Business Management is an important part of Network Development function and it largely relies on the financial and operational data submitted by dealers on a month to month basis. This data is used for extensive analysis and detailed reports with the goal of improving the overall strength of a company’s dealer network.

Business Management systems are commonly used by the automotive industry however, it is not a standard practice in all organizations globally. Also, many other industries like Powersports, Transportation and Equipment industries use similar systems sporadically – The key challenge is their ability to collect reliable data from dealers.

Challenges Faced Gathering Quality Data

There are several reasons why dealer performance data is either not easily available or subject to quality issues. Some of the key challenges include

Lack of contractual commitment by dealers Many companies operating in smaller markets do not have requirements to ensure that the dealers provide monthly performance data. The lack of a contractual requirement makes it difficult to ask an independent dealership to provide reliable data.

Mixed operations at a dealership A typical dealer will sell products and services for more than one company. Many automotive and in some cases, Powersports and Equipment companies prefer and appoint exclusive dealers, but most dealers have multiple operations. This makes it difficult for dealers to generate data that can be easily compared across different dealerships. For example, a dealership carrying only one car brand will have different income / expense and profitability measures compared to a dealer selling multiple brands and having additional business on its books.

Lack of correct mappings for data export Dealers will implement a “mapping” process that maps their internal financial data from their internal DMS / Accounting systems to the account / input data required by OEMs. Usually, they generate an extract file from their internal system in TXT / XML format. Incorrect mapping will result in inaccurate data.

Inexperienced accountant / controller The data export process does require an efficient understanding of the company’s requirements in terms of specifics of accounting and reporting. For example, some of the requirements could be reporting performance bonus, expense distribution, calculations of working capital requirement, etc. A new controller may not have a clear understanding of how to implement such requirements and that would lead to inaccurate data from a company perspective.

Staff turnover As it relates to the above. Dealerships experience significant turnover in controllers and accountants, which results in having to hire new personnel who may not have the right level of experience.

Measures to Improve Data Quality

Despite the problems previously mentioned, companies can take certain measures to improve data quality. Companies along with their Business Management service providers should be able to implement the following



  • Strong data validation processes
    Strong data validation routines / algorithms are a must for any company. These data validations can prevent some egregious errors from coming into the system. Data validations should also be able to validate data ranges (not only for raw data but also for ratios and comparisons) and address the overall consistency of data provided. For instance, Optimum Info’s system includes configurable rules for data validations.
  • Flexible Configuration
    It should be possible to easily manage changes to the data validation configurations. As requirements evolve or as specific attention is focused around varying areas of the reporting, the data validation algorithms need to be adjusted and fine-tuned. It should not require time nor money to program these changes, as its delays will impact data quality.
  • Efficient Accounting Manual and Guidelines
    Companies have to develop and make available structured accounting manuals and include additional guidelines for those items that require special treatment. Accounting manuals would also need to be made easily available and should be easy to use. If this information is not readily available, it will not be used and can therefore affect data quality.
  • Review / Plausibility Checks of each submission
    Finally, after the controllers have submitted their data, there should be a team that reviews the submissions and performs a plausibility check. These checks are required to identify data quality issues even if the input had come through without errors. Dealers may submit a statement with “warnings” – in which case the input is accepted but the dealers are notified of the need to improve on it. The review team then decides to either escalate the issue, follow up with the dealer or accept those warnings / alerts and include the dealer for later calculations and reporting.
  • Requiring comments on all critical validations
    Many systems are not set to require a dealer to comment on critical data validations, but this is a very important step in improving data quality. Such requirement forces controllers to review their data again and also provide the company with additional information and valuable insight, which is very useful not only for the review team but also the OEM.
  • Submission Checks and Follow up with the Review Team
    It is equally important that the OEM maintains up to date records of the controller and office manager contacts in case the review team does have follow up questions. Making sure that the dealership controllers are reachable is another way the data validation can be improved, pulling the correct numbers from personnel who are involved in submitting the statements and respective data.
  • Incentivize accurate & timely submissions
    A program built around the idea of incentivizing controllers with something as simple as a recognition or a small ticket item helps raise the data quality at submission. Controllers who delay submission until the deadline are more likely to overlook inaccurate data in efforts to submit the statement. This becomes a bigger issue when such controllers have similar deadlines from other brands in their organization and they do not have an opportunity to follow up with the team involved in the review mentioned above. A scoring system that checks for early submission and a few high priority accounts/KPIs should be implemented to incentivize controllers to prioritize the early and accurate submission of the statement.

    The above measures do take time to implement and would also require continuous efforts to sustain but will certainly lead to significant improvement in data quality.

Arvind Verma

CEO & President
Solutions

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