Designing a Data Strategy and Roadmap for RAW Charging

RAW Charging is perfectly positioned within the fast-growing market of vehicle charging solutions for the hospitality, retail and leisure sectors.

Investors also recognised these are exciting times for RAW with their high-growth ambitions. Antin Infrastructure Partners came on board through a successful capital fundraise that will support a £250 million electric vehicle (“EV”) installation plan by the RAW leadership team.

Working with investors meant RAW needed to provide KPIs and metrics to sense-check their growth against agreed targets, whilst also seeking ways to improve operational efficiencies, and the associated management of costs and overheads, and identify new opportunities for value generation across the organisation.

The RAW leadership recognised that to support its plans for continued growth it needed a data strategy perfectly aligned with its ambitious business strategy. Their rapid growth ambitions would require more robust data solutions, data governance and data architecture to ensure scalability and sustainability of their operations.

Fortunately, RAW agreed that they should concentrate on the strategic foundations of data before investing in tactical technology and quick fixes.

But the challenge was prioritisation – where should they invest their resources in such a thriving and data-dependent business?

With data playing a key role across finance, marketing, sales and operations, Nick de Mestre (RAW CFO) reached out to Oakland to draw on their decades of data and process improvement expertise to help shape the vision and direction for data.

With the RAW business model so dependent on timely, accurate data it made sense that the priority was a greenfield data strategy.

Our approach at Oakland is never to drag out a lengthy strategy process but to maintain momentum and focus by swiftly delivering ‘value drops’ in fortnightly sprints.

This agile approach meant the final data strategy was delivered within six weeks and mapped out a roadmap for the next two years.

What were the stages of the Data Strategy formation process?

The approach we adopted for RAW was similar to the speed and framework we execute in most of our data strategy initiatives:

Discover

2 weeks

Define

2 weeks

Plan

2 weeks

What did we achieve?

Completed a current state assessment across existing capability and discovery of desired future.

Defined the strategy for data and technology and recommendations of required capabilities to support growth.

Built an actionable plan and business case to implement the data and technology strategy and deliver the required capabilities.

Why was this important?

Providing an understanding of where RAW is today and how data and technology could enable their future growth strategy.

Ensuring capabilities align to the growth strategy and provide required flexibility within a rapidly evolving market and scale up business.

Ensuring the strategy is converted to actionable, incremental components that meet required inflection points and drive continuous value.

How did we deliver?

With full-day workshops and focus sessions, engaging 10 stakeholders across RAW we: Conducted a vision discovery

• Assessed the data capabilities
• Reviewed current state architecture
• Assessed data quality

Informed by focus workshops and follow-up interviews with RAW stakeholders we:

• Completed a gap analysis
• Defined strategic pillars
• Defined to-be architecture
• Defined a high-level data model

Informed by further focus workshops we:

• Delivered a high-level roadmap
• Highlighted constraints and dependencies
• Evaluated value proposition and business case
• Provided detailed estimates for phase 1

The Oakland workshops and focus sessions with RAW stakeholder groups helped to gather vital insights for shaping the strategy, some of which are shared below:

Empowering automation and scale through improved data architecture and platforms

Because the RAW business had accelerated so fast, elements of the existing IT infrastructure showed signs of struggling to keep up and serve the business effectively.

One example, common to other fast-growth startups, was a dependence on tools such as Excel and low-cost web apps.

These tools can serve a small business in the early days but struggle when a company grows quickly, and cross-functional pressures for better-quality data emerge.

In the case of RAW, data was being collected, but often manually via Excel and other SaaS applications with no automation. The leadership team recognised more could be done to drive greater operational performance and deeper financial and operational performance insight.

The Oakland strategy team outlined a pragmatic and achievable roadmap for delivering a data architecture that would scale through automated ETL (data movement) and low-overhead tools to meet all user needs.

Focusing on improved data culture and core capabilities

The RAW data team reflected the initial growth size of the company and recognised it needed broader data expertise to deliver the future data roadmap. It had become too reliant on contractors and ad-hoc methods, which became key areas for improvement.

The vision we outlined was for a company-wide, skilled core of data capability supported through a streamlined in-house team. Recognising data’s critical role, we also aimed for a universal, company-wide recognition of data value within the data strategy.

Overcoming challenges like manual data handling, quality control, and reporting were identified as crucial for optimising the foundational elements that private equity investors look for in a mature portfolio organisation.

The drive for timely and consolidated insights

In any fast-growing business, gathering and maintaining insights becomes a challenge, particularly in the sector RAW operates where data is a key competitive advantage for pricing, customer experience and delivery operations.

When combined with the need to keep investors informed across ten key performance indicators, RAW recognised the need to evolve its legacy reporting into a more advanced forecasting and analytics strategy for its internal processes and external oversight.

The Oakland data strategy roadmap introduced plans for a more advanced analytics capability that would provide sustainable but swift, large-scale insight delivery.

Targeting Data Improvements via Strategic Capabilities and ‘Lighthouse Projects’

At Oakland, we believe executing a data and analytics strategy requires a company to achieve data milestones at pace whilst being responsive to shifting business needs.

To enable these aspirations, we prefer to drive change through two approaches:

Targeted (Lighthouse) Solutions

Lighthouse Projects Identify “narrow slice” strategic projects to:

• Drive value and momentum quickly
• Engage and excite the business
• De-risk the change

Strategic Capability

Foundation Development

Plan, build, and manage the changes required across people, process and technology to deliver against the companywide target capability.

Linking Data Improvement to Operations, Marketing and Sales Performance

When data becomes siloed, it can suffer from ‘patchy’ completeness, timeliness and consistency. It is also disrupted by complex, manual processes which cause business operations to slow down, driving up costs and disrupting the customer experience.

The data strategy roadmap at RAW was focused on removing silos and mapping out a central architecture that would directly drive improvements across:

• Opportunity and Sales Targeting
• Site Selection and Location Profiling
• Deployment Optimisation
• Charge Point Pricing and Utilisation
• Board KPI Reporting & Analytics

Some of the capabilities that will make the data strategy deployment a reality include:

1. Data Governance and Data Quality

Implement a framework for data governance and data quality at RAW.

Appoint Data Stewards and Owners for key data domains.

Template and create data standards, dictionaries and business glossaries.

Ongoing, SLT-led data quality monitoring and improvement.

2. Data and Analytics Technology Platform

Build a scalable, low overhead platform for data management & insights at RAW.

Design and implementation of Azurebased, cloud data and analytics platform.

Creation of conceptual and logical data models for RAW to underpin future data.

3. Data and Technology Organisation

Build a lean data function to ensure data skills available at effective cost.

Appointment of a Data Manager. Hiring of additional role(s).

Identification of key data responsibilities for existing RAW FTE.

Outsourcing of other key function(s) e.g., Platform support, Architecture.

4. Data Culture and Literacy Intervention(s)

Education, training materials and leadership for a data-driven culture at RAW.

SLT agreed principles and role modelling for data-driven culture.

Long term data SLT sponsor.

Data built into top level RAW strategic pillars / goals.

Training needs diagnostic, career pathing and role profiling for data.

“It’s been about setting the path forward and giving RAW a crystal clear and prioritised roadmap, based on aligned business objectives, delivering value for them and getting the right people, process and technology structures in place to support it.”

Rob Oakland – Commercial Director at Oakland (B Corp)