Business Intelligence (BI) platforms are evolving. By adding artificial intelligence and machine learning, companies are transforming data dashboards and business analytics into more comprehensive decision support platforms. This shift toward “decision intelligence” sees its sophisticated combination of tools increasingly embedded into corporate workflows, when and where decision-makers need them most.
“Decision intelligence is the company's ability to process large amounts of data to make decisions,” says Nicole France, Analyst at Constellation Research. “It's the same thing business intelligence would do, but accessible across the entire enterprise.”
Some of the most visible examples of decision intelligence in action are recommendation engines, which use analytics to predict which products consumers would find most appropriate or which movies they should watch next. Tools like these provide context and relevant options to help people make better decisions, France says, adding that dashboards and analytics from traditional BI tools are still valuable, but decision intelligence is more accessible and relevant.
“For people on the front lines, context is important,” she says. “And there is a degree of complexity that is hard to get right. The goal is to present things in a clear and easy-to-understand way, so that people can grasp complex analyses and make a decision quickly.”
The Case for Decision Intelligence
The Covid-19 pandemic accelerated digital transformations in almost every sector of the global economy—and AI is increasingly at the center of it. More than 95% of companies surveyed by 451 Research consider AI important for digital transformation—and 65% say it is very important.
According to the survey, released in late January, AI adoption increased by 9 percentage points last year in the US compared to the previous year, with only 28% of companies saying they slowed down AI initiatives as a result of the pandemic.
And a key field where AI is gaining traction is Data & Analytics. According to a 2021 survey of software developers and IT leaders by RealBI, 41% of companies saw an increase in requests for access to data and analytics, with one of the main reasons being to enable users to make data-driven decisions. In addition, the survey showed greater interest in embedding machine learning into analytics software or dashboards, with nearly 16% planning to add the technology in the near future, compared to 6% of companies currently doing so.
Just as adding AI or machine learning to a business intelligence platform allows it to evolve into a decision intelligence platform, providing context, predictions, and recommendations when and where the decision-maker needs them.
According to Gartner, more than a third of large organizations will have analysts practicing decision intelligence by 2023.
The research firm defines “decision intelligence” as a framework that allows data and analytics leaders to design decision models and processes in the context of business outcomes and behavior. In practice, this means that decision intelligence uses analytics to help employees, customers, or business partners make decisions, offering them data, analysis, and predictions when and where they need them.
As decision intelligence becomes a core part of business processes, decisions are made faster, more easily, and more cost-effectively than before.
Use Cases for Decision Intelligence
Cybersecurity is an area where people need to make decisions based on large amounts of fast-moving data, with a lot of potential risk for their companies. Here, AI and ML can help security analysts make better decisions, as shown by networking company Cato Networks.
“We use AI and ML intensively for various activities at Cato,” says Avidan Avraham, Research Team Leader at the company. “For example, we built a reputation model that uses all the information we have about a domain or IP address. Based on internal network data and open-source intelligence data, it predicts the likelihood of it being malicious.”
This means threat-hunting analysts can prioritize their investigations, he says.
Cato built its own technology to do this, using Amazon Elastic MapReduce to train its models. The company has been using the system for over a year, Avraham says, with good results and a low false-positive rate. “It is embedded in our analysts' workflow,” he says. “Before we built this technology, we used to do the analysis manually, so obviously, it's a much faster process now.”
Decision intelligence can also help companies be more consistent. Consider, for example, a bank employee making a loan approval decision.
“What happens in many cases when individuals are involved is that each individual has a different background,” says Anand Rao, Partner and Global AI Leader at PricewaterhouseCoopers.
There are ways companies try to achieve consistency, such as with training, but external factors still come into play—if a loan officer is having a bad day, for example. Here, decision intelligence tools can provide context and recommendations to help create more consistency in business processes.
Decision intelligence applications in other corporate domains, including customer relationship management and sales tools, are also growing—and it's no surprise, given the promise of pairing human intelligence with AI to augment the decision-making process.
Maria Korolov, CIO (US)
BI systems and strategies are being augmented with AI and machine learning to provide decision-making context