Moving beyond nice-to-have data DASHBOARDS: TREATING data platforms as a strategic business imperative worth trillions
Data work is often seen as a simple means of producing analysis via analytics tools – a way to track historic performance and create reports or dashboards: often viewed as a nice-to-have investment option, particularly in a tough economic outlook. But this limited perspective misses the real opportunity of data. Whilst every organisation likes to call itself data driven, the truth is only the very few at the top of their game can say that and really mean it with conviction.
Done properly, data doesn’t just show what happened in the past – it proactively highlights risk, solves complex business problems, enables long-lived transformation, and delivers measurable value multiple times over. Unlike the mythical 10x developer or the 10x digital transformation consultancies sell you, this could be a genuine 10x data transformation opportunity.
Here’s the opportunity: A report by McKinsey suggests that data centric organisations could collectively unlock $1.3 trillion annually in economic value in the US alone, through cost savings, operational efficiency, and new revenue streams made available through the exploitation of open data and AI advancements. Yet most enterprise organisations are not even close to exploiting this potential.
A report by McKinsey suggests that data centric organisations could collectively unlock $1.3 trillion annually in economic value in the US alone
Scaling AI efforts beyond prototypes requires data to be invested in as a foundation of transformation to catalyse meaningful organisational progress
Why is this? Because they’re stuck with outdated, fragmented approaches to data management. Data centricity is often an afterthought and fails to penetrate organisational strategy, enterprise architecture, and delivery – this can be down to a lack of knowledge and skill being held at the right levels of an organisation.
Arguably more crucial, is the fact that investment in digital transformation or re-platforming efforts often fail to consider data as a strategic imperative, which results in missed opportunities, additional complexities, and layering technical debt on top of already shaky foundations. We’ve all seen a digital transformation programme that has a data work stream bolted on because it’s ‘not as important’ or ‘can be done later’.
Scaling AI efforts beyond prototypes requires data to be invested in as a foundation of transformation to catalyse meaningful organisational progress. Investing in data infrastructure and platforms is an inherently sensible centre point with a long-lived investment case for both digital transformation and any future forays into AI/ML. Save your prototyping and AI tool money to invest in infrastructure foundations and platforms.
Data: core to business transformation, not just an analytics tool
From data insights to data action
Data is often treated as a means/tool to reflect on past performance. But its real value comes when it is used to solve problems and deliver business outcomes. When data is fully integrated into business strategies, operating models, processes and decisions, organisations can expect to:
- Predict customer needs: Boost retention rates and customer satisfaction.
- Uncover inefficiencies: Reduce costs by streamlining business operations.
- Mitigate risks: Resolve threats before they become expensive issues.
Organisations that can pivot from using data as an analytics tool to a problem-solving asset can radically transform their efficiency and growth trajectory. For example, McKinsey suggests a 25% cost reduction when using data in supply chain operations.
Enabling business transformation
Every transformation effort – digital, operational, or customer-focused – needs data to succeed. When data is at the core and considered from the outset, it enables:
- Precision: A clear understanding of the current state and areas of improvement.
- Measurement: Tracking ROI and ensuring initiatives are delivering value.
- Opportunity discovery: Identifying inefficiencies, untapped markets, or cost-saving measures.
For example, businesses that fully leverage data in digital transformation initiatives report significant increases in operational efficiency of ~30%, often creating millions in savings annually – paying back the investment multiple times over.
The challenge of legacy
Organisations are still struggling to manage their data effectively, and the consequences are significant. According to Gartner, poor data practices cost organisations an average of $12.9 million annually, through inefficiencies, wasted resources, and lost opportunities. Here are five key causes:
- Fragmented, siloed solutions: Many organisations operate with disconnected point-to-point data systems. These fragmented solutions create silos that prevent teams from accessing a unified view of the business.
- Inconsistent practices: Without consistent data management practices – like quality controls, standardisation, or observability – teams are left grappling with unreliable or incomplete data. This slows decision-making and erodes trust in insights.
- No data governance: Without governance, data usage becomes chaotic. Undefined roles, policies, and compliance measures lead to inefficiencies, errors, and increased risk, costing both time and money.
- Redundant tools, lacking security: Organisations often overspend on duplicate tools that don’t integrate well or lack a unified security framework. This increases costs and exposes businesses to potential breaches or regulatory fines.
- Dark data costs: “Dark data”- unused or forgotten information (~60-90% of data in most organisations is unused!) – costs businesses massive sums of money annually in storage, compliance risks, and missed insights. Worse, it contributes to environmental harm, increasing the carbon footprint of digital operations. In addition, it increases the cyber security attack surface.
The ROI of doing data properly
The financial upside alone of prioritising data management and data infrastructure investment is enormous. Here’s how businesses can unlock value:
- Operational efficiency: Data-driven organisations reduce inefficiencies and optimize workflows, saving up to ~25% in operational costs, particularly in supply chain and production environments. For a mid-sized business with $1 billion in annual revenue, that’s a potential $250 million in savings.
- Revenue growth: According to Accenture, personalized, data-driven customer strategies can increase sales by ~10-15%. For large enterprises, this translates to hundreds of millions of dollars in additional revenue annually.
- Risk mitigation: Proactive data governance reduces regulatory risks and compliance costs, which can save millions of dollars in potential fines or breaches e.g., IBM’s 2023 report on data breaches shows that the average cost of a breach is $4.45 million. A unified security framework protects assets and minimizes downtime.
- Cost avoidance: By eliminating redundant tools and consolidating platforms, organisations can cut software and infrastructure costs by ~20%, saving millions of dollars annually. This is a particular concern in large, complex enterprise organisations where the IT estate often consists of multiple platforms with duplicative feature sets e.g., it’s common to see multiple integration layers and analytics tools as well as legacy systems. Data is key to unlocking architectural options, integrating data, and giving data ownership back to organisations.
- Real-time decision making: Integrated data infrastructure, tools, and platforms empower organisations to pivot faster and seize opportunities, adding incremental value that far outweighs the initial investment.
Why big bang data WAREHOUSE projects are outdated
The days of waiting for a massive, big-bang data warehouse project to finish to unlock value should be behind us. Modern data platform approaches – such as lakehouse architecture – focus on agility, quick wins and incremental growth:
- Start Small, scale fast: Instead of trying to solve every data challenge upfront, prioritise high impact use cases:
- Tackle specific problems, like increasing customer retention or optimising logistics.
- Deliver results quickly through missions, then scale to broader initiatives.
- Use modular, agile data platforms: Cloud-native, scalable solutions eliminate the need for heavy upfront investment. With solutions like the data lakehouse and low-code platforms, businesses can:
- Build and deliver value incrementally.
- Adapt to changing needs without overhauling infrastructure.
- Align data with business goals: Data efforts should be tightly focused on generating business outcomes. Where viable, start with measurable objectives – like saving $10 million in costs or increasing revenue by 15% – this will ensure every data initiative delivers tangible ROI.
Thedays of waiting for a massive, big-bang data warehouse project to finish to unlock value should be behind us.
What distinguishes leading data organisations
Organisations that lead in data and broader transformation see data as a strategic enabler and invest in data platform-oriented approaches. Here’s what sets them apart:
Leaders
- Build unified, scalable platforms that integrate seamlessly.
- Use modular, cloud-native architectures to deliver value incrementally.
- Implement governance frameworks to ensure data quality, compliance, and security.
- Focus on solving real problems – like improving customer experiences or reducing costs – rather than overbuilding infrastructure.
Laggards
- Operate with fragmented systems that prevent collaboration.
- Lack consistent practices, leading to poor data quality and inefficiency.
- Spend millions on redundant tools without realising ROI.
- Treat data as an afterthought rather than embedding it into transformation efforts.
Where to start on your data journey Where to start on your data journey
- Audit your data ecosystem: Identify silos, redundant tools, and inefficiencies as part of an enterprise architecture approach that considers data.
- Prioritise high impact use cases: Start small and focus on measurable wins – these are critical missions that build momentum without requiring big-bang transformation programmes from the outset.
- Adopt modern, cloud native platforms and tools: Leverage cloud-native platforms and low-code solutions to accelerate value.
- Implement robust governance: Build trust in data through consistent quality, security, and compliance. Treat data as a product and embed DataOps.
- Measure ROI continuously: Track savings, efficiencies, and revenue growth to demonstrate success.
Data is a huge financial opportunity to exploit whilst doing good in the world
The investment case for data must go beyond data dashboards and reports, CEOs are interested in the gnarly business problems that data and AI can solve, they’ve been oversold and underwhelmed on dashboards – it’s time to think bigger.
The potential of data extends far beyond analytics – it’s the foundation for solving problems, transforming operations, and driving growth. Strong data expertise can save most enterprise organisations millions of dollars annually, generate new revenue streams, and ensure AI readiness that can scale beyond prototyping.
In addition, it can help organisations address the hidden risks/issues caused by dark data (i.e., ensuring compliance but also reducing your carbon footprint in the process), gain financial control of spiralling cloud costs, improve security posture, eradicate legacy systems, and provide better solutions to staff and customers.
The investment case for data must go beyond data dashboards and reports
