By Muhammed Mafawalla • Published 24 Apr 2025 • 5 min Read

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From boardrooms to back offices, the buzz around AI is deafening.  CEOs want it featured in their strategic plans. CIOs are tasked with bringing it to life. Business teams ask, “Can’t we just use ChatGPT for that?” The appetite for AI is both real and warranted. When implemented correctly, AI can streamline operations, reduce costs, improve forecasting accuracy, and uncover new revenue opportunities.

Yet, an uncomfortable truth remains: most organisations are simply not ready for AI.

At TurningPoint Advisory, we have seen this story unfold. Executive teams are enthusiastic about predictive analytics or automating manual workflows—until they encounter the reality of their own data landscape. Fragmented systems. Siloed datasets. Information that is incomplete, inconsistent, or inaccessible. No AI model—regardless of its sophistication—can extract value from flawed foundations.

Without the correct data foundation, what you receive is not insight. It is noise.

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The Missing Ingredient: A Scalable Data Platform

Before businesses can realise the promise of AI, they need something far more fundamental: a scalable data platform.

Eight Key Fundamentals of a Strong Data Foundation:

1. Data Warehouse or Platform: A unified repository where all your business-critical data resides. This could be an Azure Data Lake or a cloud-based warehouse that aggregates data across your business systems (ERP, CRM, etc).

2. Architecture: The design of your data platform—cloud-native, scalable, and flexible enough to support both structured and unstructured data.

3. Data Governance: Ensuring the data is secure, accessible, and of high quality across all departments. A well-defined governance structure prevents errors and compliance issues.

4. Integration Tools: Tools such as Azure Data Factory enable the integration of diverse data sources—such as ERP systems, IoT data, and cloud databases—into a unified, automated pipeline, ensuring consistent data ingestion and refresh.

5. Storage Solutions: Cloud storage solutions such as Azure Data Lake Storage (ADLS), Amazon S3 or Google Cloud Storage provide scalable and flexible solutions to managing data.

6. Real-Time vs. Batch Processing: Flexibility for both types of processing. Real-time data flows enable applications such as live dashboards, while batch processing is useful for periodic reporting.

7. Data Modelling: A structured approach to transforming raw data into usable formats can achieved through the Medallion Data Model (bronze, silver, gold). The bronze layer captures raw data, the silver layer refines and enriches it, and the gold layer contains curated, business-ready information.

8. Compute Engine: Powerful compute solutions such as Databricks for advanced processing and modelling. This enables data transformations and advanced analytics such as machine learning and deep learning.

A mature data foundation enables the adoption of AI capabilities. With clean, connected, and well-governed data, organisations can unlock value through predictive analytics, intelligent automation, and real-time decision-making. AI becomes not just a concept, but a scalable, value-generating function within the business.

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How Do We Assess Technology Needs

At TurningPoint Advisory, we begin every transformation with a clear-eyed understanding of where the business is today, before recommending where it should be tomorrow.

During our engagement with a mid-sized circular economy client, we began with a needs assessment. This helped us identify the technology gaps in their current systems and align their business goals with a tech solution that could scale. We looked at their existing data sources—scattered across ERP systems, Excel files, and legacy tools—and defined the pathway for moving them to a consolidated data platform on Azure.

Our approach to assessing technology needs is structured, pragmatic, and business led. We do not begin with platforms or tools; we begin with purpose.

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Step 1: Current State Technology Assessment

We work closely with all stakeholders across business lifecycles to assess the business's current systems and data landscape. This includes:

• Identifying key systems and data sources (e.g., ERP, spreadsheets, legacy databases)

• Mapping pain points whether related to manual reporting, siloed data, or lack of visibility

• Evaluating infrastructure readiness for cloud-native architecture, automation, and analytics

• Understanding business priorities such as automated reporting, compliance, or customer growth

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Step 2: Capability & Maturity Mapping

Once we have attained a picture of the current state, we conduct a gap analysis across several core areas:

• Data architecture and storage

• Data integration and orchestration

• Analytics maturity

• Process automation potential

• AI readiness and use case feasibility

We benchmark our client's maturity against best practices and identify what is realistically achievable in the short term versus what requires foundational transformation.

With the waste recycling client, for example, we determined that they had strong operational data but lacked the governance, automation, and structure needed for analytics. Their data was available but not easily accessible or actionable.

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Step 3: Roadmap & Solution Blueprinting

We co-design a technology roadmap aligned to business value whether that is AI implementation, automation, or data democratisation. This includes:

• Recommending the optimal cloud architecture (in our client's case it was Azure with ADLS and Databricks)

• Outlining the data ingestion and transformation layers (the Medallion architecture model was utilised)

• Prioritising use cases that deliver value fast like daily sales reporting, financial dashboards, and customer insights

The transformation is modular and iterative. We do not drop in a monolithic platform and walk away. We build what the business can adopt, sustain, and grow with.

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Step 4: Build, Validate, and Deliver

Finally, we implement the foundational data infrastructure and deploy the first wave of high-value use cases. In this client’s case, that meant automating key financial reports, which previously took days to compile.  

The results were immediate: improved decision-making, reduced reporting friction, and a scalable platform to support future AI initiatives such as revenue and demand forecasting.

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Bridging the Gap Between AI Vision and Implementation

TurningPoint Advisory supports businesses in aligning their AI aspirations with practical execution. We assist businesses build scalable, future-ready data platforms tailored to their operational needs and strategic goals. Contact us to schedule a discovery session or learn more about how we support AI-enabled transformation.