Decision Intelligence Key Terms

Artificial intelligence (AI) uses advanced algorithms and machine learning to process large amounts of data quickly and accurately, driving better insights and efficiencies. Business leaders can use AI to support employee decisions at any level of their organizations.

Augmented analytics is the use of enabling technologies like machine learning and artificial intelligence to assist with data preparation and insights; this augments how people explore and analyze data. With augmented analytics, instead of requiring your data scientists to provide analytics for other employees, they can work on what they were hired to do; business experts analyze their data on their own unassisted.

Automated insights are a function of modern analytics platforms, including some business intelligence (BI) and decision intelligence (DI) platforms. They enable virtually any individual to quickly formulate insights from even complex datasets without technical expertise or even substantial effort.

Business analytics is the process of analyzing data to gain insights that can improve business performance. Organizations can use business analytics for a wide range of purposes, from identifying internal inefficiencies to predicting future trends. Decision intelligence is the next evolutionary stage of business analytics and focuses on helping organizations make better decisions.

Business intelligence (BI) is a process that turns data into insights to help organizations make better business decisions. BI as a practice has existed for decades, but it has evolved to become more sophisticated and effective with the emergence and evolution of analytics technologies.

Business modeling helps organizations understand how their data relates to business objectives, producing models that identify opportunities and strategies for competitive advantages. Driven by data queries associated with analytics, it creates a common language for informed decision-making, improving visibility, forecasting accuracy, and identifying opportunities or threats in changing markets.

Data cleansing is the process of identifying and correcting inaccurate or incomplete data within a dataset. Data cleansing typically occurs after an organization identifies and gains access to new data sources but before data from those sources is used in their analytics processes.

Data governance is the process of managing data within an organization. This includes specifying who has access to data, what type of data they have access to, and how that data can be used. Data governance technologies and applications can help organizations improve the quality of their data, protect their data from unauthorized access and use, and manage their data more effectively.

A data model is a framework that describes the structure and organization of data in a database or other system. This typically includes information about entities, attributes, and relationships, among others. Different types of data models are used for different purposes, such as relational data models for CRM and forecasting data models for predictive analytics.

Data preparation is the process of transforming raw data into a format that is ready for analysis. This can involve cleaning up, standardizing, and formatting data to make it easy to analyze, typically with the support of an analytics tool.

Data science involves extracting knowledge and insights from both structured and unstructured data. Advanced analytics platforms make this process easier for nontechnical teams by automating such tasks as extraction, model building, and visualization.

Data visualization is the graphic representation of raw data. It enables employees to quickly identify patterns and trends from data sets. Data visualization contributes to better decisions, data literacy, and unlocking the full value of data.

Decision intelligence (DI) is the set of capabilities—whether human, organizational, or technological—used to enable timely and effective decision-making. Broad organizational access to technologies used for DI leads to faster, more effective decision-making at scale.

Digital transformation is the process by which organizations phase out, adopt, and incorporate digital technologies within any variety of business functions. Use cases include data security, customer experience management, risk management, operational improvements, and employee collaboration, among others. Organizations that successfully embrace digital transformation often see improved decision-making, faster time to market for new products and services, increased efficiency, and better collaboration across teams—among other potential benefits.

Direct query is a data access technique that enables people to issue queries against a data source and receive results in real-time. Direct query tools vary in their complexity, but they typically allow wider access to a variety of data sources that wouldn’t be possible with traditional BI approaches.

Embedded analytics describes analytical capabilities embedded within a purpose-built application. Embedded analytics can improve efficiencies and processes because it enables people to take advantage of analytics’s power within the application they are already working in. These capabilities can be integrated directly into proprietary and off-the-shelf systems and cloud-based software.

An in-memory database is a type of database that stores data in RAM for quick access and low latency. In-memory databases are commonly used in business intelligence tools to provide users with real-time insights from data analysis.

Machine learning is a subset of artificial intelligence (AI). It is a type of artificial intelligence that uses data and algorithms to imitate how humans learn. Using statistical methods and techniques, humans create algorithms that can be trained to classify data or make predictions to uncover key insights.

Predictive analytics uses data mining, machine learning (ML), and statistical analysis to make predictions about future events, enabling organizations to make informed decisions and improve efficiency.

Prescriptive analytics is a type of advanced business analytics that uses artificial intelligence (AI) and algorithms to analyze problems and recommend actions to achieve goals. It helps organizations increase efficiency, reduce risk, and improve decision-making.

Self-service analytics is a powerful tool for improving decision-making across all levels of an organization. It enables businesses to be more agile, collaborative, and data-driven by giving employees access to data and insights in an intuitive, self-service manner. Many companies today are turning to self-service analytics as part of broader decision intelligence initiatives.