Harnessing the power of data to optimize operations and enhance decision-making for a leading connected vehicles company in North America

Challenge

Connected Vehicles, a client subsidiary, provides a widely used connected vehicle platform in North America. It offers a range of safety, security, and convenience services, including automatic crash notification, roadside assistance, remote vehicle control, stolen vehicle recovery, navigation, and smart home integration.

With over 12 million active vehicles on the road, the client is continuously seeking to expand its customer base and service offerings, aiming to provide seamless and interactive experiences for both customers and merchants.

To achieve data-driven decision-making, it is crucial to have access to the right data at the right time. The effectiveness of large-scale strategic and modernization programs is heavily reliant on the quality, availability, and recency of data. However, two major challenges hindered their ability to harness data for business growth. One, the client’s enterprise strategy lacked a comprehensive understanding of data availability, leading to inefficiencies and difficulties in making informed decisions, and two, a significant portion of their resources, approximately 20-25%, was devoted to data validation, diverting valuable time and expertise away from growth initiatives.

Solution

To gain a comprehensive understanding of the client’s current data management strategy and practices, we conducted discovery sessions and workshops. These sessions were guided by Marlabs’ design thinking framework and involved separate discussions with stakeholders to assess the current state of data management, its alignment with organizational goals, and identify any gaps or inconsistencies across teams. Additionally, we conducted a series of interviews with data producers and consumers across the organization, which served as the foundation for redefining the data management strategy. The outcome of these efforts was a roadmap outlining the client’s desired future state, including a detailed list of actionable gaps in the areas of People, Process, Technology, and Data. It also revealed the need for a foundational enterprise architecture that would act as a central hub for aggregating data from various data providers and distributing it to all relevant consumers.

To implement this significant change, we began with small, incremental steps to ensure that all stakeholder concerns were addressed while prioritizing scalability. Our approach involved:

• Configured data quality parameters to address data trust concerns
• Designed and implemented a data governance process to control the data
• Refined business expectations on use case delivery through real-time data feeds
• Articulated change management process aimed at providing finer control over processes between data providers, data processors, and data consumers
• Designed and presented a plan for periodic data maturity measurement and improvement goals

Results

The interventions created by our team enhanced the value realization with immediate reduction in friction between data providers and consumers. This was largely enabled by identifying business process gaps that were interdependent and disconnected due to disparate systems. Organizational-level KRAs were defined that could be monitored and measured, thereby allowing remedial action for improvement.

With 60% of the data feed becoming near-real-time and a redefined customer segmentation, teams were able to make better decisions, improve customer experience and reduce customer churn.

We also defined new AI models during the first quarter of the engagement that allowed the client to monitor ROI on AI use cases.

Impact

• Unified data across departments and 5 on-premise solutions into a single source of truth in one cloud-managed solution
• Reduced reporting time and improved data accuracy
• Business use case delivery cycle reduced from 3 weeks to 1 week
• A ~36% saving in operational costs by discontinuing redundant technology licenses
• Enabled real-time decision-making with predictive insights
• Increased operational efficiency through automation
• Strengthened data governance and compliance across the organization