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In today’s technology-driven, digital world, data is the most valuable currency. Businesses monetize data and enhance business efficiency through intelligent data-driven decisions. Reports suggest that at least 2.5 quintillion bytes of data are generated every day by humans. The abundance of data might make the process of analysis difficult. Well, with disruptive techs like AI, data analytics, and machine learning it becomes easier to curate, process, and analyze them to derive business insights. The deployment of advanced data analytics paves way for business intelligence. 

However, this process of data-driven business intelligence transcends beyond collecting and mining datasets. This is exactly where decision intelligence steps in. Gartner defines decision intelligence as a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor, and tune decision models and processes. 

The Decision-Making Context

Deriving business intelligence is not a simple process. Wrong decisions might end up in disastrous outcomes and complex situations. Instead of limiting the decision-making context to just correlating the data reports, businesses should try working with outcome-based contexts. For example, starting by defining the goals or outcomes we need, it becomes easier to answer the questions. Decision intelligence smartly uses data science to arrive at smarter business decisions. With decision intelligence, the decision is the major part and queries come after that. 

Decision intelligence will help reduce the time in developing and deploying complex business choices. Rather, we can just incorporate different decision-making methods and try to focus on the notion of how actions lead to outcomes. This might look like a reverse methodology, where the company can start from the decision itself, and then move on to defining the solution. 

Decision Intelligence in Financial Sector

The rapid acceleration of digital transformation has redefined the financial services sector. While consumers are going digital, these institutions desperately need to revamp their conventional operation patterns and look at adopting intelligent business processes. Adoption of decision intelligence in the financial sector will enable processing credit, car loans, and mortgage applications based on the customer’s credit score, income, and other information. AI-driven decision-making models can enhance the customer experience and services. Financial service providers can also use decision intelligence to make decisions on brand extension, or geographical expansions based on intelligent analytics. Asset and investment management, retail banking, and financial security are the other use cases of deploying decision intelligence in financial institutions. 

Decision Intelligence with Hybrid Decisions

Machines have taken up many tasks and automated them to unburden the human workforce. However, decision-making is something that needs assistance from humans. Feeding data into machine algorithms and expecting them to arrive at business decisions might cause huge damage since the AI systems are not yet as intelligent as humans. These machines and algorithms are efficient enough to provide us insights and possible decisions, but still, need consultation from humans. This is what makes decision intelligence more acceptable since it provides a hybrid decision-making model, where both machines and humans form decisions together based on recommendations and insights. 

 

It could eliminate biases that are learned from old or existing datasets and incorporate new aspects of a business. Unlike machines, human barons do not work with respect to a black or white background. Instead, we see nuances, inner meanings, and possess intuitive intelligence and logical reasoning capabilities. This makes the decision outcomes more clear and objective. 

 

The Rise of New Perspectives

Decision intelligence leverages the capabilities of AI and ML to derive business outcomes based on decisions and what-if scenarios fed to the algorithms. Both predictive and prescriptive analytics plays a pivotal role in extracting intelligent insights from data pools. 

 

The future seems bright with decision intelligence now trending among businesses. Imagine somebody sitting beside you and telling you how your new product will work in the market, or maybe you could access product recommendations for a particular consumer. Isn’t it great? Well, in the case of decision intelligence, the difference is just that the instructor is not a human but an AI. Decision intelligence platforms should integrate rule-based analytics, process automation, machine learning, AI, data, and analytics. Decision intelligence clubbed with higher computational powers will enable the AI systems to provide the most profitable options and business decisions to the CIOs.