Fragmented BI, fragmented decisions: 5 key challenges for Utilities – and How to Overcome Them
In recent years, utilities have invested heavily in data analytics as a driver of efficiency and decision-making. However, this evolution has not always been supported by a unified strategy around Business Intelligence (BI) platforms.
As a result, many organizations today operate with multiple tools running in parallel, creating a series of technical, operational, and strategic challenges that must be addressed.
Federico Balsa, Data Architect at Quanam, explains that this situation is more common than it seems: “Many utilities adopted new platforms to meet an immediate need, without decommissioning the old ones or integrating them into a unified vision. This leads to duplicated efforts and difficulties in consolidating a single view of the business.”
Below, we break down the five main challenges utilities face when working with multiple BI platforms.
- Lack of standardization in reporting
When different departments use different tools, with their own models and definitions, it becomes difficult to ensure report consistency. This not only hinders result comparison but also undermines trust in the data.
Ignacio Manzo, also a Data Architect at Quanam, sums it up: “The same KPI can show two different values depending on the source. That erodes data credibility and forces teams to spend time explaining discrepancies instead of analyzing causes and making decisions.”
The lack of standard definitions for metrics and data models is one of the main barriers to building a mature analytics culture. This fragmentation also complicates auditing, traceability, and the development of predictive models with a solid foundation.
- Integration issues between tools
Another major challenge is technical integration between platforms. Often, new solutions are adopted without proper communication with existing ones, forcing teams to manually consolidate data or build costly middleware layers.
“Integration between tools should be considered at the design stage,” Balsa notes, adding: “In practice, it’s often handled afterward, becoming a chronic maintenance issue.”
Beyond operational costs, this creates rigidity: any structural change requires adjustments across multiple platforms, slowing the organization’s ability to respond to new business needs.
- High costs
Every BI tool carries costs: licenses, support, infrastructure, and training. When multiple platforms accumulate without a clear rationalization policy, spending multiplies—often without a proportional return in value.
According to Manzo, “the hidden costs of technological dispersion are often higher than the visible ones. Beyond direct budget impact, there’s a significant hit to operational time, vendor dependency, and the ability to scale solutions.”
The proliferation of tools also tends to create dependence on specific technical profiles, making staff turnover or long-term project continuity more difficult.
- Security and compliance risks
Platform sprawl also increases information security and regulatory compliance risks. Each tool may have its own policies for access, encryption, or traceability, making it difficult to ensure a consistent level of protection.
Balsa emphasizes that “well-designed governance allows different platforms to coexist without losing coherence, integrity, or control.” This is essential in the utilities sector, where a weak policy in this area not only exposes the organization to operational errors but can also jeopardize service continuity, customer relationships, and regulatory compliance.
Without centralized management of access, audits, and backups, exposure to incidents, compliance breaches, and data leaks increases—affecting both operations and institutional trust, two key assets for any utility.
- Redundant analytical efforts
Finally, the lack of coordination between platforms fosters duplicated work. Different teams may be developing similar dashboards without knowing it, or reusing inconsistent data.
Manzo points out: “When there’s no common space where data is organized and shared, everyone solves things their own way. That might seem agile at first, but it ends up fragmenting knowledge and limiting the organization’s ability to learn collectively.”
This redundancy not only consumes resources but also hinders continuous improvement. Lessons learned aren’t consolidated, and advances in one area rarely transfer to others.
The path toward consolidation
Overcoming these challenges requires moving toward a more integrated BI model, with a coherent technology architecture, clear rules for report generation, and an active data governance policy.
As highlighted by international organizations such as the American Water Works Association (AWWA) and the Utility Analytics Institute (UAI), platform fragmentation and lack of standardization remain critical barriers to digital transformation in utilities.
Addressing these obstacles not only improves operational efficiency but also strengthens institutional capacity to respond to emerging regulatory, environmental, and service challenges.
Building a unified data catalog, defining shared metrics, and ensuring interoperability between tools are fundamental steps in this direction.
Solutions are neither one-size-fits-all nor immediate, but the first step is acknowledging current limitations and creating a realistic evolution plan focused on simplification, efficiency, and security.
If you’d like to explore how to build the organizational foundation for a strong BI framework, we invite you to read our article: “Why Data Governance Matters in the Utilities Sector.”
Author: Journalist Alejandro Acle
Co-authored with: Federico Balsa and Ignacio Manzo, Data Architects at Quanam