Why Decision Intelligence Is important?
Decision intelligence is a framework that supports data and analytics architects model, align, develop, implement and track decision-making models and processes. Decision intelligence is thought to have a huge impact on business results and performance, with Gartner forecasting that over 33% of organizations will have analysts that practice business intelligence by 2023.
Decision intelligence connects business problems and applies data science to find appropriate solutions. For this to be achieved, stakeholder behaviors need to be analyzed and incorporated into the decision-making process. Data intelligence is best described as an amalgamation of data science, business intelligence, decision modeling, and overall management.
Distinctive characteristics of decision intelligence include:
- Decisions depend on the belief of action = outcome
- Rule-based approaches to ML (Machine Learning) & AI (Artificial Intelligence)
- Business problem solutions over diverse industries
Decision intelligence is crucial for any modern business wishing to function and grow in the digital age. It’s an important resource that helps businesses to establish protocols for decisions that have quantifiable future impact. When businesses incorporate decision intelligence into their digital transformation strategies they can make useful data-based decisions that impact important systems and processes to solve unique business problems.
Data science isn’t enough anymore and is only one-half of the solution. Decision intelligence goes one step further and combines analytical and structured behavioral approaches to decision-making. Many organizations fail due to half-hearted attitudes to decision intelligence. A lack of foresight and intuition is the central reason for business failures related to decision intelligence.
In the future, businesses need to connect decision makers to innovative technologies such as AI and machine learning to address complex, diverse, and multifaceted problems that wouldn’t be achievable without the use of decision intelligence.
Decision Intelligence Example
Visible examples of decision intelligence include recommendation engines that use analytics and powerful reduction algorithms to predict consumer affinity and disposition to a certain product or service. These tools deliver context and a range of multifaceted choices to help people make better judgments.
Sabre Airline Solutions
Sabre Airlines Solutions is a travel technology company based in Southlake, Texas. They provide booking tools, web and mobile urinary tools, and revenue management for a string of airlines and hotels. They used a strategic approach to decision intelligence to accelerate their business insights by developing an enterprise travel data warehouse (ETDW). This warehouse enabled them to provide round-the-clock business insights and attain a scalable infrastructure, (GUI) graphical user interface, and data aggregation capabilities which increased overall customer satisfaction.
Decision Intelligence Companies
The following are two key examples of decision intelligence companies:
- Peak – Peak delivers a range of decision intelligence services including demand planning, forecasting, supply chain logistics, and warehousing. They work across industries to provide decision intelligence services that yield quick results whilst supporting long-term business strategies. They use a unique decision intelligence platform that enables businesses to quickly deploy AI applications and harness true data potential.
- Silico – Silico has developed a platform that organizes data and collates it from different sources. The data is then aggregated and synthesized through decision models that reflect value and provide a framework for exploring decisions. The powerful decision models created by Silico connect actions, data, and outcomes which map value processes that are unique to businesses.
Decision Intelligence AI
Decision intelligence AI refers to the use of data to analyze the decision-making process through a series of automated judgments.
A successful AI journey should incorporate the following components:
- Knowledge and awareness of business problems
- Deep understanding of core techniques such as optimization and machine learning
- Aligned business teams that work together with decision-makers to transform the decision process
- Successful deployment capabilities that take into account the accuracy of models that change when they encounter production data
Decision intelligence framework
Decision intelligence frameworks meld together both traditional and advanced techniques to model, execute, design, tune and monitor models and processes. Businesses are turning to AI and automation wherever possible to modernize outdated approaches to decision-making.
Whilst AI models are great at generating predictions and labeling, they aren’t able to find substantial meaning behind the data, which is why organizations are starting to adopt decision intelligence frameworks.
The four main parts of decision intelligence frameworks are:
- A data warehouse in a centralized accessible location that stores varied business data
- Data management and business analytics tools that analyze and mine data from data warehouses
- (BPM) Business performance management tools that oversee business expectations
- A (UI) User interface that provides ease of access to information through an interactive dashboard
What Is Contextual Decision Intelligence?
Contextual decision intelligence autonomously collects and builds critical data to support important decisions. Extensive network graphs can be created that demonstrate real-world entities and their links to internal and external data. It then defines recognizable habits and patterns which help data scientists discern information.
Contextual decision intelligence integrates context and data with science models to automate critical decisions. The benefits are huge and build on integrated human knowledge and augmented decision making that redesigns business functions and spots consumer behavior patterns.
Decision Intelligence & Digital Transformation
The core foundation of decision intelligence focuses on accurate and efficient decision-making that’s based on the actionable knowledge of efforts and outcomes.
Businesses are becoming more aware of the need to future-proof their processes and infrastructure than ever before. Most organizations have expedited their endeavors to digitize operations and procedures to promptly respond to changing markets. Decision intelligence powers competent, actionable verdicts that combine data science, social science, and managerial science to access vital business decisions.
There are five steps to decision intelligence within a digital transformation context:
- Observe Relevant information can be collected and collated through models from multiple sources including transactional and historical data.
- Investigate Refining and analyzing information helps to build models that interpret outcomes.
- Model Emanating outcomes allows models to pursue different options based on existing capabilities and skill sets.
- Contextualize The complexity of the situation is contemplated and executable actions are evaluated and considered.
- Execute A final decision is chosen and action is made based on that decision.
