Data-based decisions. It is a phrase that has become dull with overuse. It even suggests that choices are made obvious if you have the right data.
Data-based decisions seems to ignore the fact that decisions, for the most part, are still made by people — people who have colleagues and bosses and customers and stakeholders to balance. Results shown in data often run counter to long-held assumptions or exclude important, unmeasured factors. Data-based decisions aren’t make in a vacuum; they are made in our social, political workplaces.
Perhaps we’d be better of focusing on a similar phrase: data-informed discussions. Before a decision can be made, there is a discussion. Data can, and should, influence these discussion. It is left to humans to sort out options, weight outside factors, and evaluate risks that are not evident in the data.
Consider any discussion you’ve had recently that should be informed by data…a political discussion about climate change; optimization of your marketing tactics; education options for your children. Why is it so hard to bring data into these discussions in ways that enlighten and encourage smart decisions?
A few of the common problems include:
- Misalignment about the nature of the subject, the decision to be made, or the meaning of concepts;
- Failing to make assumptions explicit;
- Different conceptual models of how the world works;
- Different sources of information, leading to conflicting results.
Ideally, these issues would be addressed ahead of a discussion. What if every difficult discussion could be framed by data so the dialogue could focus on the most important things?
A couple years ago we tried to answer that question for one specific discussion that happens all the time. The tool we created — inelegantly called the “Valuation Analyzer” — facilitates discussions between start-up founders and investors. The debate centers around financial projects, value of the company, and the ultimate payback for equity holders. Our tool is intended to get these two parties on the same page.
Here what it looks like:
The Valuation Analyzer provides common and explicit ground rules for estimating the future value of a company. Users can define a small set of assumptions and instantly see how those inputs impact valuation and return on investment.
Each of the items underlined in blue can be adjusted by clicking and dragging left or right (thanks to Bret Victor’s Tangle library for this input mechanism). Most of the important assumptions are in the lower half of the screen: Do you want to calculate valuation based on revenue or EBITDA? What multiple will you use? How will revenue/EBITDA change over the coming years?
As you make adjustments, the values and visualization updates instantly. As a special treat, we added a “founder ownership” option (see the button under the title) to answer the most urgent question for any start-up founder: what’s my potential financial outcome?
Finally, a scenario can be saved and shared. The save button will keep all the assumptions you’ve entered and create a unique URL for that scenario.
This tool sets the parameters for a productive data conversation. The dynamic sentence across the top explains the meaning of the content. The assumptions are flexible, obvious, and explicitly stated. Two people sitting across a table can focus on the important values that impact equity owners’ outcomes. The discussion can happen in real time and the results can be saved.
It would be impossible to have a data discussion tool for every situation — there are too many unique circumstance. Nevertheless, this tool provides a model for those opportunities when a discussion happens time and again.
At Juice, we’ve found that organizations are so caught up in the race to capture and analyze data that they’re rushing past the most critical component – the end user. The best data in the world is useless if the everyday decision maker can’t understand it and interact with it. We’ve created a solution. Juicebox [www.juiceanalytics.com] delivers a more thoughtful approach to data visualization. We think about data conversations, not just presentations. Instead of just presenting data, we create more ways for people to interact with, socialize, and act on the data.