![Decision Intelligence: Expanding the Horizon of Business Intelligence](http://media.assettype.com/analyticsinsight%2Fimport%2Fwp-content%2Fuploads%2F2021%2F03%2FDecision-intelligence.jpg?w=480&auto=format%2Ccompress&fit=max)
The volume of data businesses produce today carries much significance in terms of overall growth. Foresighted companies know that if they want to vie in a highly-competitive market, they must deploy advanced analytics to ever-growing data sets. Using business intelligence allows them to look into their historical and current data sets, and it provides them predictive views of their business operations. Augmented by artificial intelligence and machine learning, business intelligence provides enterprises with decision-making context and recommendations. This significantly drives a move towards decision intelligence, the creative blend of technology into enterprise decision-making strategies and workflows.
Decision intelligence is an emerging field that involves different decision-making techniques to design, model, align, implement, and track decision models and processes. Integrating it offers a framework for business decision-making and processes through the integration of ML algorithms.
Since decision intelligence provides intelligent decision-making to businesses, it performs by observing germane information, investigating collected data, considering existing business capabilities, and contextualizing decision time.
As modern businesses are starting to make the most of their data, they perceive that data and content should not be considered distinct aspects of information management. Instead, both should be managed in an integrated enterprise approach. This enterprise information management brings BI and enterprise content management on the same page. With the introduction of business intelligence, companies are now better able to interpret and visualize every aspect of their business. This will even enable them to function in a better way.
Conventionally, many BI vendors target only the top of the pyramid. But there is a paradigm shift now, taking business intelligence to the bottom of the pyramid with a focus of self-service BI. Self-service BI provides end-users the ability to capitalize on their data without having technical skills. Since it is crucial to comprehend the impact of today's decisions on all of the desired outcomes, the next-generation BI tools can be the obvious solution leveraging faster and more modern cloud data platforms to solve business problems. Unlike self-service BI, decision intelligence makes the use of the right information to solve the problem at the right time and more accurately.
Financial services providers use decision intelligence to process credit applications for mortgages and car loans, for example. Driven by artificial intelligence, it can use a customer's credit score, income, and other information to determine whether the client is eligible for certain services. By integrating decision intelligence, retailers can easily decide how much inventory to purchase to meet optimal stock levels. They can also be able to make decisions on when to order things to ensure just-in-time warehouse management.
As it is expected that there will be 175 zettabytes of data worldwide by 2025, much of these data are unstructured. This massive volume of data won't be processed manually. This is where organizations will need to use decision intelligence coalesced with advanced machine learning algorithms. It will augment humans' capabilities in the decision-making process with data and algorithms, thereby enhancing the quality of decisions. Businesses must implement the decision intelligence platform that combines business rules, process automation, data and analytics, and machine learning technologies. That also must allow human intervention when required for both stateless and stateful decision-making processes.
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