Cloverpop Launches Decision Management Solution

Decision-making platform provider Cloverpop today launched a cloud solution for managing and improving decisions across the enterprise that consists of three components: a system of record, analytics, and artificial intelligence (AI).

The system of record is akin to a CRM system, but for managing business decisions. It indicates what was decided and why, with a full context database including who was involved, what information and choices were considered, and a comparison of results with expectations. It also features tagging, searching, and filtering capabilities.

“Decisions are very consistent data objects—there’s a group of people that were involved, a group of choices that were considered, information that was considered at the time that a decision was made, expectations about what’s going to happen, and the actual results. The idea of having a decision object stored and tracked in a database is the fundamental underlying new concept,” says Erik Larson, Cloverpop’s founder and CEO.

The analytics portion has four key elements, the first of which encompasses an organization’s decision goals. “We’re able to look at the actual decisions that are being made across an organization and the goals that those decisions impact, and we can roll that up and read back to executives what the actual goals of the organization are according to the decisions that are being made,” Larson explains.

The second key analytics element involves the speed of and degree of participation in decision making. “If the pace of decisions is going down, you can know for sure that the rate of innovation is going down as well,” Larson says. The software can also determine which employees are involved in the most decisions and, crucially, which aren’t. “If a manager can see this person hasn’t been participating, they can take action to do something about a very important part of their job.”

The third key analytics element focuses on decision quality. High-quality decision making, for example, usually involves five to six people, at least four alternatives, and three to five goals impacted. “We can show the quality of the decisions based on all of the data we have in the system, and we can do that by team,” Larson says.

Fourth, the platform’s analytics can measure the effect of inclusion on decision making by looking at factors such as the gender and age of people involved in decisions. 

As for its AI capabilities, the solution includes an AI engine that can offer suggestions on how to improve decision making. “When someone frames up a decision the AI engine looks at all the inputs they provided and makes a recommendation for the easiest way for them to improve the quality,” Larson says. For example, he says, it might suggest inviting two additional people to weigh in on the decision. “The goal is to reduce the amount of work to get the biggest benefit, so if you just invite two more people and that gives you a huge chunk of improved quality and speed, it’s worth doing,” he adds.

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