Anyone who wants to promote innovations in start-ups should establish basic rules for the way autonomous start-up teams work. This is the only way to protect the parent organization from harm and ensure a successful one Innovation enrich.
- Turn old-style models upside down
- Evaluate data
- If it is difficult to understand the data
- Where does the data come from?
- If the facts are not accepted by everyone
- Why discussions about data are time-consuming
- Disguise tactics for fear
- Experiencing instead of deciding?
- When the strike exchange becomes a ritual
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Turn old-style models upside down
I would first like to describe a typical meeting at one of my client companies, a large corporation. The management met to decidewhat features should be included in the next version of their product.
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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.
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?
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Worse still, no one could say for sure which ones customers had participated in the experiment. Different teams had been responsible for the implementation and so 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 heard then I aufmerksam and assumed that the meeting was over. Since there were no facts that were accepted by everyone and were used for decision-making, mine had Opinions according to nobody a tangible reason to plead for a specific course of action. But far from it.
Why discussions about data are time-consuming
Each department interpreted the data in their own way and championed their position. In the end, another fell decision, but not on the basis of solid data. The executive in charge of the session was forced to use the most plausible argument as a guide.
I thought it was a waste of time to give so much time to discussing the data, because the arguments that won the race in the end would be there at the beginning clear and be able to make clear.
Disguise tactics for fear
Obviously, everyone was afraid of being bulldozed by another team, and therefore had a disguise tactics. What a waste!
Such meetings had the data-driven decision-making and experimentation within the Company discredited, and with good reason. The data warehouse team created 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.
“Performing an experiment” was a coded one for them message; in plain language this meant that a difficult decision was put off. These meetings gave management a chronic headache.
When the strike exchange becomes a ritual
Your meetings after old Pattern, which focused on product prioritization, might have been little more than a verbal exchange of blows, but at least they understood what was at stake.
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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