Decision intelligence is a relatively new discipline that has recently gotten a lot of attention in the industry, especially as the COVID-19 pandemic and digital disruption continue to increase both business problems and decision-making processes are complex.
Decision intelligence is emerging as a solution that can connect decision support, decision management, and complicated systems applications, as the urgency to digitize and gain competitive value from new technologies like artificial intelligence (AI) and machine learning (ML) grows.
Decision intelligence serves as the missing link between data and better decision-making for organizations trying to stay afloat in a sea of data. Financial services, healthcare, and supply chain are among the industries in desperate need of reliable decision-making, and decision intelligence can help them make the most of their data and maximize AI’s potential.
What Is Decision Intelligence?
Data and analytics architects can use decision intelligence to model, align, develop, implement, and track decision-making models and processes. Business results and performance are thought to be influenced by decision intelligence.
Using decision intelligence, data science is used to connect business problems and find appropriate solutions. In order to accomplish this, stakeholder behaviors must be examined and incorporated into the decision-making process. Data intelligence encompasses data science, business intelligence, decision modeling, and overall management.
The Benefits of Decision Intelligence for Business
- Data-driven decision: Despite the fact that 91 percent of businesses believe data-driven decision-making can help them grow, only 57 percent use their data. To gain a competitive advantage, you must correctly analyze the available data, make some predictions, and choose the best option. AI can dig deeper into the data set, detecting hidden patterns and anomalies that could have a big impact on the outcome.
- Multiple problem-solving options: AI-powered decision-making algorithms can be quite flexible when one of the parameters is changed, highlighting several outcomes of a particular decision. This feature can help the business choose the best option from a plethora of possibilities while keeping current goals and growth strategies in mind.
- Faster decision-making: Only 20% of organizations are satisfied with their decision-making speed. Others admit to wasting too much time trying to make the best decision, which isn’t always the best decision. Because AI decision-making systems can process large amounts of data almost instantly, they can speed up the process as much as possible.
- Mistakes and biases elimination: At least five different types of biases have been identified as having a direct impact on the outcomes of business decisions. It empower you to completely avoid them because a properly developed algorithm takes an ultimately objective look at the available facts. Do intelligent systems, on the other hand, consistently make better decisions than humans? Despite the fact that they are guided by vast amounts of data and are immune to cognitive biases, they still require human validation, particularly when the choice made may result in conflicts of interest or values.
- Handle complexity: Data that is useful must be distinguished from garbage, and businesses must derive insights from it. By applying machine learning to data and assisting people in making decisions, Decision Intelligence, which combines data science and social science, has the potential to democratize analytics. While machines are becoming more efficient at work and other time-consuming manual tasks, they still lack the nuance and understanding that humans have. A Decision Intelligence initiative could include examining sets of data, running potential outcomes through machine learning models, and presenting decision-makers with potential courses of action.
Examples of Decision Intelligence
- Recommendations engines: These tools use analytics to forecast what products or services customers will want, as well as what movie or television show they will watch next. These tools assist the end-user in making context-sensitive decisions. Your business will benefit from automated tools that use human logic to increase product consumption (s).
- Pricing: Prices can be adjusted by automated systems based on data thresholds. Companies can use multiple decision-making frameworks to test, iterate, and refine decision processes and AI models due to the large volume of transactions. To ensure you have the most up-to-date information, use intelligent apps to break down data silos and get data across the organization. This is especially beneficial for businesses that deal with a lot of transactions, such as airlines and pharmaceutical companies.
- Sale optimization: By analyzing data on potential customers, automated tools can assist in prioritizing sales leads. Reps can better understand and focus on high-impact sales activities, identify the most likely to close opportunities, and even update their sales forecasts in real-time, thanks to decision intelligence. Alternatively, you could determine which deals in your pipeline are the most vulnerable, forecast future revenue based on historical conversion rates and close times, and distribute this information to the front-line teams who require it.
- Retail store management: Collect real-time data on retail stores and performance with intelligent apps, allowing you to make more targeted decisions that affect performance. If you track individual store performance in conjunction with customer demographics and geographic trends, for example, you’ll be able to react faster and make more accurate decisions and forecasts.
Any modern business that wants to function and grow in the digital age needs decision intelligence. It’s a useful tool for businesses to establish protocols for making decisions that have a measurable future impact. Businesses can make useful data-based decisions that impact important systems and processes to solve unique business problems when they incorporate decision intelligence into their digital transformation strategies.
Data science is no longer enough, and it is only half of the solution. By combining analytical and structured behavioral decision-making approaches, decision intelligence takes it a step further. A half-hearted approach to decision intelligence has led to the failure of many businesses. The primary cause of business failures involving decision intelligence is a lack of foresight and intuition.
In order to solve complex, diverse, and multifaceted problems that would be impossible to solve without the use of decision intelligence, businesses will need to connect decision makers to innovative technologies like AI and machine learning within the future.