Do you really know the difference between Business Intelligence, Decision Intelligence, Decision Science, and Data Science? What about AI vs. Machine Learning? This helpful guide clarifies the medley of terms you need to know if you’re going to talk data and analytics in 2026.
This is a simplified form of a controlled, randomized experiment that compares two variants of a product, service, or process to determine which is the most effective. The main objective of an A/B test is to identify which variant produces the highest life in key KPIs.
Encompassing fully autonomous AI systems that rely on large language models, machine learning, and natural language processing to perform tasks on behalf of the user. As opposed to how Gen AI creates new content, agentic AI is solely focused on autonomous decision-making and action, oftentimes with no supervision required.
.Any technology or machine that can simulate human decision-making and perform complex tasks. The field of AI has been expanding quickly over recent years, causing the terminology to constantly expand and new subfields unveiled (such as machine learning, generative AI, agentic AI, etc.)
A statistical approach that incorporates prior beliefs and knowledge (prior distribution) about the parameters to determine the probability of observing a particular value. As observed data flows in, the probabilities are updated (posterior distribution) to provide a more nuanced interpretation of the results compared to traditional methods. This approach contains more flexibility, and the probabilistic predictions are treated as measures of belief rather than fixed point estimates.
Datasets are defined as Big Data based on the four V’s; if they are extremely large (volume), generate new data at a high-speed (velocity), contain a mix of structured and unstructured information (variety), and is potentially inaccurate, unreliable, or inapplicable (veracity). Data of this magnitude cannot be managed by traditional data-processing software and oftentimes requires distributed storage and processing tools to manage it. It is often used in machine learning and predictive modeling.
BI relies on tools (Tableau, Power BI, Qlik Sense, etc.) to gather, process, and present data to help interpret and communicate the story that lies within it. BI systems were designed to convert historical data into insights about the business, usually beginning with pre-defined questions and powered by traditional query analysis. The one thing BI cannot provide is a predictive lens into what should happen next. The predictive insights are what Decision Intelligence can deliver, and why every Data and Analytics business must have both capabilities.
Having a CRO strategy is imperative for marketers to drive revenue, get more value from existing visitors, and acquire more customers. It involves refining landing pages to maximize impact and ensuring an excellent experience on all devices. Most importantly, understanding your customer’s behaviors so your organization can leverage their high-conversion area.
An interdisciplinary field that meshes statistics, computer science, specialized programming, AI, and machine learning with subject matter expertise to extract knowledge and insights from data. Data science is incredibly versatile, with applications spanning across multiple industries. Beyond streamlined data exploration, data science paves the way predictive (e.g., forecasting) and descriptive (e.g., clustering techniques) data modeling.
DI equips organizations with predictive and prescriptive capabilities. By utilizing Artificial Intelligence (AI), Machine Learning (ML), and predictive modeling, Decision Intelligence can automate decisions, augment decision-making, and provide a roadmap for organizations’ next moves. DI is all about how to improve the decision-making process, effectively reducing the time-to-insight, and lifting a proportion of human effort required to analyze complex data.
A cumulation of quantitative techniques purposed to inform decision-making. Decision science expands upon data science to support business functions by focusing on operational problems using analysis methods and other statistical techniques. The core objective of decision science is to find the most optimal solution to support business decisions.
A subset of AI and machine learning that relies on layered neural network techniques to discover complex patterns in expansive datasets, unlike simpler machine learning models. Use case examples include powering modern AI applications, image recognition, enabling autonomous systems, etc.
A systematic methodology to solve business problems, validate assumptions, minimize risk, and inform decision-making in a business context. By leveraging traditional hypothesis testing, experimentation is one of the best methods for businesses to test ideas, assess alternative solutions, and provide insights to stakeholders.
This is likely what you remember from Stats 101. Frequentist methods are centered upon determining the frequency or proportion of data occurrences. In this instance, parameters are considered fixed values with unknown quantities. For example, when conducting hypothesis testing to determine whether to reject the null or not, a p-value will be calculated and will be used as the determinant in making this decision. In other words, frequentist methods rely solely on the data for making decisions.
A form of AI that can generate original content (such as text, images, audio, software code, and synthetic data) based upon a user’s prompt. Gen AI relies on machine learning, deep learning models, and other techniques to identify and encode patterns that power a repones to the user.
A subfield of AI that allows computers to “learn” and constantly improve their systematic performance when it is fed new data or context. Oftentimes machine learning is powered by algorithms to allow for a quick time-to-insight and continuous error reduction. Three different types of machine learning include supervised, unsupervised, and reinforcement learning.
By automating various actions such as “We Think You’ll Like” these related products, automation can help organizations streamline their personalization attempts. AI, natural language processing, and machine learning have advanced the ways automation technology drives value.
Is a form of ML automation that allows for a more dynamic version of an A/B test to run. The MAB algorithm effectively makes real-time adaptive decisions based on the best-performing variant during the test and allocates more traffic to that variant. While doing so, MAB is continuously pulling more samples and gathering more data.
Or multivariant testing (MVT), is a form of A/B testing that tests multiple variables, in different combinations simultaneously to determine which combination of variables performs the best. Full factorial designs and fractional factorial designs are common forms of MVT.
Using customer data and machine learning to deeply understand customer preferences and deliver individually tailored experiences. When done successfully, this process allows organizations to optimize their gain and surpass revenue goals because it increases relevancy to the target customer, conversion rates, and encourage customer retention.
Structured data follows a predefined model and is typically stored in rigid schemas. This form of data will typically fit neatly into rows and columns. Examples include datasets that are stored in relational databases or data warehouses and are easier to manipulate or analyze for the user.
A machine learning approach defined by its use of structured datasets to train or “supervise” AI algorithms into accurately reading from the data, being able to classify it, and predict outcomes. Under this approach, the algorithm acts like a student learning with an answer key. For example, image classification/recognition requires supervised learning for the system to classify objects based on trained features.
Data that is artificially generated by AI models or algorithms to act as a proxy for real-world data. Best used when real data is unavailable, when you need a known known truth for validation purposes, or for infrastructure work. However, synthetic data should not be treated as a substitute for real-world data as it is constrained by its generating assumptions and won’t always capture real-world messiness or edge-cases. It’s a powerful tool for specific scenarios (validation, privacy protection) but can be misleading if treated as equivalent to real-world data for decision-making
This approach emphasizes learning, not revenue. Test and Learn practices allow organizations to acquire evidence via small-scale testing to provide insight for making data-driven decisions, before committing to roll anything out full-scale. An example of this approach would be A/B testing, piloting, beta testing, etc.
Unstructured data tends to be more complex than structured data. It involves data that does not follow a predefined schema and is relatively difficult to store. This can involve a variety of high-volume and non-tabular datasets including elements such as documents, images, audio, video files, etc.
Unsupervised learning relies on machine learning algorithms to analyze datasets. This approach requires no need for human intervention and is utilized for three main tasks: clustering, dimensionality reduction, and association. A classic example of when the unsupervised learning method is in practice is the operation of agentic AI models.
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