Operational Intelligence: Addressing Data Fragmentation in Utilities and Infrastructure
Utilities and infrastructure providers are generating more data than ever, from operations systems, finance logs, and thousands of sensors. Yet many organisations are still ‘data rich but insight poor’. Despite enormous data volumes, the information needed for decision-making is often scattered across siloed systems, duplicated in conflicting reports, or hidden in teams’ offline spreadsheets.
Instead of enabling transformation, data becomes a barrier to it.
What Are The Challenges?
When Data Works Against You
Research shows that 84% of executives believe data silos directly slow decision-making and reduce organisational agility [1]. In fact, many leaders admit that they do not fully trust their own dashboards because each department maintains its own definitions, its own reporting templates, and its own interpretation of what ‘good’ looks like.
This lack of coherence creates structural drag across the business. Decisions take longer and teams lose visibility of what is truly happening within the organisation.
The Hidden Cost of Disconnected and Fragmented Systems
When data is split across independent systems, stakeholders lose the ability to see their operations end-to-end. For example, siloed utilities often fail to detect inefficiencies or emerging issues because their teams work from incomplete datasets.
Examples play out every day:
Asset performance data sits in maintenance systems while customer complaints sit in CRM platforms, thus no one sees the connection.
Smart meter anomalies indicate potential faults, but because meter data lives outside core operational systems, problems surface only when customers report outages.
Finance tracks asset age and depreciation separately from engineering teams tracking condition data, leading to misaligned investment priorities.
These blind spots force organisations into reactive mode. Managers often scramble across systems just to understand how operations are running, discovering issues too late to prevent them.
Why Innovation Gets Stuck
Modern transformation, from predictive maintenance to AI-driven forecasting, depends on joined-up data. However, most utility providers cannot feed AI models or advanced analytics because they are still stitching together disconnected data points.
Organisations without a unified data foundation struggle to scale pilots beyond isolated use cases. Each new digital initiative requires bespoke integration, custom transformations, and repeated data cleansing. The result is innovation that begins with promise but fails to embed.
Without unified data, even the best technology becomes an isolated experiment.
How do we resolve them?
What a Unified Operational View Really Looks Like
A unified operational view is not a single dashboard. It is a connected ecosystem where data is consistent, governed, and accessible across the organisation.
Modernise the Data Architecture
Modernisation does not mean replacing everything. It means connecting everything.
Many utility providers are now adopting:
Cloud data lakes / lakehouses to consolidate raw, structured, and unstructured data
Real-time streaming ingestion from sensors and meters
Data fabric architectures to create a unified data layer across on-premise, cloud, and operational systems
A data fabric can provide a consistent, secure, real-time view of all enterprise data.
This allows analytics teams and operational managers to draw from the same integrated environment without requiring every platform to be replaced.
Deliver Insights Where People Need Them
Data becomes useful when it becomes visible. This is where unified dashboards and shared analytics environments matter.
When organisations present standardised KPIs through a single analytics platform, leaders gain real-time end-to-end visibility, from customer behaviour to asset performance to network load.
And when teams access the same view, discussions shift from debating whose data is ‘right’ to aligning on what action to take next.
Self-service analytics promote higher engagement, whereas organisations without self-service tools can suffer from missed opportunities and teams that stop engaging with data altogether.
The Human Change Behind the Technical Fix
Data fragmentation is not fixed by technology alone. Silos form because teams protect their systems, definitions, and processes. Breaking them requires cultural change.
Effective governance includes:
Clear data ownership and stewardship
A shared data dictionary
Cross-functional governance forums
Role-based access control and security guardrails
Upskilling staff to read, question, and use data confidently
Technology connects the systems – governance connects the people.
Why does this matter?
For utilities facing increasing demand, ageing infrastructure, and rising regulatory pressure, a unified view of operations is no longer optional. It is the foundation for transformation, resilience, and innovation.
Organisations that fix data fragmentation gain:
Faster decision-making
Higher operational reliability
Improved regulatory confidence
Better customer outcomes
The ability to scale AI and automation
A culture that trusts and uses data
‘Operational intelligence’ is not a theoretical exercise, it is the practical ability to run a company safely, efficiently, and compliantly while budgets tighten and scrutiny increases. The challenge we see repeatedly is not a lack of data, but a lack of usable, trusted information that flows across operational, customer, finance, and engineering teams in time to make better decisions.
In short, utilities do not need a disruptive overhaul. They need a structured, phased journey that builds trust, consistency, and clarity, because making data work end-to-end is how improved resilience and performance is unlocked. This is exactly the kind of structured, phased change that Deecon supports, working alongside organisations to enhance their data environments and build operational intelligence that leaders and front-line teams can rely on.
References
[1] https://hbr.org/resources/pdfs/comm/splunk/Overcoming_Barriers_to_Data_Impact.pdf
Words by Marianne Bedon
Edited by Kate Randall