Now more than ever, business decisions carry a lot of weight. They must be faster, more accurate, automated, and cognizant of the entirety of your business intelligence. It’s a daunting list of responsibilities for any decision to live up to.

In order to make the most of your data and ensure you are taking actions that will benefit your business, you must evolve your decision-making processes. When you blend new ways of engaging with and acting on critical business data, you reach a new level of data-driven decision-making. You discover what Gartner calls decision intelligence, which enables your teams to take definitive action that leads to optimal business outcomes.

Taking intelligent action on your data

Decision intelligence combines a variety of decision-making techniques with artificial intelligence (AI), automation, business intelligence (BI), and forward-thinking decision-makers to drive meaningful impact on your organization and achieve a higher return on your data and advanced technology investments, giving you the opportunity to gather actionable intelligence that powers more progressive decisions. 

One key component of decision intelligence is intelligent business apps—custom applications within your BI tool that users with little or no coding experience can create to develop more robust BI. When intelligent apps are layered on top of your data, you get specifically targeted, interactive analytics that can automate actions or enable users at any level to take action on the data they are seeing.

Such apps enable you to gain additional context around business decisions, scale your ability to utilize massive amounts of data for insight, and review the impacts decisions will have across your organization.

Decision intelligence can also be facilitated by machine learning—a component of AI—and data science, as they are technologies capable of enabling a level of forecasting and predicting that otherwise wouldn’t be possible.

As your company adopts decision intelligence, you’ll make decisions faster, easier, and more cost-effectively than before.

The need for decision intelligence

Perhaps the following decision-making process example sounds familiar: You’ve collected data, you’ve visualized it and found critical insights that will affect your business, and then, through dashboards or reports, stakeholders have used those insights to make decisions. A final choice is made and the process starts all over again. 

It’s a very iterative and linear process. Decisions can come days, weeks, or months after the initial data is gathered. And predicting outcomes is very much based on past performance and behavior. 

Traditional decision-making processes like the example just provided are becoming less effective in a modern business environment. The complexity of globally connected organizations and digital disruption across all industries have introduced a level of unpredictability for forward-thinking decisions. Given the exponential growth of available data, traditional models become unsustainable. 

In contrast, decision intelligence allows your organization to take your traditional decision-making processes and combine them with advanced technologies—like AI, ML, intelligent apps, and natural language queries (NLQ)—to “transform data dashboards and business analytics into more comprehensive decision-support platforms,” CIO.com contributor Maria Korolov wrote in March 2021.

Now with decision intelligence, you can make decisions based on more than just past performance. You can use your data and tools to analyze nuanced data relationships and ask questions like, “How will this decision affect my company next year?”

With the integration of advanced AI like NLQ, your BI tools can suggest data that aligns with your current questions—so you don’t even have to ask exactly the right business questions to get impactful answers. 

As a result, “decision intelligence technology and people each do what they do best,” PCMag.com’s Pam Baker wrote in April 2021. “Technologies, like analytics and AI, rapidly find the connections and patterns in huge pools of data. Decision intelligence can use that information and help you apply the more intangible human factors, like intuitive intelligence, creativity, experience, and the ability to successfully navigate through nuances.”

Examples of decision intelligence

So, what does decision intelligence look like outside of theoretical descriptions? How could it impact your business? Here are a few examples:

• Recommendation engines. These tools use analytics to predict what products or services customers will want, or what movie or TV show to watch next. These tools help the end user make decisions with context. Your business benefits from automated tools with human logic that will increase the consumption of your product(s).
• Sales optimization. Automated tools can analyze data on prospective customers and help prioritize sales leads. Use decision intelligence to understand and focus on high-impact sales activities, identify the opportunities most likely to close, and even enable reps to update their sales forecasts in real time. Or you could see which deals in your pipeline are most at risk, predict future revenue using historical conversion rates and close times, and get this information to the teams on the front lines who need it.
• Pricing. Automated systems can adjust prices based on data thresholds. With the large volume of transactions, companies can apply multiple decision-making frameworks to test, iterate on, and refine decision processes and AI models. Use intelligent apps to break down data silos and get data across the organization to ensure you have the most up-to-date information. This is especially beneficial for transaction-heavy businesses, such as airlines and pharmaceutical companies.
• Talent management. Use decision intelligence and intelligent apps throughout the hiring and employee evaluation process. HR departments can use intelligent apps to track potential employees through the application, interview, and hiring process. And they can monitor current employee satisfaction to better understand retention and predict future hiring needs.
• Retail store management. Use intelligent apps to gather real-time information on retail stores and performance to make more targeted decisions impacting performance. For example, by tracking individual store performance in conjunction with customer demographics and geographic trends, you’ll be able to react with greater agility and make more accurate decisions and forecasts.

Check out the second blog post in this three-part series on decision intelligence to learn how to establish a DI framework.