Lakestack empowered a premier automotive company to drive 70% operational excellence

Customer overview

Our customer is a prominent SaaS provider offering comprehensive CRM solutions tailored for automotive showrooms. Their platform seamlessly manages both sales and workshop operations, serving a vast network of dealerships. Each showroom location generates a wealth of critical data from various systems, including:

• CRM: Leads, deals, customer information, and sales pipeline data.
• ERP: Parts inventory, invoicing details, and supplier data.
• Repair & workshop management: Service logs, repair histories, and technician assignments.
• Customer feedback & service history: Direct customer input and historical service interactions.
• Finance & compliance records: Transactional data and regulatory adherence information.

Industry and customer challenges

McKinsey reported that 70% of top-performing automotive businesses report challenges integrating data into AI systems. Specifically highlighting that fragmented, low‑quality, or poorly governed data remains the biggest barrier to meaningful AI deployment.

And our customer was facing similar challenges

1. Fragmented data

‍Data was scattered across numerous disparate systems and legacy platforms (each with different formats and schemas), making it difficult to gain a holistic view of their operations and customer behavior. This is a prevalent issue, as over 65% of automotive companies struggle with fragmented systems that block cross-functional data use. CRM, ERP, DMS, and customer feedback data were all siloed, preventing a 360° view of customers or vehicles.

2. Lack of unified visibility

‍There was no single, comprehensive view of the vehicle lifecycle, from acquisition and sale through service and repeat purchases, impeding strategic planning.

3. ZERO data automation

Manual processes for data ingestion and transformation were time-consuming, error-prone, and unsustainable given the increasing volume and velocity of data.

4. Manual reporting delays

‍The reliance on manual data extraction and report generation led to significant delays, preventing timely sales and service decisions.

5. Scalability issues

Scaling data insights and analytics across their expanding network of approx 50 showroom locations proved increasingly difficult and resource-intensive.

How we helped our customer

80% of automotive data is unstructured, which significantly complicates AI readiness due to fragmentation and poor standardization, making a data lake approach essential for actionable intelligence.

We introduced Lakestack to our customer, which is a no-code, AWS-native data lakehouse solution. Our platform offered a robust, scalable, and secure framework to unify their data, automate processes, and unlock advanced analytical capabilities. It unifies CRM, ERP, workshop, and service feedback data into a single source of truth, and is deployed in the customer’s AWS account, ensuring compliance, data sovereignty, and zero vendor lock-in.

1. Proof of Concept (PoC) validation

A PoC was delivered within six weeks, validating the core capabilities of data ingestion, secure storage, and dashboard functionalities. This rapid validation minimized risk and demonstrated early value.

2. Data unification

Raw data from 15 disparate source systems was ingested, normalized, enriched, and finally transformed into BI-ready datasets. AWS Lake Formation and AWS Glue Catalog were instrumental in managing metadata and permissions across these layers, providing a unified view of all data assets.

3. ETL automation

Lakestack's no-code capabilities allowed automation of ingestion pipelines using AWS Glue ETL, AWS Batch, and AWS Lambda. This enabled seamless handling of diverse data sources, including consuming data from CRM REST APIs, processing ERP database dumps, integrating workshop logs, and capturing customer feedback forms. Our solution supports both streaming and batch modes, ensuring comprehensive and automated data capture with minimal manual intervention.

4. Self-serve Analytics using NLP

‍Our solution empowers leaders with self-serve analytics by deploying Amazon Redshift and Athena for high-performance querying of the consolidated datasets. It seamlessly integrates with Amazon QuickSight to provide intuitive dashboards, offering showroom managers real-time insights into sales pipeline status, service KPIs, parts inventory alerts, and customer repeat-service forecasts. Crucially, our solution integrates with QuickSight Q enabled natural language processing (NLP)-driven queries, making data accessible and actionable for non-technical users.

5. Accelerated Data AI Readiness

By leveraging Amazon SageMaker, our solution facilitated the training of sophisticated machine learning models for predictive maintenance alerts, customer up-sell probabilities, and service parts demand forecasting. The outputs from these models were seamlessly integrated into the BI dashboards, providing actionable intelligence and enabling proactive decision making.

Success metrics

Through Lakestack, our customer transformed its complex data landscape into a powerful asset, driving innovation and maintaining their leadership in the automotive software industry.

1. 45% Faster data accessibility

By consolidating 15 disparate source systems into a unified data lake, the client drastically reduced manual effort and gained significantly faster access to critical information.

2. 70% Gain in operational efficiency

Automated reporting and data processing freed up the equivalent of 3 full-time employees per month, allowing staff to focus on higher-value activities. 

3. Improved decision-making

‍Real-time dashboards led to better parts stock optimization, moving from reactive orders to proactive inventory management.

4. Reduced vehicle downtime by ~30%

Predictive service forecasts and fewer emergency repairs, driven by ML insights, significantly improved vehicle uptime for their customers.

5. Customer retention increased by 15%

‍Intelligent service reminders and targeted upsell campaigns, powered by data-driven insights, enhanced customer satisfaction and loyalty.