DMS data consolidation: 6 ways to get it right

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Your dealer network is possibly your single most important source of information about your customers. Getting the most from your dealer management system (DMS) enhances customer service and, ultimately, profitability.

But there are many pitfalls in consolidating DMS data, including:

  • Getting a ‘good’ DMS extract: timely, reliable, fault tolerant and secure
  • Variety of DMS to extract from
  • Nuances e.g. national differences
  • Handling changes
  • Development risk
  • Obtaining dealer network buy-in
  • Handling day-to-day issues
  • Gaining support from DMS software providers.

In this blog post we’ll share six ways in which our consultants minimise these risks and maximise the usefulness of our clients’ DMS data. Their aim is to provide clients with the exact data they need in the form they need it. If a dealer changes their DMS, or a software provider releases a new version, our DataHub service will handle it. Clients have complete control and transparency, driving increased sales, better customer care and improved parts management.

1. Have a clear view of the data required

Any endeavour benefits from clearly defined goals. In this context, you need visibility of the overall business goals and then to define the data needed to support them. Don’t waste effort collecting and processing data that is not required. This does assume that your access method allows the extensibility of the data sets at a future point – this is usually achievable for a reasonably low cost.

2. Go for quick wins

We find that creating a quick win helps to gain support for a project throughout the business. If planned correctly, it also minimises risk by acting as a test pilot. A quick win can be successfully created by:

  • Identifying a friendly subset of dealers
  • Identifying a region rather than the whole network
  • Focusing on a high value business requirement
  • Defining a narrow data footprint
  • Aiming for short timescales
  • Allowing the results time to bed in.

Our work with Honda is an example of going for a quick win. For our first project, we used a small set of dealers in the initial pilot and narrowed the data footprint to support a specific parts replenishment requirement. The project was piloted with Scottish dealers; Glasgow in particular was a useful location as it had several Honda dealers using different DMS. A two day visit to Glasgow allowed us to visit these different dealers and investigate the best approach to extract data from the different DMS types. Once an option had been chosen for a particular DMS there was no need to visit the site again: all dealers with this DMS will be accessible for this project and any future projects.

3. Ensure fit-for-purpose data

If access to all the data is not possible when profiling, ensure that the sample sets provided are covering boundary cases. Profile individual data sets, and then carry out unified profiling to ensure that there are no gaps or exceptional cases which need to be addressed. This will assist in identifying tasks such as data cleansing, enrichment of data (with reference data) and de-duplicating.

With our clients we typically carry out data cleansing, validating names, addresses, postcodes, telephone numbers, emails and vehicle IDs. We also classify the type of customer (e.g. retail, trade, owner, driver) and categorise the work carried out (e.g. service, repair, MOT, bodyshop, warranty). For all automotive projects we also apply de-duplication rules. Our consultants use a number of different fields (e.g. invoice number, dealer code, invoice amount) to check for duplication.

4. Consider sustainability

The need to get up-to-date information and customer insight is usually not a one-off. Dealers and manufacturers often require monthly or weekly trends, or wish to implement on-going marketing campaigns such as service reminders. Everything that the business does, both human and application based, relies on fit-for-purpose, timely and accurate consolidated data.

Data from the dealer network can be provided in a weekly, daily or near real-time basis. This requires an approach that facilitates on-going delivery, so the people, technology and processes have to be in place to support it. How are you going to identify that a dealer feed has failed or changed? How are you going to ensure the data from a range of DMS types is consistent and has the same rules applied? A technology framework will be necessary and this requires an appropriate level of budget. Whether it is an internal or external service, creating a sustainable framework is the foundation.

5. Embrace flux

We integrate hundreds of DMS and, at any one time, there are changes to data sets, changes to contacts, new dealers or a change of management. For our clients we put in place a motivated, responsive and helpful service to get through any issues quickly and with the least disruption for everyone.

With Honda, we find that a dealer network that enjoys this level of service is open and helpful in supporting the initiative for the long term.

6. Do it once

We often find that clients have implemented, over time, numerous projects or initiatives. This could be a new CRM, a parts replenishment solution or using a marketing agency for customer call backs. All will typically have had investment in some form of data collection, integration and cleansing. By the time you add up the effort and cost, it may be that the same data has been paid for many times over and by a number of budgets across the business. Many times we see data sets that are rich and which overlap with others found in the same manufacturer, even within the same department. Those sets of data will have cost money to create and are still costing the business funds to maintain.

Instead, we build projects for our clients that avoid silos of data. We collect the data once, get it into a fit-for-purpose state and then make it available for internal or partner use. Honda had previously attempted to integrate its dealer network and found itself supporting a number of software and service initiatives, with separate budgets for data extraction, quality and cleansing, customer insight and more. Our consultants put together a single service to consolidate the data from the entire network. Corporate then had complete on-going control over the data. They were able to share that data across multiple internal and external teams for much less than the combined cost of the previous initiatives.

Further reading

In our next blog post, we will share KPIs from our work with Honda, providing targets to aim for in your own data consolidation project.

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