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How can we bridge the data story gap in commercial forecasts?

I have worked in project management in the engineering industry for many years. Usually, I compare the commercial tasks of this job to being a translator: we translate real-world events into numbers, mainly in the form of a commercial forecast. 👩🏻‍💻

Attending project meetings was essential in order to scan the conversation for commercial relevance. 👀

An example: If someone said in the team meeting, that the issuance of a purchase order to a supplier would take some more time than initially planned, the project managers would be keen to understand the impact for the project time line. And if that timeline was off, this might lead to a shift in the booking of revenue & gross profit and worst case to having to book a risk contingency for delivery delay as one had to pay penalties 💵 to the customer.

Hence, tiny remarks could potentially result in major discussion on top management level due to the translation of the information into the commercial matrix. 🧑🏼‍💻

Once every project submits their numbers, they are aggregated on every layer of the organization. This way the data – but not necessarily the story behind the data – travels up the chain of command. At each higher level of the organizations, the business controller may see the red flags 🚩 on the “Excel tapestry”, as they see e.g. severe drops in gross profits or major changes in the forecast compared to the one from last month, but they do not know the why’s, how’s and when’s by just looking at the table. The story remains in the dark. 🤷🏻‍♀️

What happens is a constant communication back and forth🔙🔛🔝🔚🔜 between the controllers on different levels (depending on how big the impact is), the originator of the data and possibly the individual project members. Meanwhile there are various other strings of conversations ongoing, if the initial problem has big enough of an impact. 🤯

This absorbs a lot of energy in an organization. (And we have not even touched the topic of data quality, yet.)

How can we report the qualitative data along with the quantitative information? Obviously, we do not want to shut down the human interaction within the organizational layers. I just believe that we could use it for a more important conversation: How can we support? What can we learn? What can we do better? 👏🏻

What do you think? How can we bridge the gap in the data story❓

(picture source: Adobe Stock)

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