You need proper data and model governance
Ideally, all data is correct and usable from the source, but even in that case, it is rarely ready to be shown immediately. That is why we must process it through other tools. We do that to develop insights through analytics and to visualize them in an appealing and digestible way. However, it does not come without challenges. Let us look at how you can tackle at least four of them.
Increase the risk of making errors
The challenge in most cases is that data is extracted in free form to Excel, allowing the user to make any modification (s)he desires. With every additional modification, the risk increases. Hence, you can build a data model to prevent this and ensure that data is always processed similarly. However, the model should be documented and agreed upon with stakeholders and clearly stated if a true ad hoc analysis is needed. This way, you can ensure alignment with stakeholders about the risk of errors.
The difficulty of tracking data to the source
For every single number you show to your stakeholders, you must be able to state what the source is. In addition, you should be able to show what processing has been done to it to arrive at its current form. If you are looking at inventory numbers, for instance, you should be able to go to the warehouse and point at the physical item. One way to ensure you can do this is to create a simple flow- or process chart that shows the flow of data from sources to uses.
The complications of having a dialogue about the numbers
It must be transparent to all stakeholders where numbers come from and how they have been processed. The easiest way to do this is by having an audit trail from aggregated numbers to the source. You cannot do this if all you have is flat data in Excel (or even PDF). In this case, each sheet must contain a link that leads the user to the source data. Doing this makes your dialogue with stakeholders straightforward when looking into what and why something happened.
The slow data-to-insights process
Your stakeholders want their numbers to be relevant for decision-making as soon as possible. However, every step you take to prepare the data will slow down the process. The best way to go around this is to build automatic interfaces between systems. If all you have is Excel, this is clearly a challenge, although not impossible, if you create macros that can replicate your manual steps.
Regardless of how you choose to solve these challenges, it is crucial that you use a proper data model and adhere to strict governance around using it. This might feel limiting to the flexibility of how you can use the data. However, if you clearly distinguish between standard and ad hoc analysis, you can at least set the right expectations with your stakeholders.