The language of Decision Intelligence.

Actionable metrics are data that helps you make decisions and helps your business reach its goals or grow. They are related to something you can control or repeat meaningfully.

Advanced analytics leverages sophisticated autonomous and semi-autonomous tools to evaluate large datasets of real-time and historical information. These tools—including artificial intelligence and machine learning algorithms—can process both structured and unstructured data, though text-based unstructured data typically requires preprocessing through text mining before becoming actionable.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI employs sophisticated analysis and logic-based techniques—including machine learning—to interpret events, facilitate and automate decisions, and execute actions. This technology enables machines to understand, respond to, and learn with human-comparable levels of intelligence.

Augmented Analytics, also referred to as Augmented Intelligence, uses AI to surface insights automatically instead of requiring a human to manually discover insights from traditional BI reports and dashboards.

Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The methodology provides tools and software that democratize analytical capabilities — including recommendations, insights, and query guidance — across organizations.

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. The automation potentially encompasses every stage from beginning with raw datasets through feature engineering, model selection, deployment, and solution monitoring. More specifically, AutoML automates the selection, composition, and parameterization of machine learning models.

A BI (business intelligence) dashboard is a data visualization and analysis tool that displays on one screen the status of key performance indicators (KPIs) and other important business metrics. Dashboards serve as integral components of most BI software platforms and deliver analytics information to business executives and workers. They typically visualize data through charts, graphs, and maps to help stakeholders understand, share, and collaborate on information.

Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software can't manage them. The concept is characterized by three key attributes: unusually high data volumes, high velocity (the rate at which data changes, is collected, and grows), and significant variety in dimensionality and format.

A Business Context Graph (BCG) is an interconnected dataset that's been enriched with meaning. It enables Diwo's applications to apply reasoning against data sources, supporting complex decision-making processes. Traditional databases suffer from static, shallow context, which limits intelligence capabilities—BCG adds the semantic layer required for decision intelligence.

Business intelligence (BI) is an umbrella term for the processes, technologies and strategies used to analyze and present insights derived from data, including everything from simple spreadsheets and graphs to customer satisfaction survey results and resources that make data usable. The primary objective is helping organizations make better and faster decisions.

Business rules guide the everyday decision-making within businesses by outlining the relationships between objects, such as customer names and their corresponding orders. Business rules provide the foundation for automation systems by taking documented or undocumented information and translating it into various conditional statements.

Composite AI, also known as Multidisciplinary AI, represents the combined application of different AI techniques to improve learning efficiency and broaden knowledge representations. Rather than treating machine learning as a universal solution, organizations combine various AI approaches for better results when addressing complex business challenges. A composite AI system should be built on a "composite architecture" that integrates multiple methodologies: traditional rules-based systems, natural language processing (NLP), optimization techniques, and graph techniques.

Contextual Intelligence is a form of AI which leverages a business context graph to understand the business intent of analytics efforts. Contextual Intelligence applies the business metrics and business logic of a use case to uncover areas of improvement and propose specific actions, with a much higher degree of relevance and personalization to an end user. It generates a recommendation to improve performance against a specific metric and identifies the impact of proposed actions on the metric in order to predict the butterfly effect associated with a potential action.

Data analytics is the systemic computational analysis of data. It is used for the discovery, interpretation and communication of meaningful patterns in data. The discipline also encompasses applying discovered data patterns to inform effective business decision-making.

Data fabric is an architectural approach addressing data silos through flexible, resilient integration of data sources across platforms and business users, ensuring data availability wherever needed. While not a single static technology, data fabric conceptually provides consistent visibility and unified controls for managing disparate data and diverse technologies deployed across multiple data centers and edge computing locations—both on-premises and cloud-based.

A data pipeline constitutes a sequence of data processing steps. If data isn't already loaded into the data platform, ingestion occurs at the pipeline's start. Subsequently, each step produces output serving as input for the next step, continuing until completion. Some independent steps may execute in parallel.

Data-driven decision-making (DDDM) involves leveraging facts, metrics, and data to direct strategic business choices that support organizational goals, objectives, and initiatives. When companies fully harness their data's potential, all personnel — from business analysts to sales managers to frontline staff — gain the ability to make improved decisions grounded in data on a regular basis.

DataOps encompasses practices, processes, and technologies that merge data management with agile software engineering principles. The approach emphasizes automation and workflow optimization to enhance quality, speed, and collaboration while fostering continuous improvement in analytics.

Machines generate recommendations for decisions, including an expected business outcome — for example: "Buy X units from supplier Y, then you will save $Z million." The machine proposes the decision, but people make it. The user accepts, rejects, or changes the recommendations for a decision.

A decision flow is a diagram that helps make the decision between alternative courses of action that will lead to and effect a business decision.

Decision intelligence is a data-driven process that enables you to rapidly make faster, more accurate fact-based decisions rather than relying on intuition or gut feel. The approach combines decision-making techniques with AI, ML, contextual intelligence, and automation to generate actionable business recommendations. Rather than replacing human judgment, Decision Intelligence augments human ability to make better and more consistent decisions.

Diwo's Decision Intelligence Platform generates an opportunity as a result of its analysis. The platform applies business context to identify areas of performance improvement against specific business metrics. These opportunities serve as the foundation for business recommendations that address or explore potential areas of business improvement.

A decision intelligence recommendation represents a very specific, actionable suggestion for what decision should be taken to help achieve an improvement against a key business metric. These recommendations are characterized by being clear, measurable, and tailored to enhance specific business outcomes or initiatives.

Decision velocity is the use of data to rapidly make informed decisions. Through the combination of analytics, automation and AI, organizations can vastly improve decision accuracy and velocity.

Decision-making in a business context refers to frameworks used by managers and stakeholders to: identify a business challenge, collect and analyze relevant data, generate and evaluate possible solutions and their potential outcomes, choose and implement one course of action in business operations, and then monitor its progress and make changes as needed.

Descriptive analytics is a type of data analytics that looks at past data to help companies understand what has happened to date. Unlike other analytical approaches, it focuses exclusively on historical insights rather than making predictions or drawing inferences. Results are presented through reports, dashboards, bar charts, and other visual formats designed for easy interpretation.

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, "Why did it happen?" By using diagnostic analytics, companies can gain insights into the causes of patterns they've observed in their data. It can involve a variety of techniques, including drill-down, data discovery, data mining, and correlations.

Digital Intelligence is the set of key skills needed to adapt to and succeed in a world driven by technology and changing at break-neck speed. It encompasses foundational awareness of current and emerging digital technologies that can affect organizations — including cyber security, predictive analytics, AI, social media, online collaboration, and remote work technology. Rather than requiring deep expertise in specific areas, it emphasizes being open to applying this knowledge toward identifying and adopting new working methods and discovering fresh sources of business value.

Digital transformation involves integrating digital technology across all business functions, fundamentally altering operational methods and customer value delivery. Beyond technology implementation, it represents a cultural shift requiring organizations to continuously question existing practices, embrace experimentation, and accept failure as part of growth.

Evidence represents the pertinent data and analytical logic enabling users to authenticate recommendations from a decision intelligence system. It encompasses the insights that prompted the recommendation alongside supporting data utilized by models and the DI platform. Evidence frequently aligns with essential metrics and KPIs, incorporating projected impacts of recommendations expressed using the same measurement terms.

Explainable artificial intelligence (XAI) comprises processes and methods enabling users to comprehend and trust machine learning algorithm outputs. It describes AI models, their expected impact, and potential biases while characterizing model accuracy, fairness, transparency, and decision-making outcomes.

Graph databases allow data points to establish simultaneous connections with multiple other data points, unlike traditional relational databases that link to just one point at a time. This architecture enables users to discover relationships between data more readily—such as customer information from various sources or identifying family connections among multiple customers—resulting in speed and accuracy.

The Graph Intelligence platform enables data analysts to identify trends, detect outliers, and uncover connections within extensive datasets. It provides elastic performance at scale across four key capabilities: Graph Queries for contextual search, Graph Analytics for path finding and community detection, Graph Mining for pattern discovery, and Graph AI for prediction.

Hindsight analysis is the analysis of results after recommendations have been made, compared to the prediction value associated with the recommendation. It is a retrospective evaluation process that assesses how actual outcomes matched the predicted values from prior recommendations.

Human in the loop refers to the business user or analyst who is responsible for translating system-generated analytic output into business value. The human participant handles post-BI cognitive work, including discovering insights, linking them together, synthesizing findings, incorporating business context, determining optimal next actions, and ultimately making decisions.

Diwo's AI-driven Insight Engine framework delivers contextually relevant views into organizational data and business operations. The framework integrates with Diwo's Business Context graph to capture relationships between entities and their impact on decision-making. The engine analyzes trends and time-varying data changes, automatically surfacing and correlating actionable insights.

Intelligent automation (IA), also called cognitive automation, is the use of automation technologies — artificial intelligence (AI), business process management (BPM), and robotic process automation (RPA) — to streamline and scale decision-making across organizations.

A key performance indicator (KPI) is a quantifiable measure that shows how well a company or its products are performing against key strategic goals. KPIs provide targets for teams to shoot for, milestones to gauge progress, and insights that help people across the organization make better decisions.

A knowledge graph is a collection of linked entities (objects, events, concepts) that acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.

The Knowledge Management Module (KMM) represents a group of distributed microservices which collectively maintain and manage all the knowledge and associated data within Diwo's Decision Intelligence Platform. The module functions as an abstraction layer across disparate data sources, providing essential knowledge support to core microservices throughout the platform.

The last mile of analytics represents the concluding phase in the analytics workflow that connects insights to business outcomes through automation and collaboration to quantify, prioritize, and curate analytics with actionable bias. More specifically, it addresses the gap between analytical outputs — reports, charts, dashboards — and actual decision-making implementation. Currently, this step relies entirely on human cognitive processes to extract business value from analytics deliverables, making it the least automated step in the data and analytics value chain.

Machine learning represents a data analytics approach that harnesses artificial intelligence to emulate human learning from experience. Rather than depending on preset formulas, machine learning algorithms extract "knowledge" directly from data through computational methods. These algorithms identify inherent patterns within datasets that generate actionable insights and support improved forecasting and decision-making.

A model orchestrator is the software layer inside a Decision Intelligence platform that decides which AI or machine-learning model to invoke for a given decision, routes the request to that model (or sequence of models), coordinates the inputs and outputs across them, and returns a single coherent result to the caller. Modern enterprise decisions rarely call for one model — they require a pipeline: a forecasting model for demand, a causal model for lift estimation, a ranking model for prioritization, and a large language model for the narrative. The orchestrator is the conductor that runs this pipeline on every request.

Data architecture comprises the components that collectively fulfill an organization's data needs, including acquisition, storage, preparation, and analysis. Modern data architecture has been substantially shaped by concurrent advancements in big data, machine learning/AI, and cloud computing. It is designed proactively with scalability and flexibility in mind, anticipating complex data needs.

Natural Language Processing (NLP) is a user interface (UI) advancement that allows business users to query and interact with their data using their own natural language. The system leverages AI to comprehend both verbal and text-based communications, responding with analytics output in matching formats.

No-code represents an application development methodology that allows users to build application functionality without traditional programming. These frameworks enable users to define application design and workflow by connecting building blocks in a graphical interface, while selecting implementation details through menu-driven options.

The OODA loop (Observe, Orient, Decide, Act) is a framework for decision-making that emphasizes filtering available information, putting it in context, and quickly making the most appropriate decision while remaining adaptable as new data emerges. The process involves collecting relevant information, recognizing potential biases, deciding and acting, and understanding that adjustments can be made with additional data.

Predictive analytics employs data, statistical algorithms, and machine learning to assess the likelihood of future outcomes using historical information. Rather than merely documenting past events, it forecasts what will occur next, enabling executives and managers to adopt proactive, data-informed business strategies.

Prescriptive analytics is a type of data analytics — the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from immediate to long term.

Self-service BI is an analytics approach enabling business users to independently access and explore datasets without requiring specialized technical backgrounds in data mining or statistical analysis. The tools empower business analysts, executives, and other non-technical staff to execute queries and develop their own dashboards, reports, and data visualizations.