10 tips for getting started with decision intelligence
For organizations looking to move beyond stale reports, decision intelligence holds promise, giving them the ability to process large amounts of data with a sophisticated mix of tools such as artificial intelligence and machine learning to transform data dashboards and business analytics into more comprehensive decision support platforms.
Successful decision intelligence strategies, however, require an understanding of how organizational decisions are made, as well as a commitment to evaluate outcomes and manage and improve the decision-making process with feedback.
“It’s not a technology,” says Gartner analyst Erick Brethenoux. “It’s a discipline made of many different technologies.”
Decision intelligence is one of the top strategic technology trends for 2022, according to the analyst firm, with more than a third of large organizations expected to be practicing the discipline by 2023.
The trend is brewing at a time when organizations need to make decisions faster than ever — and at a scale not yet seen. Decision intelligence helps provide an automated way to make decisions, which in turn can help companies stay competitive and meet market demands, Brethenoux says.
But that takes a deep understanding of the decision-making process, the risks and rewards of each decision, the acceptable margin of error, and the ability to figure how confident you should be in any decision offered by your automated decision processes.
Here are some tips to help you do all of that.
It helps to start with a process that is extremely well-defined, low-risk, and has a large collection of examples. Many companies have such processes already in place, and not all of them are fully automated yet.
Companies too busy with the day-to-day might not notice that they’re missing these opportunities, says Ray Wang, principal analyst and founder at Constellation Research. “Then they start wondering why competitors are doing better but by the time they’re doing that, it’s too late.”
Even when a process has already been automated, adding more factors to the decision engine may improve accuracy, he says. “The more attributes you have, the more likely those things haven’t been correlated,” he says.
For example, a risk scoring decision might be improved by considering the time of day, or the user’s location.
The key takeaway, though, is that decision intelligence isn’t a once-and-done process. You must continually tweak your approach based on feedback.
The more often a process is repeated, and the clearer the results, the more opportunities a company will have to improve it.
LexisNexis, for example, uses its ThreatMetrix product to make 300 million fraud-related decisions a day, but the decision’s aren’t 100% perfect.
“We are in the spectrum of making many decisions across a huge dataset that are not life-threatening if we get them wrong,” says Matthias Baumhof, CTO at LexisNexis Risk Solutions. “But they offer huge value to the customers if we get them 99% right.”
LexisNexis uses machine learning algorithms to sort transactions into behavioral profiles to predict whether any particular transaction is fraudulent, or suspicious. There’s historic data, for the initial training set, as well as ongoing training.
“If a current transaction is confirmed to be a fraud after a few days, and they share that with us, we can learn from the confirmed fraudulent behavior,” he says, noting that for anyone looking to make the most of decision intelligence, now that behavior patterns change. “A certain amount of learning is always business as usual. If you don’t learn, you actually fall behind.”
Risk scoring traditionally involved a series of if-then decisions. If a transaction was over a certain amount, or outside the user’s home area, or with a new merchant, it would be flagged for review. But as the decisions get more complicated, it’s hard for if-then systems to keep up.
