Why Aggregating Data is Difficult (and What To Do About It!)

Collecting and aggregating data is a crucial element in running a successful franchise. Clear and actionable data is required for franchisors to track franchisee performance, identify areas for improvement, and make informed business decisions. Unfortunately, the nature of franchising makes it difficult to effectively organize and interpret data across the business for a variety of reasons, and as a result, franchises are sometimes left in the dark on exactly where their business stands, and what they could be doing better.

Lets look at why aggregating data is difficult in franchising, and what can be done about it:

1. Lack of a Clear Data Strategy

When a franchise business is first created, it is crucially important for the business to develop a concise strategy around how they will gather, house, and interpret business data. This will set the stage for the practices that are put into place when additional franchise locations are launched. Most of the time, franchisors are not carefully considering their data strategy for 3,5, and 10 years into the future, and this can lead to big issues when changes need to be made later. Franchises must make their data a priority from day 1 to avoid these headaches down the line.

2. Difficult Governance

It can be difficult to mandate that specific data implementation & collection processes are adhered to across the franchise network. This is especially difficult if a clear data strategy wasn’t defined from the beginning, and then franchisees must learn new programs, platforms, and processes. The best way to avoid issues here is to define a clear data strategy from the start (see #1), and provide franchisees with as much support and training to understand how to not only abide by these processes, but the benefit in doing so.

3. Data Fragmentation

Data fragmentation can be a big problem in franchise systems. With many different platforms being used, It’s common for similar data points to reside in different data systems. Once the data points have been separated, it can be difficult or impossible to link the data sets together again. For example, customer “John Smith” may have contact info in one system, and purchase history in another one, and it’s not guaranteed that there is a common data point to link the contact info and purchase history together. Thus, the data loses much of its value, because there is little context to go along with it. Minimize platforms used, adhere to a clear data strategy, and assign common “IDs” or other data points across data sets to ensure they can be linked together.

4. Slower to Innovate

Jokes aside, franchising has been historically slower to innovate at times. This is in part due to the general structure of franchising. In a somewhat decentralized structure, making sweeping changes across the business can be difficult for a variety of reasons (see points 1-3). Because of this, rolling out innovations in how data is captured and processed is a complex and challenging task, often resulting in franchises being left behind on the latest technologies. Franchisors should be thinking about a centralized approach in capturing their franchise network data from the start, so that they are primed to innovate more easily down the road. If points 1-3 are taken into account, innovation is much more likely to follow, making the data collection process much easier.  

In conclusion, aggregating data in a franchise can be challenging due to a variety of factors, including data fragmentation & governance issues . However, with the right tools and strategies, it is possible to effectively aggregate data and use it to drive the success of the franchise. Businesses like Transitiv are also working to help solve this challenge by expediting the data collection and aggregation process, and in addition, providing insights and recommendations based on that data to improve franchise performance across the network. To learn more, visit Transitiv.io

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