From BI to AI: Powering the Next Energy Frontier
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#Data Strategy#Enterprise Data Architecture#Business Transformation#Enterprise Data Warehouse (EDW)#Enterprise Information Management (EIM)#ETL#Data Integration#Teradata
#Strategy#Enterprise Applications Architecture#Digital Transformation#Applications Portfolio Management (APM)#EAaaS
#Data Strategy#AI Strategy#Enterprise Data Architecture#AI Architecture#DPaaS#GenAI Applications#EAaaS#RAG#LLMs & SLMs#Agentic Frameworks
#IT Strategy#EA Strategy#Enterprise Architecture Practice#Digital Transformation#Architecture Review Board (ARB)#Design Authority#EA Governance#EAaaS
#Data & AI Strategy#Data & AI Architecture#Data Lakehouse#Data Integration#Data Governance#Data Engineering#GenAI Apps#APRA Compliance
#Data Strategy#Enterprise Data Architecture#Advanced Analytics#Enterprise Data Warehouse (EDW)#Teradata LDM (TLDM)
#Strategy#Enterprise Applications Architecture#Digital Transformation#Applications Portfolio Management (APM)#EAaaS#Target Operating Model (TOM)
We led the Data & Analytics (DnA) uplift program within the Strategy & Transformation Office of a top Australian energy utility. Our mandate: deliver on the organisation’s vision to become truly data-driven.
By establishing a modern DnA strategy, implementing future-ready architectures, and embedding advanced analytics and AI, we transformed fragmented systems and failed programs into a resilient foundation for sustainable digital transformation.
Transformed a leading energy utility into a data-driven organisation—cutting duplication, enabling AI at scale, and strengthening compliance while delivering a future-ready analytics strategy.
The Challenge
The utility struggled with a history of stalled initiatives and low confidence in its data capabilities, making it difficult to realise its vision of becoming a data-driven organisation.
Business Challenges
- Rising Operational Costs: Regulatory pressure from AER and AEMO compliance increased expenses.
- Limited Visibility: Lack of insights into customer demand, network performance, and asset utilisation.
- Repeated Failures: Previous data transformation initiatives failed, eroding executive confidence.
- Fragmented Structures: Disconnected people, processes, and technology with poor data governance.
IT Challenges
- Legacy and Siloed Data Systems: SAP BW, SAS, MS Dynamics CRM, MDW, and scattered data marts.
- Lack of Canonical Data Model: No unified model to integrate business, operational, and regulatory data.
- Weak Analytics Capability: Absence of an enterprise strategy for AI or machine learning.
- Inconsistent Governance: Poor data quality, unclear ownership, and limited accountability.
Our Approach
- Strategy & Alignment
- Developed a five-year Data & Analytics (DnA) strategy, target state reference architecture, and aligned roadmap with AER submissions and the utility’s digital transformation objectives.
- Shifted focus from fragmented BI projects to an integrated AI-driven capability.
- Operating Model Design
- Established AI Labs to accelerate ML experimentation and platformification.
- Empowered citizen data scientists through analytics catalogue and ML libraries under a data mesh operating model.
- Technology Enablement
- Delivered canonical data models (conceptual, logical, physical, semantic) across IT/OT domains.
- Implemented Microsoft Azure Databricks, Data Lake, HD Insights, SQL Server, RStudio, and Python.
- Integrated data from SAP BW, SAS, MS Dynamics CRM, Metering Data Warehouse, and data marts.
- Built predictive models to address network capacity, consumer demand, and customer experience challenges.
- Governance & Execution
- Implemented data governance and Information Management framework, and Enterprise Data Catalogue (EDC).
- Delivered Data Loss Prevention (DLP) aligned with AEMO and ACSC compliance.
- Established Design Authority and deployed Avolution’s Abacus repository.
- Developed business case, financial models, and risk/benefit frameworks to secure sustained funding.
Outcomes
Our transformation program delivered measurable impact across operations, compliance, and innovation:
- Significant uplift in information management, analytics, and governance maturity.
- Data de-duplication and ownership framework ensured accuracy, consistency, and accountability.
- Analytics catalogue and ML libraries democratised AI — enabling citizen data scientists and business units to leverage advanced insights.
- Improved compliance posture with AEMO and ACSC, reducing regulatory risk.
- Redirection of IT investments from infrastructure towards DnA capabilities, accelerating the shift from BI to AI.
Program Artefacts
Program Technology Stack
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Customer Testimonial
Before, we were marketing in the dark. Now, we know who our customers are, what they want, and how to speak to them. The 360° view has not only increased our revenue but also strengthened our customer relationships.
Sarah Langston, VP of Marketing, NovaRetail Inc.
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/Airlines
Institutionalising Analytics, One Warehouse at a Time
We helped a major telecom break down data silos and unlock a 360° view of its customers, powering real-time insights for 3,600+ BI users. The result? Accurate revenue assurance, smarter decision-making, and a data foundation built to scale.
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