Advanced analytics uses sophisticated techniques to uncover insights, identify patterns, predict outcomes, and generate advanced recommendations.
...Can agentic AI deliver decisions that are faster, more adaptive, and more resilient than traditional automation?
...Learn what agentic workflows are, how they work, and how they combine automation, analytics, and AI agents to streamline decision-making and business processes.
...Discover how AI analytics transforms data into actionable insights. Boost decision-making and stay competitive in today’s business landscape.
...Explore clear definitions of AI governance terms. This glossary helps readers understand responsible AI, ethics, and compliance concepts.
...Learn what an AI tech stack is and how its layered set of technology components form an ecosystem that enables organizations to operationalize AI at scale.
...Learn how AI-ready data boosts ROI by enabling faster insights, smarter decisions, and more reliable outcomes.
...Discover how analytics automation streamlines data tasks, boosts insights, and drives smarter decisions with less manual effort.
...Learn what an analytics maturity model is and how assessing data and analytics capabilities boosts performance, efficiency, and business outcomes.
...Artificial Intelligence (AI) is when computers perform tasks that usually need human thinking, like spotting patterns, making predictions, or automating decisions. Companies use AI to save time, work smarter, and make faster, better choices across many industries.
...Artificial intelligence for IT operations (AIOps) is a predictive, proactive technology approach that integrates data analytics, automation, and AI across complex IT environments. It improves how IT systems are monitored, managed, and optimized by applying machine learning (ML) and advanced analy ...
Automated machine learning, or AutoML, makes ML accessible to non-experts by enabling them to build, validate, iterate, and explore ML models through an automated experience.
...Bias in AI refers to systematic errors in algorithms or datasets that result in unfair, inaccurate, or unbalanced outcomes. It happens when AI systems reflect or amplify the biases found in their training data, design, or deployment environments.
...Business analytics is the process of using data to identify patterns, evaluate performance, and guide better business decisions. It combines statistical analysis, data visualization, and predictive modeling to turn raw information into actionable insights.
...Business intelligence is the cumulative outcome of an organization’s data, software, infrastructure, business processes, and human intuition that delivers actionable insights.
...Cloud analytics involves both using data stored in the cloud for analytic processes and leveraging the fast computing power of the cloud for faster analytics.
...Learn what cloud data integration is, how it works, and how organizations use it to connect, transform, and manage data across hybrid and multi-cloud environments.
...Learn what cloud data management is, how it works, and how organizations across industries apply it to break down data silos and bolster analytics.
...Learn how cloud data platforms boost ROI by streamlining data management, enhancing scalability, and supporting AI-driven insights.
...A cloud data warehouse is a database that is managed as a service and delivered by a third party, such as Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure. Cloud data architectures are distinct from on-premise data architectures, where organizations manage their own phys ...
Customer journey analytics (CJA) is the process of analyzing customer interactions across every channel and touchpoint to reveal patterns, behaviors, and opportunities to improve customer experience. By combining data from marketing, sales, service, and digital systems, organizations can see wher ...
Learn what data aggregation is and how combining and summarizing data from multiple sources helps businesses improve analytics accuracy and reporting.
...Explore the power of data analytics to uncover patterns, drive smarter choices, and create lasting business impact.
...Data applications are applications built on top of databases that solve a niche data problem and, by means of a visual interface, allow for multiple queries at the same time to explore and interact with that data. Data applications do not require coding knowledge in order to procure or understand ...
Data blending is the act of bringing data together from a wide variety of sources into one useful dataset to perform deeper, more complex analyses.
...A data catalog is a comprehensive collection of an organization’s data assets, which are compiled to make it easier for professionals across the organization to locate the data they need. Just as book catalogs help users quickly locate books in libraries, data catalogs help users quickly search ...
Data cleansing is the process of finding and fixing inaccurate, incomplete, or duplicate information in a data set. It improves data quality by ensuring that data is accurate, consistent, and ready to support analytics, automation, and better business decisions.
...Data compliance ensures your organization meets all legal, regulatory, and industry requirements for collecting, storing, processing, and protecting personal and sensitive data while maintaining business operations.
...A data connector is a software component or integration tool that enables different systems, applications, or databases to exchange data seamlessly. It acts as a bridge between sources like CRMs, cloud storage, APIs, or analytics platforms and allows data to flow automatically without manual expo ...
Data democratization is about removing barriers so that everyone — not just IT or data scientists — can access, understand, and act on data. Organizations pursue it to speed decisions, increase agility, and create a culture where insights fuel every function. In practice, democratizing data m ...
Learn what a data dictionary is and how defining and documenting data elements helps teams stay consistent, collaborate better, and trust their data.
...Data enrichment is a data management process that augments existing data sets by adding relevant information from internal or external sources to make them more robust, accurate, and valuable. It goes beyond simple data collection to add context, attributes, and meaning that help organizations be ...
Data exploration is one of the initial steps in the analysis process that is used to begin exploring and determining what patterns and trends are found in the dataset. An analyst will usually begin data exploration by using data visualization techniques and other tools to describe the characteris ...
Learn what data extraction is and how automating data collection from multiple sources improves accuracy, saves time, and powers better analytics.
...Data governance is the set of rules, processes, and responsibilities that ensure an organization’s data is accurate, secure, usable, and compliant. It provides the guardrails that let organizations protect their data while enabling teams to use it confidently for decision-making.
...A data hub is a centralized architecture that consolidates, integrates, and governs key data assets — such as customer, product, or operational data — from multiple systems. Unlike a traditional data warehouse or a data lake, a data hub emphasizes connectivity, real-time access, domain autono ...
Data ingestion is the process of bringing data together from multiple sources — like apps, databases, APIs, and external feeds — into one place where it can be stored, analyzed, and used. It’s the first step in building a data pipeline, helping organizations move information efficiently int ...
Data integrity refers to the accuracy and consistency of data over its entire lifecycle, as well as compliance with necessary permissioning constraints and other security measures. In short, it is the trustworthiness of your data.
...A data lakehouse is a data management architecture that seeks to combine the strengths of data lakes with the strengths of data warehouses.
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Data lineage tracks and visualizes how data moves and changes throughout its lifecycle, from its source to its final destination. It maps where data originates, how it transforms, and where it’s used, enabling transparency, accountability, and trust across the data ecosystem.
...Data mining is the process of discovering significant patterns, relationships, and trends in large, raw data sets to guide better business decisions. It combines statistics, machine learning, and artificial intelligence to identify valuable insights that might not be visible otherwise.
...Discover how data modeling structures information for clarity, consistency, and better decision-making across your organization.
...Data munging is the process of transforming and preparing data from its original, often unstructured state into a clean, organized format suitable for analysis. It involves collecting, cleaning, reshaping, and enriching data so it can be easily used in analytics, reporting, or machine learning.
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Data observability refers to the ability of an organization to monitor, track, and make recommendations about what’s happening inside their data systems in order to maintain system health and reduce downtime. Its objective is to ensure that data pipelines are productive and can continue running ...
Data onboarding is the process of preparing and uploading customer data into an online environment. It allows organizations to bring customer records gathered through offline means into online systems, such as CRMs. Data onboarding requires significant data cleansing to correct for errors and for ...
A data pipeline is a sequence of steps that collect, process, and move data between sources for storage, analytics, machine learning, or other uses. For example, data pipelines are often used to send data from applications to storage ...
Discover what data preparation is, why it matters in analytics and AI workflows, and how organizations streamline data cleaning, transformation, and enrichment to make data ready for insights.
...Learn what data profiling is and how analyzing data quality helps organizations uncover errors, improve accuracy, and build trust in their data.
...Learn what data quality is, why it matters for analytics and AI, and how organizations assess, improve, and maintain the reliability of their information.
...Data science is a form of applied statistics that incorporates elements of computer science and mathematics to extract insight from both quantitative and qualitative data.
...Data science and machine learning are buzzwords in the technology world. Both enhance AI operations across the business and industry spectrum. But which is best?
...Data security protects sensitive information through policies, technologies, and controls that prevent breaches and misuse. It also helps organizations cut risk, build trust, and stay compliant with regulations like GDPR and HIPAA.
...A data source is the digital or physical location where data originates from or is stored, which influences how it is stored per its location (e.g. data table or data object) and its connectivity properties.
...Data standardization abstracts away all the complex semantics of how data is captured, standardized, and cobbled together to provide businesses with faster and more accurate analytics.
...A data steward is the professional responsible for ensuring that an organization’s data assets are accurate, consistent, secure, and aligned with established governance policies. Their work bridges business needs and technical delivery, helping teams trust and effectively use enterprise data.
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Learn what data structure is and how organizing and storing data efficiently boosts analytics performance, data integrity, and faster business decisions.
...Data transformation is the process of converting data into a different format that is more useful to an organization. It is used to standardize data between data sets, or to make data more useful for analysis and machine learning. The most common data transformations involve converting raw data i ...
Data validation is the process of checking data for accuracy, consistency, and integrity before it’s used in analysis, reporting, or decision-making. It ensures that information meets the right rules, formats, and standards, helping teams maintain high data quality, avoid costly errors, and bui ...
Data visualization is the visual representation of data by using graphs, charts, plots or information graphics.
...Learn what data wrangling is and how cleaning, structuring, and enriching data from multiple sources improves analytics accuracy and business insight.
...Decision intelligence is the process of applying analytics, AI and automation to decisions that impact< ...
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Demand forecasts estimate future demand for products and services, which helps inform business decisions. Demand forecasts include granular data, historical sales data, questionnaires, and more.
...Descriptive analytics answers the question “What happened?” by drawing conclusions from large, raw datasets. The findings are then visualized into accessible line graphs, tables, pie and bar charts, and generated narratives.
...Learn what dirty data really is, how it happens, and most importantly — how to prevent it so your organization can operate at top speed with agility.
...Embedded analytics is the integration of data analysis and data visualization capabilities directly into existing business applications, systems, or workflows. Instead of switching among platforms to access insights, users can view and interact with analytics within the tools they already use — ...
An ETL Developer is an IT specialist who designs, develops, automates, and supports complex applications to extract, transform, and load data. An ETL Developer plays an important role in determining the data storage needs of their organization.
...Explainable AI (XAI) refers to techniques and methods that make the decision-making processes of AI systems understandable to humans. Its goal is to reveal how models arrive at outputs so that users, regulators, and organizations can trust, verify, and govern those decisions.
...Extract, transform, load (ETL) is a core data integration process that enables organizations to collect data from multiple sources, clean and organize it, and load it into a central data storage location, such as a data warehouse or data lake, for analysis. ETL ensures that data is accurate, cons ...
Feature engineering is the process of creating, selecting, or transforming the variables — known as features — that a machine learning model uses to learn patterns and make predictions. These features help the model understand relationships in the data more clearly, improving its accuracy and ...
Generative AI (GenAI) helps organizations quickly turn data into useful outputs like reports, insights, or even workflow suggestions. By learning from existing data, it reduces manual effort and makes advanced analytics more accessible.
...Key performance indicators (KPIs) are quantifiable measures that reflect the critical success factors of an organization or a specific business function. They help track progress toward strategic goals, align teams around measurable objectives, and focus attention on what matters most.
...Learn what a large language model is and how it supports B2B teams with AI-powered insights and applications.
...Machine learning is a branch of artificial intelligence that enables computers to identify patterns, make predictions, and improve performance without being explicitly programmed. It helps organizations uncover insights, automate complex tasks, and support faster, more accurate decision-making.
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A machine learning pipeline (ML pipeline) is a repeatable workflow that organizes and automates every step of the model lifecycle from preparing input data to training, deploying, and evaluating a machine learning model. Instead of handling these tasks manually, an ML pipeline streamlines the ent ...
Master Data Management (MDM) is the practice of creating a trusted, consolidated view of an organization’s critical data — such as customers, products, suppliers, and employees — across systems and teams. It provides the structure and governance needed so that core data is accurate, consist ...
Machine learning models (MLOps) provide valuable insights to the business, but only if those models can access and analyze the organization’s data on an ongoing basis. MLOps is the critical process that makes this possible.
...Objectives and key results (OKRs) are a goal-setting framework that helps organizations define clear, measurable outcomes and track progress toward strategic priorities. In analytics and data-driven organizations, OKRs align teams around specific business outcomes, turning data insights into focu ...
Predictive AI uses historical and real-time data, machine learning models, and statistical techniques to forecast future outcomes and support data-driven decision-making.
...Predictive analytics is a type of data analysis that uses statistics, data science, machine learning, and other techniques to predict what will happen in the future.
...Discover how prescriptive analytics helps businesses optimize strategy, predict outcomes, and take data-driven action for growth.
...Understand what quantitative data means and how it fuels smarter business strategies through measurable insights.
...A regex (short for regular expression) is a sequence of characters used to specify a search pattern. It allows users to easily conduct searches matching very specific criteria, saving large amounts of time for those who regularly work with text or analyze large volumes of data. An example of a re ...
Learn how Retrieval Augmented Generation (RAG) boosts AI accuracy by blending search and generation for smarter, faster results.
...Learn what role-based access control (RBAC) is and how assigning permissions by user role strengthens data security, governance, and compliance across systems.
...Sales analytics is the practice of generating insights from data and used to set goals, metrics, and a larger strategy.
...Self-service analytics are a modern approach to business intelligence that allows non-technical users to independently access, analyze, and visualize data without relying on IT or data specialists. By democratizing data and automating access through governed analytics tools, it enables faster, da ...
Source-to-target mapping (STM) is the practice of documenting how data fields from one or more source systems correspond to fields in a destination system. It helps teams see exactly which data moves, how it transforms, and how it will be used in reporting, analytics, or downstream applications.< ...
Spatial analysis models problems geographically, allowing a company to analyze the locations, relationships, attributes, and proximities in geospatial data to answer questions and develop insights.
...Spatial analytics helps organizations understand their data in relation to physical location. Instead of looking only at what is happening, spatial analytics adds the context of where it’s happening — revealing geographic patterns and relationships that lead to smarter, faster business decisi ...
Supervised and unsupervised learning have one key difference. Supervised learning uses labeled datasets, whereas unsupervised learning uses unlabeled datasets.
...Learn what synthetic data generation is and how creating privacy-safe, AI-ready data helps teams accelerate analytics, improve models, and innovate faster.
...Systems of intelligence help organizations extract value from their tech stack by creating a highly accessible single source of data-driven insights from their systems of record to support strategic decision-making.
...Telemetry data is information automatically collected from systems, devices, or applications and sent to a central platform for monitoring and analysis. It gives teams real-time visibility into system performance by capturing signals like usage patterns, health indicators, performance metrics, se ...
A User Defined Function (UDF) is a custom programming function that allows users to reuse processes without having to rewrite code. For example, a complex calculation can be programmed using SQL and stored as a UDF. When this calculation needs to be used in the future on a different set of data, ...
