Decision intelligence: Key benefits and implementation steps 

Modern organizations, either in the commercial or public sector, perform a wide range of operations that differ in scope and importance – from elementary and routine daily pipeline processes (like inventory management or customer support) to strategic planning and large-scale initiatives (like optimizing supply chains or ensuring continuous improvement of personnel performance). The success or failure of these endeavors crucially depends on timely and consistent decisions made by CEOs and rank-and-file employees. 

Accurate decisions adopted in the nick of time can dramatically boost business outcomes and enable the enterprise to stay ahead of the curve in its niche. In contrast, poor decision-making spells subpar financial performance and lagging behind your competitors.  

While decision-making processes are a tough row to hoe in, the exponential growth of business and customer data obtained from multiple data sources has turned this challenging mission into a tall order for government and commercial decision-makers. Decision intelligence (DI) is a godsend for data scientists and top managers, helping them make knowledgeable and more informed decisions and enabling organizations to revolutionize their workflows.  

This article will explore the essence, significance, and benefits of decision intelligence, analyze the principles and mechanisms of its functioning, draw a clear borderline between decision intelligence and business intelligence, and showcase practical applications of this technology across various verticals. 

What is decision intelligence?

Decision intelligence unifies several disruptive technologies (primarily AI and machine learning but also natural language processing, data visualization, collaboration tools, and more) that allow for advanced analytics of available data retrieved from internal and external sources. Machine learning algorithms uncover hidden patterns across disparate data points, providing decision-makers with valuable insights to be leveraged as guidelines in decision support.  

Traditional approach to data exploration

What makes decision intelligence important for large organizations, small startups, and other entities in the contemporary business landscape? To answer this question, we should first look at the traditional approach to data exploration and adopting critical decisions.  

Conventionally, data science teams armed with essential software tools scanned different data source systems and drew all relevant information. Then, data interpretation began, relying on manual analysis and searching for hidden connections. Data-driven insights obtained in that way were leveraged for making strategic, tactical, and operational decisions on all levels of an enterprise’s hierarchy.  

This time- and effort-consuming procedure was rendered inadequate with increased data. Such information accumulates faster than employees can process, and analytical output doesn’t scale up parallel to data volumes. As a result of the information overload, organizations face a lack of insights to bridge the gap between data and decisions. This gap causes handoffs between departments and teams, data silos, analytics queues, and other inefficiencies, leading to increased risks engendered by biased analysis and missed business opportunities since decisions are considerably delayed. 

AI-powered decision intelligence platforms

The evident shortcomings of the old-school approach forced analytics leaders to turn to artificial intelligence. AI-powered decision intelligence platforms don’t replace humans since it is people who are charged with the responsibility to make decisions. Rather, they serve as robust supporting tools, enabling them to analyze data quicker, receive a holistic view of shop floor processes and business behaviors, and automate decisions that become more accurate as the technology’s sophistication and capabilities grow. 

Moreover, classical data analytics initiatives were the province of a limited group of professionals in the sector and technical experts, which acted as a halter on the organizations’ attempts to step up the routine and involve wider workforce audiences in making data-driven decisions. Thanks to the democratization of predictive analytics and data governance DI solutions usher in, actionable insights become accessible to a wider user pool, which includes non-technical personnel such as researchers, analysts, and executives. They ask simple who, what, why, and where questions and receive prompt responses that help them make better decisions. 

Wait a bit, you may say. Doesn’t business intelligence (BI) do this? Let’s find out how these two technologies differ. 

Decision intelligence vs. business intelligence: Highlighting the differences 

Being somewhat similar, BI and DI remain distinct phenomena in many aspects.  
 

Aspect	Business Intelligence (BI)	Decision Intelligence (DI)
Focus	Monitors business performance through analysis of historical data.	Prioritizes decision support for future actions.
Data processing approach	Uses prescriptive and diagnostic analytics to summarize and explain past performance.	Utilizes predictive and prescriptive tools to forecast future outcomes and suggest actions.
Type of data	Primarily deals with historical data.	Expands to include real-time data.
Sources of data	Relies on databases, spreadsheets, ERPs, and internal systems.	Incorporates external sources, such as sensors and IoT, handling both structured and unstructured data.
Technologies utilized	Uses traditional data warehouses, reporting tools, and visualization dashboards.	Leverages advanced technologies, including AI, ML, and NLP.
User interaction	An emerging field popular among forward-looking companies focused on advanced decision-making.	Highly interactive, adapting to user feedback and detected data patterns.
Complexity	Simpler, with basic queries, data aggregation, and elementary reporting tools.	More complex, involving advanced algorithms, machine learning models, and intricate data processing techniques.
Customization potential	High customization power but potentially less flexible, often constrained by predefined report formats.	High customization potential with adaptability to diverse decision-making needs.
Novelty	A mature, widely used practice.	An emerging field, popular among forward-looking companies focused on advanced decision-making.

Business intelligence provides information on what and why something has occurred, whereas decision intelligence shows how to improve. Given such impressive proactive decision augmentation power DI tools wield, it is no wonder that this market niche manifests a rapid spike in every decision intelligence category, increasing at a solid CAGR of 15.2%. 

What makes decision intelligence so effective? The secret is DI’s cornerstone principles and operational mechanisms.  

Digging deeper into decision intelligence organization 

There are three significant areas where DI solutions bring maximum value. 

• Insight generation. This software can sift through massive datasets and extract essential trends, patterns, and predictions that drive strategic decisions.  
• Decision automation. Complex and repetitive tasks are a cakewalk for AI and ML mechanisms that handle them error-free and fast, freeing human personnel for more creative assignments. 
• Optimization of future outcomes. Insight-driven decisions are always more accurate, consistent, and systematic than steps that rely on gut instincts or historical trends of long ago.  

Principles of DI systems

The efficiency of DI in solving these tasks is conditioned by the underlying principles that govern the functioning of decision intelligence software. 

• Data centricity. The input data conditions the quality of the system’s performance. That is why software engineers and data scientists implementing DI solutions prioritize data accuracy, integrity, completeness, consistency, relevance, conformity, and understandability. 
• Analytical precision. Even the best-organized data doesn’t guarantee adequate analysis outcomes if you utilize substandard methods and techniques for its processing. Understanding this simple truth, data experts involve cutting-edge technologies and approaches (machine learning, statistical modeling, entity resolution, and more) to dissect the data they deal with. 
• Explicability and transparency. When decision-makers have clear and straightforward explanations concerning the insights they obtain from DI tools, they come to trust them and aren’t afraid to leverage them in their workflows.  
• Continuous improvement. The decision-making process isn’t carved in stone once and for all. New challenges and unorthodox problems require constant refinement of the routine and instruments used in it. You can elevate them to a new level by fostering a culture of experimentation, encouraging perpetual learning, and setting up feedback loops. 

All these principles allow for the smooth operation of the decision intelligence pipeline. 

How does decision intelligence work? 

As vetted experts in data analytics services, we at DICEUS are well aware of the typical decision intelligence algorithm.  

Step 1. Data collection and integration 

It all starts with checking out multiple external and internal sources (such as consumer interactions, social media posts, sensor data, financial records, and more) to retrieve relevant information. The obtained data can be structured (for instance, spreadsheets or databases) and unstructured (emails, texts, etc.). When the collection process is completed, DI systems integrate all data in a unified data bank where all data points form a cohesive framework. Such an approach removes silos and presents a 360-degree view of the organization’s activities. 

Step 2. Data pre-processing and QA 

As a rule, raw data funneled through a DI solution contains duplicates, missing values, errors, and other inadequacies that distort analysis results unless eliminated. Besides, the disparate formats of various records hinder their successful analysis and interpretation. Therefore, all data subject for DI is cleaned, enriched, corrected, and transformed into a standardized shape to prepare it for further handling.  

Step 3. Model creation and simulation 

At this stage, novel technologies come into play. AI and ML algorithms are leveraged to build predictive models that analyze historical and real-time data and pinpoint trends and patterns. These models are then employed to forecast future developments and simulate different scenarios for such processes. By modifying input variables, analysts explore the scope of possible outcomes and understand the potential impacts of decisions they recommend.  

Step 4. Analysis and insights generation 

Now comes the analysis proper. At first, descriptive and diagnostic procedures are employed to understand the reasons for past events, detect data relationships, trends, and anomalies, and identify pivotal factors that influence outcomes. Then, predictive techniques help anticipate future developments, while prescriptive methods provide recommendations concerning the course of actions to take. Combining the two analysis types allows stakeholders to devise actionable strategies for various use cases. 

Step 5. Decision modeling and optimization 

Creating decision models paves the way to showcasing the range of choices decision-makers have. Since these models are based on multiple factors (objectives, risks, constraints, etc.), analysts obtain a comprehensive list of possible options and can assess the results they will face if they take any of them. Using optimization algorithms, they can select the most effective decision that will allow the organization to achieve its goals, optimize resources, and find a healthy balance among various tradeoffs. 

Step 6. Visualization and reporting 

To make complex data more understandable, DI tools present insights through visualizations, graphs, diagrams, charts, and interactive dashboards, allowing for foolproof comparison of scenarios and easy monitoring of the organization’s KPIs. Then, decision intelligence software generates automated reports where analysis results are summarized, and the best course of further action is outlined. Such reports can be tailored to meet the requirements of different users and expertise sectors. 

Step 7. Decision execution and monitoring 

The decision is just an intention. Only by implementing it can you see positive changes in the pipeline processes they are honed to improve. However, you can’t rest on your oars after a decision is put into operation. You should track its implementation, assess results, and understand whether they correspond to the envisaged outcomes. If any deviations are spotted, DI systems step in to perform readjustment and fine-tuning of their mechanisms in real time. 

Step 8. Learning and adaptation 

Machine learning-driven DI solutions can not only modify their functioning on the hoof but also learn from the results they obtain. Such ML model training occurs continuously, which leads to more accurate and effective decisions down the line. Moreover, changing conditions also enable DI software to stay abreast of market fluctuations and consumer behavior shifts, providing ultimate agility, flexibility, and system responsiveness in the dynamic industry environment.  

Step 9. Collaboration and enhanced intelligence 

DI solutions act as a go-between in human-AI collaboration. They provide data-driven recommendations that boost workforce expertise and supplement human intuition in making knowledgeable and nuanced decisions. Besides, state-of-the-art DI platforms offer various collaboration tools that foster intense communication and cooperation between teams and their members, ushering in an inclusive decision-making process based on collective thought, strategy alignment, and exchange of opinions. 

Such a meticulous and well-thought-out decision intelligence pipeline promises numerous perks to organizations onboarding DI software. 

Benefits of decision intelligence 

What advantages does decision intelligence have in store for companies? 

1. Greater decision accuracy. Intuition and guesswork give way to stringent and unbiased data analysis, which augments the reliability and objectivity of decisions. 
2. Increased efficiency. DI ushers in the automation of routine decisions and optimizes resource allocation, accelerating decision cycles, reducing wastage, and boosting productivity. 
3. Cost reduction. Since DI tools excel at detecting performance inadequacies and poor resource utilization, managers can optimize them, saving a pretty penny for the organization.  
4. Improved risk management. A proactive approach and predictive modeling harnessed by DI software help foresee possible risks, forestall negative scenarios, and develop robust contingency policies. 
5. Enhanced strategic planning. DI’s forecasting power allows for long-term decision-making and accurate assessment of potential outcomes. 
6. Significant adaptive capabilities. Armed with DI’s real-time insights, organizations can tweak their policies and approaches ad hoc and take the dynamic market situation in its stride.  
7. Augmented customer satisfaction. Insights provided by DI tools expose customer needs, preferences, and pain points, allowing companies to tailor their products and services accordingly. Plus, such data enables the creation of individualized customer experiences, which boosts client satisfaction and brand loyalty.  
8. Low onboarding barrier. Given most DI solutions’ automated and foolproof operation, you don’t have to rely on high-profile technicians or data experts to handle them. Commercial decision-makers can do it well after some short training.  
9. Sharper competitive edge. With DI software in place, you keep abreast of technological breakthroughs, leveraging the emerging know-how to outstrip your less innovation-driven rivals both in speed and quality of decisions you make. 

As you see, decision intelligence can become a game-changer in multiple aspects of your enterprise’s internal routines. What are the verticals where DI can be effectively employed? 

Applications of decision intelligence across various industries 

DICEUS has delivered dozens of data analytics projects to multiple organizations across different domains. This experience allows us to single out industry areas where DI solutions can bring the most value.  

Insurance 

Insurance companies employ decision intelligence software mostly in risk assessment and claims management. When leveraged for these purposes, DI mechanisms streamline and facilitate underwriting routine, accelerate claims resolution, minimize fraud incidences, and increase the precision of premium pricing. Besides, by analyzing customer data, insurers can tailor their offers and personalize policies, guaranteeing high customer satisfaction and clientele retention.

Banking and finance 

Risk assessment is the primary task DI tools perform for fintech organizations and banks, too. As a result, they mitigate potential threats, curtail losses, and ensure regulatory compliance. An in-depth analysis of customer demographics, needs, and purchasing behaviors fosters an individualized approach to loan recommendations, investment advice, and cross-selling options offered to consumers. 

Healthcare 

In this domain, decision intelligence excels at improving operational efficiency. Thanks to DI, medical institutions optimize scheduling and resource allocation, step up their inventory management, reduce the patient’s waiting time, and improve service delivery. DI solutions can also forecast disease outbreaks, patient admission rates, and resource needs, ushering in full-scale proactive healthcare management. And, of course, decision intelligence becomes a superb personalization instrument, allowing customized treatment plans and timely intervention in each medical case.  

E-commerce and retail 

Online and offline retailers benefit from DI’s predictive potential when they can understand future demand for all categories of products, especially for consumer packaged goods that bring them most of their income. Thus, decision intelligence helps them optimize inventory levels and avoid stockouts. Besides, decision intelligence acts as an ultimate personalization tool that enables the launch of targeted marketing and advertising campaigns and increases conversion indices.  

Transportation and logistics 

Route optimization and fleet management are the key tasks decision intelligence is used to solve in this sector. When leveraged wisely, DI can reduce fuel consumption and maintenance costs, shorten delivery time, increase vehicle lifespan, minimize downtime, and optimize vehicle utilization. Supply chain visibility can also be improved, boosting inventory management and enhancing supplier performance. 

Education 

Here, decision intelligence is all about the personalization of educational services. Schools and universities employ DI to tailor their curricula and learning materials to students’ individual needs, proficiency level, academic performance, learning style, etc., which results in more relevant training, high career readiness, enhanced quality of education and augmented student satisfaction.  

DICEUS has expertise in all these domains. We can develop and implement a first-rate DI solution that will revolutionize the decision-making routine across all departments and workflows of your organization, providing actionable insights for stakeholders and ensuring maximum customer satisfaction of your clientele. By contacting us, you will take the first step toward harnessing the decision-making power of artificial intelligence to improve your company’s efficiency. 

Conclusion 

Decision intelligence relies on cutting-edge technologies (primarily AI and ML) to analyze vast business and customer data, delivering actionable insights instrumental in operational and strategic decision-making. It is a more advanced version of business intelligence that allows analysts to process historical and real-time data from external and internal sources and solve more complex predictive tasks. 

Organizations that onboard DI software report greater decision accuracy, improved risk management, augmented long-term planning, significant OPEX reduction, and enhanced customer satisfaction. Decision intelligence solutions can be applied across a number of verticals (healthcare, education, retail, logistics, banking, insurance, and more) where they bring maximum value if they are developed and implemented by high-profile IT specialists with profound expertise in data processing technologies. 

Frequently asked questions 

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