ACH jako skupinový (kolaborativní) process

ACH je vynikající rámec pro spolupráci mezi analytiky. Vzájemné obohacování nápady pomáhá analytikům vyhnout se osobní zaujatosti a vytvářet více a lepší nápady. Tabulka může kombinovat příspěvky analytiků z různých prostředí. Když analytici nesouhlasí, může být skupinová matice použita pro zvýraznění přesné oblasti nesouhlasu. Následná diskuse se pak zaměří produktivně na konečný zdroj rozdílů. Je možné provést citlivostní analýzu s cílem zjistit, jak alternativní výklady důkazů nebo různých domněnek ovlivňují pravděpodobnost alternativních hypotéz.

This often helps resolve, or at least narrow down, areas of disagreement. It is also possible to go back and enter different ratings and see how any single change or set of changes affects the overall likelihood of the various hypotheses. In this way, it is possible to clearly identify which disagreements are important to resolve and which really aren\'t worth arguing about.

A collaborative ACH project can be implemented with a four-step process:

• Identify an agreed-upon set of data relevant to the topic; remember to include assumptions, logical deductions, the absence of data, and conclusions from other analyses.
• Convene a structured brainstorming session with a diverse group of analysts to identify all the potential hypotheses.
• Commission one analyst or a small group of analysts to work independently--and on their own time schedules--to load the data. Then invite all interested analysts to take part in the project by analyzing the data in their own Personal Matrix. This might take several days or weeks. This should provide independent validation of the key conclusions, and because they are working from their own desks instead of together, the likelihood of groupthink is minimized.
• Reconvene the larger group to assess the results of the working group. Use the Group Matrix to determine the sources of disagreement. Focus on what data emerges as most diagnostic, the most persuasive reasons for discounting hypotheses, the credibility of the data supporting the most likely hypotheses, and the most productive areas for future research or collection.

(If you want to know more about collaborating with ACH, read our article about the software\'s collaborative features.)

The next article about the ACH methodology explains a critical step: creating good hypotheses.