What is decision intelligence?
Decision intelligence is the ability to understand and engineer how decisions are made to improve decision-making. With connected and contextualized data, organizations can power effective analytics, decisions, and outcomes across the enterprise.
Decision intelligence supports nuanced decision-making at every level – strategic, operational, and tactical. It creates a trusted data foundation to provide confidence in tools such as artificial intelligence that help automate businesses and enable faster, more accurate decisions.
At their core, decision intelligence platforms combine explicit decision modelling, AI, analytics and related capabilities to support, augment or automate decision making, driving business outcomes.
In today’s decision intelligence era, it’s not enough to simply connect data. The real transformation happens when it’s contextualized.
At the heart of this transformation are three foundational pillars:
Trusted data—because decisions are only as good as the data they’re built on.
Composite AI—because no single model or technique fits every decision. Everyone needs the right tools for the right tasks.
Contextual analytics—because context is what turns raw data into real-world insight.
When these three pillars come together, organizations can move from fragmented insights to unified intelligence; from guesswork to precision; from isolated decisions to enterprise-wide impact.
This is the bottom line: with trusted data, effective AI, and contextual insights, organizations can make decisions that are not just faster—but smarter, more explainable, and more aligned to strategic outcomes.
Why is decision intelligence important?
Decision intelligence plays a pivotal role in the ongoing landscape of data-driven insights to uncover risk and opportunity. It provides avenues for measurable growth, as well as mitigating against challenges which could have an impact on your wider business goals.
Several internal and external factors can pose barriers to growth. In a period of global uncertainty, having a platform you can rely on to make informed and calculated real-time decisions is invaluable. Meanwhile, as customer or client expectations shift and your pace of change needs to match this, decision intelligence ensures that you don’t get left behind.
The need to manage risk and adhere to industry governance and regulations has also never been more pertinent. A decision intelligence platform can employ a detailed framework for transparency, safety, accountability, and privacy to achieve this. This can be done with the use of an approach that focuses on management, helping to monitor and govern potential threats and negate them before they can have an impact.
At its core, decision intelligence makes it easier to navigate all of these hurdles, while providing faster, smarter and more transparent decision-making at scale.
What’s more, it also encourages and facilitates the adoption of emerging technologies and business opportunities to drive you towards your strategic business goals. The use of AI and machine learning has automated many decision-making narratives, enhancing the speed and efficiency at which businesses can operate.
A decision intelligence platform will leverage a network of interconnected data ecosystems to analyze data and provide holistic real-time insights which drive business decisions. At a time when there’s a booming data economy which draws on innovative new technologies, decision intelligence makes it more effective than ever to make faster and smarter choices that are aligned to your organizational strategy.
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Using artificial intelligence, decision intelligence unlocks the power of entity resolution and knowledge graphs to turn data into accurate decisions at scale. This means that data becomes more than the sum of its parts – not piecemeal, siloed, and afflicted with poor quality. Entity resolution connects all data from previously siloed and scattered points, and creates a single, trusted, connected, and reusable resource.
What is the impact of decision intelligence?
Decision intelligence and the ability to utilize entity resolution and graph analytics to create a complete, meaningful view of data across an enterprise provides the contextual data foundation businesses need to enhance decision-making across the customer lifestyle, uncover hidden risks, and discover hidden opportunities. Decision intelligence is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made, and how outcomes are evaluated, managed and improved via feedback.
It’s clear that decision intelligence is a critical must-have. The key reason, says Joseph Sieczkowski, CIO and Chief Architect of BNY Mellon, is that data and contextual decision-making are core to the bank’s digital transformation.
“Our hard-won vantage point is allowing us to use data science to highlight future opportunities, which in turn, we can present back to our customers to help them increase revenue, reduce costs, and reduce risks.”
That point is echoed by Bina Mehta, Chair at KPMG, who explains that data is at the core of enterprise decision-making.
“It’s about making better decisions and about managing our risk,” Mehta says. “But it’s not just about our financial performance – it’s about the other commitments we make. In our world, that might be quality, customer service, and ESG commitments. Those measures are harder to pinpoint. That’s why data is really valuable in the broader sense”
What is the decision lifecycle?
- Overview
This is where we design how a decision should be made, what data is used, who’s involved, what rules apply, and what outcomes are possible. It’s like creating a blueprint for a smart decision. Teams use real data, expert insights, and tools to map out decisions before putting them into action making them clearer, dependable, and easier to explain.
- Decision modeling
This brings together connected data, machine learning, analytics, rules, and knowledge graphs to frame the decision. This step ensures that decisions are grounded in context and supported by explainable models.
- Next is decision execution
Once a decision is modeled, execution is about putting it into action. That could mean automating it completely, or guiding a person to make it. Decision platforms help connect decision logic to real-world systems, like approving a loan, flagging a fraud risk, or sending a customer alert so that decisions happen consistently, quickly, and at the right moment.
- Finally, there’s decision monitoring.
After decisions go live, monitoring lets us track how they’re performing. Are they working as expected? Are there patterns we didn’t anticipate? We can measure results, review trends, and make improvements over time. It’s about keeping decisions accountable, adaptable, and optimized with clear insights for both teams and regulators.
Underpinning all of this is Quantexa’s Decision Intelligence Platform, which integrates key capabilities like data governance, entity resolution, composite AI, and contextual visualization. These elements work together to support, augment, and automate decisions across the enterprise.
The takeaway here is that decision-making isn’t a one-off event, it’s a lifecycle. Your Decision Intelligence Platform should enable you to optimize every phase of that lifecycle with trusted data, contextual intelligence, and scalable AI.
What is a decision intelligence platform?
Decision Intelligence Platforms (DIPs) are advanced software solutions that enable organizations to design, manage, and automate decision-making processes for both humans and machines. By integrating data, analytics, knowledge, and artificial intelligence, these platforms help enterprises make more informed and consistent decisions.
DIPs provide a collaborative environment where decision models can be explicitly designed, executed, and governed. They coordinate decision workflows, monitor performance, and ensure accountability through transparent tracking and evaluation of outcomes.
Key capabilities typically include rule- and logic-based engines, machine learning, business intelligence, natural language processing, optimization, graph analytics, AI agents, and simulation. In addition, DIPs leverage real-time event processing and prepare multi-structured data to deliver actionable insights and continuous decision improvement.
IDC MarketScape Recognizes Quantexa as a Decision Intelligence Leader
What are the core capabilities of a decision intelligence platform?
Decision Intelligence Platforms provide a series of core capabilities that support a robust decision lifecycle which make them a pivotal asset in making faster and more informed choices. Here are some of the most important:
Decision modelling
This process draws on historical data, predictive analytics, and scenario testing to break down and represent how repeatable, operational decisions are made. They are visual representations of underlying logic, data inputs and rules which can be used to forecast outcomes, unify business intent across an organization, and streamline the decision-making process.
Inputs can be mapped in accordance with their predicted outcome via tools such as decision model notation, frameworks or scorecards. An example would be how a bank’s credit model might formalize how credit score, income, and collateral lead to loan approval or rejection.
Decision execution
This revolves around the processing and execution of decision flows, which can be done by both human and machine. Decision intelligence platforms allow for real-time batch execution as part of a wider decision flow.
As part of the decision execution process, operational decisions are integrated into works and other business systems. This allows them to be automatically actioned based on a series of rules, analytical predictions and predefined workflows. In doing so, any decisions which are made can be immediately implemented across an organization.
An example of decision execution in action might be when a customer applies for a loan. The execution layer of a system might rely on a credit-score model in real-time to apply internal business rules. This would lead to an immediate approval or denial.
In order for this kind of system to function properly, high operational capabilities are needed. This means decision engines or scoring services, APIs or data streams for integration, case-management for workflows and human review of exceptions, and reliable infrastructure.
Decision monitoring
Part of any good decision intelligence platform is the ongoing monitoring and auditing of the system. Decision monitoring exists to view, evaluate, and audit decision flows. This ensures the continued accuracy of a platform, while also allowing improvements to be regularly implemented.
In doing so, it becomes easier to monitor the success of performance targets, while also making the necessary changes to adapt and adjust to improve outcomes. Using the same bank loan example, decision monitoring might be represented by assessing loan default rates against predicted risk scores.
Governance can also be included within this stage. Data lineage and business rules can be catalogued in a way that allows for each decision to be explained. The regular monitoring of these kinds of policies fosters high levels of transparency. This in turn builds good faith, while also carrying out vital audits that help protect organizations.
What are decision intelligence platforms used for?
Decision intelligence platforms are ultimately used to provide real-time insights for faster, more confident decision-making. How they achieve this will depend on the type of decisions that need to be made. Three of the main decision types that a decision intelligence platform can address are:
Decision type | Strategic use | Use cases |
Operational | Long-term, high-impact, cross-functional | M&A, market entry, digital strategy |
Tactical | Medium-term, departmental or cross-team decisions | Budget allocation, hiring plans |
Strategic | Daily, high-frequency, usually automated | Fraud detection, routing, approvals |
In order to enable these types of decisions, a decision intelligence platform will rely on three core areas of opportunity: automation, augmentation, and support. Here’s how each of these elements can be utilized, as well as examples of how each might be used.
Decision Automation
Used primarily to support operational decisions, this is the process of fully automating decisions within a platform. Automation is most commonly used for lower-risk decisions which are less complex in nature. This helps organizations respond quickly and effectively to disruptions of unforeseen opportunities.
Use case: A bank might deploy automation systems that continuously monitor customer transactions. This will be done in accordance with predefined risk monitoring thresholds and existing revenue targets. This mitigates risk while also ensuring revenue potential is optimized in line with current market conditions.
Other examples:
Data stewardship
Decision Augmentation
Augmentation is best employed when making tactical decisions. Advanced recommendations and insights are used to enhance human decision-making capabilities. This is most useful when complex decisions need to be made that can be boosted by the use of AI. In these instances, a human will still remain responsible for the final decision that is made.
Use case: An insurance provider might integrate augmentation tools as part of a workflow that processes claims. The tool would automatically flag high-risk applications via the use of historical customer information, market data, and any other relevant analytics which are available. The augmentation tool would not action any decisions but rather inform and suggest ways in which the provider might want to move forward with a claim. The claims handler will be able to assess the nuanced insights which the tool provides, while still making the final decision with the information available.
Other examples:
Customer 360
SAR filing
Campaign execution
Decision Support
Strategic decisions rely on decision support to function to their fullest. These are data-driven insights which help to assist human decision-makers. This might happen in the form of AI and prescriptive analytics, which provide actionable suggestions for a human to assess and consider. Interactive dashboards and data visualization tools are prominent features of this process.
Use case: A unified dashboard might collect key risk metrics a bank wants to monitor. These could be factors such as credit exposures, liquidity ratios, and market changes. The dashboard can then be assessed by a human worker to understand stress scenarios and monitor real-time indicators of risk. This gives them the power to adapt and change strategy when required, in turn protecting the capital of the bank.
Other examples:
FRAML Transformation
Data democratization
CX Enhancement
These three core aspects of the wider decision-making process are what enable organizations to make informed, real-time decisions to drive growth and achieve positive business outcomes.
Who can use a decision intelligence platform?
Decision intelligence platforms are used for decision-making across different industries and use cases, helping professionals with their everyday work. Some examples include:
Customer-facing teams
Relationship managers, Sales representatives and Customer support staff use data-driven insights for better customer experience, timely engagement and new sales opportunities.
Financial crime detection and prevention
Financial crime and Fraud analysts and investigators benefit from a truly connected and detailed view of risky behavior to detect and prevent fraudulent activity.
Business decision makers
Business decision makers use insights to understand trends, make predictions and support key decisions.
Data scientists and data engineers
Data scientists and data engineers can truly make a difference for their organization by using a decision intelligence platform to run accurate and trustworthy analytics.
What are the benefits of decision intelligence?
Onboarding the right decision intelligence platform empowers organizations to use their data more effectively in driving better decisions, to understand how effective decision making processes are, and to enhance how efficiently and effectively decisions can be made.
Build a trusted data foundation
Ingest large external datasets while meeting security and data privacy requirements.
Maximize your data
Make the most of data across multiple use cases with a single repository.
Make more informed decisions
Get a holistic view of an entity and its hidden connections to make more informed and accurate decisions at every level – strategic, tactical, and operational.
Increase investigative efficiency
Reduce workloads by using context to generate only high-quality alerts, reduce false positives, and reduce investigation time from weeks to hours.
Automate decisions and enable digital channels
Provide customers, suppliers, or citizens with the most accurate, real-time decisions or recommendations through digital channels, while reducing workloads and costs of operational staff.
Scale with simple integration
Augment and enhance in-house capabilities using an open architecture, which enables rapid scaling to accelerate risk detection across multiple lines of business including retail, markets, correspondent banking, and trade finance.
How has decision intelligence changed traditional ways of decision making?
Before the advent of decision intelligence, data was often scattered across systems, siloed, incomplete, or of such poor quality that it was difficult to make connections. This often led to poor or inaccurate decisions that threatened the customer experience and lost revenue.
Today, decision intelligence provides the means to harness this fragmented data to provide a holistic, trusted view of B2C and B2B customers, allowing decision-makers and analysts to uncover hidden patterns that were previously unseeable to dramatically increase the value of insights.
With Quantexa's Decision Intelligence platform, decision-makers are now empowered to:
Get a holistic, trusted view of customers
Connect disparate data from internal systems and external sources to form a 360-degree view of your customers.
See a broader picture of relationships
Interlink distinct entities to see the big picture. Gain deep understanding of relationships such as social connections, households, corporate structures, and supply chains.
Uncover hidden opportunities
Use advanced analytics to trigger real-time insights that enhance customer experience and drive growth.
Tap into trusted data insights
Provide a rich visualization layer along with open APIs to allow integration within existing IT landscapes.
What are the critical components of Quantexa’s decision intelligence platform?
Decision intelligence platforms employ a new generation of software designed to automate or augment processes – empowering operational teams to make faster and more accurate strategic, tactical, and operational decisions.
By utilizing the power of artificial intelligence, these platforms connect billions of data points across internal and external data sources to provide a trusted and reusable data foundation enriched with vital intelligence about the relationships between real-world entities such as people, organizations, events, and places.
Quantexa brings these capabilities together in its single Decision Intelligence platform to give organizations the ability to protect their most valuable assets, optimize their resources, and identify opportunities for growth by providing the tools. Quantexa's platform takes these three steps:
- Unify data
Integrate any source at scale to build a trusted data foundation With multi-source data ingestion, you can rapidly onboard any source – internal, external, structured, or unstructured. Using Entity Resolution, you can bring siloed, disparate, and messy data together at scale for accurate single views. Continuously improve data quality as part of the unification process.
- Contextualize data
Reveal relationships and insights, create stronger analytics Reveal the relationships and context of how people, organizations, places, and other entities interact with each other. Generate insights that build stronger, more meaningful analytical models. Use open and easy-to-use tools and frameworks to make use of the full power of innovative graph, AI, and machine-learning methods.
- Decide and act
Augment and automate decision-making for actionable insights Automate manual, high-volume operational decisions and maximize your investments in AI for efficiency and cost savings. Transparent models mean each decision is explainable, with full visibility for security and regulatory requirements as well as model validation and optimization purposes. Empower teams to proactively explore insights and act — making faster, more confident, and more accurate decisions.
The ideal decision intelligence platform should also integrate easily with your existing IT landscape and support flexible deployment options: native or containerized, for private and public cloud, to future-proof your business and its growing data needs.
This platform should also provide quick time-to-value through a modular approach, allowing you to expand your platform's capabilities as your business grows.
What is the future outlook for decision intelligence?
As the world looks to the future, technologies will continue to evolve, providing decision-makers with even better tools with which to understand their customers to create more value, discover new markets and revenue opportunities, and increase their competitive advantage through better decision-making.
Adopting Decision Intelligence technologies will not become a substitute for human thinking, however. In fact, quite the opposite is true. Technology and automation delivered through DI platforms will provide a deeper understanding of the decision-making process itself thereby increasing the capabilities of decision-makers and guiding them through reliable data to make the best decisions possible.
As Gartner recently summarized, "in times of increasing complexity and uncertainty, decision-making as an organizational capability will become a main competitive differentiator. Those who are able to make better, faster decisions will win in the market."
Decision intelligence FAQs
What’s the difference between decision intelligence and AI? 
Artificial Intelligence (AI) is a branch of computer science focused on creating systems or machines that can perform tasks typically requiring human intelligence. These tasks include learning from experience, recognizing patterns, solving problems, understanding natural language, and making decisions.
Decision intelligence puts AI to work by creating a trusted data foundation with AI-powered data ingestion, entity resolution and graph generation, and then running analytical and AI models to turn the data into insights for decision-making.
This new approach helps to improve decision-making by understanding and engineering how decisions are made and enhancing how outcomes are evaluated and managed. It’s a practical discipline that helps organizations consistently make smarter, faster, and more accurate decisions.
How does decision intelligence use AI?
In today's fast-paced business world, decision-makers are often under intense pressure to make fact-based, consistent decisions in a hurry. This is why enterprises turn to technologies such as AI and machine learning (ML) to augment their decision-making capabilities.
The problem is that organizations often deploy these technologies without fully understanding the importance of placing the data they collect in context to gain the bigger picture of what data is being collected and how it is being applied to the decision-making process. Without context, AI predictions lack dependability, opening the door to a wide range of long-term automation challenges and other setbacks that can sink profits.
Entity resolution and graph analytics is what gives data context by connecting billions of data points spread across multiple systems into a single, accurate view that reflects the real-world connections between people, places, and organizations to create a single source of truth and revealing the hidden relationships between them.
When it comes to employing AI and ML as part of the decision-making process, the quality of the data you use is everything. This is why data scientists are so tightly focused on using reliable, transparent data to make the best algorithms possible.
What are examples of use cases of decision intelligence?
To protect your organization by improving fraud and financial crime detection and minimizing risk, organizations across banking, insurance and other industries can utilize the decision intelligence platform to conduct due diligence, and continuously monitor illicit activities, reducing the impact of fraud and financial crime on the institution by tens of millions, while reducing claims, and improving customer service.
Decision intelligence can be used to connect difficult-to-match datasets across the business and gain insights into customer interactions to enable sales and support teams to use a 360-degree customer view to be more efficient in their prospecting and support activities, leading to a growth in customer value. This helps to grow your revenue and improve customer experience by gaining better insights into customers with a 360-degree view.
Public and private organizations use decision intelligence to achieve data trust, transparency, and explainability, helping to improve digital resiliency and efficiency to provide customers, citizens and patients with simpler and smarter solutions as well as enable analytics and AI programs. This approach optimizes your organization and streamline workflows by creating a connected view of data assets that powers unique business applications
Is business intelligence the same as decision intelligence?
Decision intelligence is the latest step in the evolution of business intelligence. Instead of combining all available data, context-free, and presenting it to a decision-maker, decision intelligence uses artificial intelligence (AI) to provide a single analytical view – to either fully automate decisions or to help data professionals make faster, more accurate decisions through augmentation.
Is decision intelligence the future of decision making?
Decision intelligence represents a significant shift in how decisions are made, moving from intuition-based approaches to more systematic, data-driven methods. As technology continues to advance and organizations recognize the value of more intelligent decision-making, decision intelligence is likely to play an increasingly central role in the future of decision-making across various sectors. However, the full potential of decision intelligence will depend on overcoming current challenges and ensuring that it is implemented thoughtfully, ethically and by using a trusted data foundation.
Useful links
We’ve discussed a lot regarding decision intelligence in this guide. However, there could be more you want to know about the impact it can have on your organization. Browse the following articles for further reading.