle required moving all interconnected parts simultaneously. The sheer data volume also made migration egress fees eye-watering.
The team faced a stark choice: attempt a risky, expensive “big bang” migration of the entire ecosystem, or keep core applications on-premises. The latter option effectively halted the modernisation of critical business functions.
For Heritage Retail, failure to modernise iteratively toward a long-term vision painted them into a corner. Data gravity, not strategy, had guided IT evolution. Over time, its compounding effects created a mass of interdependent systems too cumbersome to modernise. By the time Heritage Retail needed to respond, its data assets were locked in inertia, thwarting its ability to adapt. They had failed at defying gravity and surviving disruption.
Overcoming data inertia
So, what can today’s established businesses do to avoid a fate like Heritage Retail?
Three steps stand out:
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Know what you have
Most organisations already feel the weight of data gravity. The first step is knowing your key technology and data liabilities and assets. What is mission critical? What are the costs and dependencies? The sooner you map this out, the sooner you can build a plan. Architecture and licensing reviews with a trusted partner can accelerate this discovery process.
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Know where you are going
A vision must accompany a modern data strategy. This strategy should define how your business uses, stores, connects, classifies, and governs data assets. The effort must be cross-functional. Technology is one part of a trifecta that also includes people and process. Line-of-business experts provide context for data value, while technology experts define what is possible. Together, you can create a roadmap geared toward both short and long-term outcomes.
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Start now and avoid boiling the ocean
Large organisations have vast, complex data landscapes. Trying to catalogue, cleanse, and govern every dataset before launching cloud initiatives can create analysis paralysis. Instead, prioritise based on benefits, risks and dependencies. Address sensitive or critical data first, focusing on security, compliance and quality. Less critical data can be handled iteratively as migration or new applications roll out.
The effort required to improve data quality varies depending on usage. Data for operational tasks, analytics, or AI training may each need different standards. Focus on making data “fit for purpose” so it supports its intended use. Standardising metadata is one practical step that helps both systems and users.
Move ahead with confidence
Data and technology strategy are crucial for preparing for the AI-driven future. Making strong, informed decisions today will shape tomorrow’s competitiveness. With discipline, vision and action, organisations can avoid inertia. They can chart a course toward resilience by defying gravity and surviving disruption.
