For years, we’ve been hearing people in the tech sector call data “the new oil.” But at this point, data—which there is an incomprehensible amount of now—is only as valuable as what you do with it.
Indeed, the ability to take action on all the information you have access to is the new competitive advantage. For that reason, it’s no surprise that more and more businesses are turning to tools such as action-based dashboards.
Megan Connell is seeing it firsthand. Her company, Praxis Metrics, is increasingly setting organizations up with such dashboards, because business leaders are starting to understand that their companies or teams can be “just one data-driven action or decision away from exponential growth,” she said.
Before you can build action-based dashboards, however, it’s important to recognize and establish what Megan considers four key foundational processes.
1 – Metrics mapping
If 80% of your results are caused by 20% of your actions—otherwise known as the 80-20 rule—you don’t need to build 100 different key performance indicators (KPIs) when only 20 of them could deliver the outcomes you are looking for.
Before building a dashboard, identify your business goals. Then create a dashboard—or dashboards—that takes into account the metrics that could potentially serve as the type that will help you achieve those goals.
A good example can be found with a raw goods company Praxis works with. One of its goals was to reduce the amount of yogurt it was making that was going to waste. So, it set up a dashboard and discovered that production decisions were being made based on annual consumption averages, instead of when exactly people were more inclined to buy yogurt.
Armed with the information it needed to reverse a troubling trend, the company adjusted its production schedule accordingly, and in turn experienced significant savings.
2 – Scientific method
Once you’ve identified what you think are your key metrics, it’s not only important to test your hypotheses, but to test variables—and to document everything along the way.
Having that insight provides the kind of context needed to properly analyze and interpret the results, because you now possess what Megan calls “the full picture.”
Data science and machine learning tools—especially ones integrated within a modern BI platform such as Domo’s—can undoubtedly help, as they take the kinds of specific actions (from Clustering to Outlier Detection) that can predict business outcomes more accurately.
“When you know all the variables and inputs that created the outcomes,” Megan said, “you can then accept or reject your hypothesis.”
3 – KPI prioritization
Testing allows you to create a list of KPIs. But just as not all business intelligence platforms are equal, not all KPIs are, either.
Megan recommends listing out your KPIs in a matrix, then labeling each one as “nice to know” or “need to know.” In other words, what’s the highest priority, and what’s the lowest priority?
Most marketing teams want to track CPC (cost per click), impressions, and CTR (click-thru rate). But other teams might be more interested in KPIs such as LTV (lifetime value of a customer). Once your particular list is in order, start at the top and work your way down.
4 – Cadence of accountability
It’s great to create a list of KPIs in order of priority, but that list means nothing if it can’t be executed upon.
You need to put someone in charge of each KPI, Megan said. Then you need to define how frequently each KPI should be monitored, define the hypothesis, and create a rhythm of frequent and short meetings for accountability.
It’s also a good idea to create a scorecard “so people know where they stand,” she said.
To see how Domo helps companies build interactive, customizable dashboards that enable teams to take informed, decisive action, click here.
