Six months ago, when we began building our new solution, Ignite Network Intelligence, our goal wasn’t to create just another “innovative” data platform in a crowded market. We wanted to develop something that transforms how OEMs manage their networks - a massive leap forward in OEM business intelligence.
In a sector overflowing with reports, dashboards, and KPIs, we know OEMs don’t need more scattered data muddying the waters. They need intelligence: clear, real-time insights that drive confident decisions, better outcomes, and long-term value.
That’s exactly what Ignite Network Intelligence (INI) delivers - a smarter approach to OEM analytics and automotive manufacturing solutions.
I’m Tiarnán, Senior Data Analyst at RWA Automotive. Over the past four years, I’ve played a key role in developing our automotive retail solutions, and I was particularly excited to help shape our first used car business intelligence tool for the OEM market.
For a data analyst, building a project from the ground up is one of the most rewarding challenges. It brings the problem-solving nature of the role to the forefront. Most projects begin with a hunch - that a specific dataset could offer real value to a particular market. In this case, we drew on RWA Automotive’s deep experience supporting some of the UK’s largest automotive retailers, especially in the used car space.
We understand both the opportunities and the constraints in this sector. Time and again, we’ve seen how difficult it is for large dealerships to source used franchised stock, challenges that we believe are mirrored across the wider OEM network, including at manufacturer head office level.
Before diving into development, we recognised that OEMs were overwhelmed by fragmented data streams. But amid the noise, one gap stood out: a lack of clear, centralised visibility into used car performance across the network.
We were confident we could create an OEM business intelligence solution to transform how Used Car Network Managers evaluate network health. But to do that, we needed something that didn’t yet exist: a purpose-built dataset designed specifically for this challenge.
Our CEO, John Hogan, previously met the team at Solera | cap hpi at a networking event and reached out to explore a collaboration for the OEM market. We travelled to the UK to present our concept: to provide OEMs with a comprehensive view of the health of their used car networks. The team at Solera cap hpi quickly grasped our idea and recognised that they had the necessary data to make this idea a reality.
As a result, Solera cap hpi became our data supplier for this project, and the now finished OEM solution is powered by their data.
After establishing our partnership, we focused on building a foundation for our project. Our initial iteration was far from the final solution we have today. We began by analysing the available data, cleaning and segmenting it, and then creating visualisations to make the insights more accessible and engaging. From there, we collaborated with OEMs and consultants to address the challenges faced by manufacturers.
Our goal was to create a tool that was not only powerful but also intuitive, flexible, and reliable. Most importantly, it needed to meet the real-world demands of OEMs navigating the complexities of today’s automotive manufacturing landscape. This involved numerous phone calls and office visits to identify the root problems that our solution could help solve for OEMs. Additionally, we aimed to develop actionable insights that would empower OEMs to improve their operations within the network.
I was actively involved in every stage of developing the solution, from brainstorming names to participating in the first live demo. This project was entirely unique and centred on adding value for the end OEM user. That said, there are essential steps in any data project that a Data Analyst like me typically follows:
As a Tableau developer, I specialise in creating data visualisations that drive actionable insights for businesses. To achieve this, it is crucial to have a consistent and accurate flow of data. Throughout this project, we encountered various data sets provided by cap hpi. We needed to collaborate effectively to identify which elements would add value to our reports and which would introduce unnecessary complexity..
After we determined which data sets were necessary, I cleaned the data. This process involved analysing the data for errors, duplicates, and inconsistencies. Validating the data through consistency checks, cross-referencing, and applying various logical rules and statistical methods requires significant time and attention. This is one of the most time-consuming aspects of development because if the data isn't cleaned properly, issues can arise quickly and can be difficult and time-consuming to resolve.
The next step involves analysing the data. I begin by examining it in detail to identify potential segments, trends, or insights, and to assess how easily it can be interpreted. Before reaching this stage, I have a general understanding of what I aim to build, so my focus is on determining whether this can be developed using the available data. Throughout this phase, I collaborated closely with John Hogan, our CEO, and Laura Murphy, our Product Lead, to ensure we stayed aligned with the project brief.
Then I started to build out the system with Laura. Data is just numbers unless you can do something with it, which is why visualisation is so important. We created multiple dashboards to address the challenges faced by OEMs and developed daily reports that provided actionable solutions, which we then tested.
At this stage, I realised that our reports were too cluttered. Last December (2024), Laura and I sat down together and agreed that we needed to simplify the information for the end user. We also recognised the need for additional data sets to ensure we presented a complete picture. The team at cap hpi played a crucial role in securing this additional data, which was essential for bringing our final solution to life. With the new data, we had to go through another round of cleaning, analysis, and report creation. Although this was a bit daunting at the time, it turned out to be the right decision, as the finished solution is polished and easily actionable for the end user.
No project of this scale can be accomplished alone! The excitement of this solution came not only from constructing something from the ground up but also from collaborating with others. When creating something new, everything is experimental and open to discussion. Having a trusted partner like cap hpi made the process enjoyable for everyone involved.
Our goal for this solution was to provide insights that would prompt action for OEMs. So, what does this mean in practice?
It means that OEMs no longer have to wait for end-of-month reports to make changes. Ignite Network Intelligence offers real-time visibility across their networks, unlocking a new level of OEM analytics. Whether focusing on a specific dealer group or examining national trends, this tool makes the process fast, clear, and actionable.
Ignite Network Intelligence identifies opportunities for both the network dealer and the OEM, unlocking millions of potential profits.
If you’re interested in seeing a demo of Ignite Network Intelligence, get in touch.
And if you'd like to explore our wider portfolio of OEM analytics and automotive manufacturing solutions, please visit this page.
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