From the author:
Turn old-style models upside down
The focus of the classic recommendations on internal innovation is the protection of the startup from the parent organization. This model should be turned upside down.
I would like to start with a typical meeting with one of my clients undertake describe, a large corporation. The management met to decide which features the next version of their product should contain.
As the company benefited from being data-driven, it had carried out a pricing experiment. The first half of the meeting was devoted to the analysis of the data obtained.
The problem was that you could not agree on what the data meant. Numerous custom reports had been created for the meeting, and the data warehouse team was also present.
If it is difficult to understand the data
When it was asked to explain exactly the individual columns of the spreadsheet, it turned out that no one knew exactly how to get on the numbers.
We were only able to take a look at the gross sales of the product at various prices, broken down by quarter and customer segment. There were tons of data that were difficult to understand.
Where does the data come from?
Even worse, nobody could say with certainty which customers had participated in the experiment. Several teams had been responsible for implementation, and parts of the product had been updated at different times.
The entire process had taken months and by now some of the parties had been transferred to other divisions. You should now be able to pinpoint the issues in such a situation:
If the facts are not accepted by everyone
The use of facade metrics instead of action-oriented parameters, too long a cycle time, the use of large batch sizes, an unclear growth hypothesis, a weak build-up of the experiment, a lack of self-responsibility of the team and consequently low learning progress.
I listened attentively at that time, and I assumed the meeting would be over. Because there were no facts accepted by all and used for decision-making, no one had any real reason to argue for a particular course of action. But far from it.
Why discussions about data are time-consuming
Each department interpreted the data in its own sense and strengthened its position. In the end there was still a decision, but not on the basis of hard and hard data. The management, entrusted with the management of the meeting, was compelled to orientate itself on the most obvious argument.
I thought it was a waste of time to give the discussion about the data so much time, because the arguments that made the race at the end could have been made clear at the beginning.
Disguise tactics for fear
Obviously, everyone was afraid of being bulldozed by another team, and therefore had a disguise tactics. What a waste!
Such discussions had discredited the data-driven decision-making and experimentation within the company, and for good reason. The data warehouse team produced reports that no one read or understood.
Experiencing instead of deciding?
The project teams felt that the experiments were a waste of time because product features should be inserted half way, which was rarely good.
“Doing an experiment” was an encrypted message for her; in plain language that meant that a difficult decision was put on the back burner. The management team had chronic headaches from these meetings.
When the strike exchange becomes a ritual
Their old-fashioned sessions, prioritizing products, might have been little more than a verbal exchange, but at least they understood what it was all about.
Now it had become a ritual that involved complex computer operations, did not produce a clear result, and ended with a verbal beat.
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