Data Science and Data Analytics Glossary of Terms

Data Prep and Analytics

Analytics Maturity Model

The higher your organization’s level of analytics maturity, the more capable it is of using data to deliver business outcomes.

Business Analytics

Business analytics is the process analyzing data using statistical and quantitative methods to make decisions that drive better business outcomes.

Business Intelligence

Business intelligence is the cumulative outcome of an organization's data, software, infrastructure, business processes, and human intuition that delivers actionable insights.

Cloud Analytics

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.

Data Analytics

Data analytics is the process of exploring, transforming, and analyzing data to identify meaningful insights and efficiencies that support decision-making.

Data Blending

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.

Data Catalog

How a Data Catalog Helps an Organization Make the Best Use of Data Assets

Data Cleansing

Data cleansing, also known as data cleaning or scrubbing, identifies and fixes errors, duplicates, and irrelevant data from a raw dataset.

Data Exploration

Data exploration is a way to get to know data before working with it. Through survey and investigation, large datasets are readied for deeper, more structured analysis.

Data Governance

Learn what data governance is, the tools and framework used to implement it, and how you can use it to drive value.

Data Lineage

Track where an organization’s data comes from, the journey it takes through the system, and keep business data compliant and accurate.

Data Preparation

Data preparation is the act of cleaning and consolidating raw data prior to using it for business analysis. Learn why it's critical and how it works.

Data Profiling

Data profiling helps discover, understand, and organize data by identifying its characteristics and assessing its quality.

Data Standardization

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.

Data Wrangling

Data wrangling is the act of transforming, cleansing, and enriching data to make it more applicable, consumable, and useful to make smarter business decisions.

Demand Forecasting

Accurate demand forecasts help you with inventory management, capacity planning, product demand, and resource allocation.

Descriptive Analytics

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.

ETL

ETL is the process used to copy, combine, and convert data from different sources and formats and load it into a new destination such as a data warehouse or data lake.

Source-to-Target Mapping

Source-to-Target Mapping is a set of data transformation instructions that determine how to convert the structure and content of data in the source system to the structure and content needed in the target system.

Spatial Analytics

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.

Advanced Analytics

Advanced analytics uses sophisticated techniques to uncover insights, identify patterns, predict outcomes, and generate recommendations.

AutoML

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.

Data Science

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 vs Machine Learning

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?

Feature Engineering

With feature engineering, organizations can make sense of their data and turn it into something beneficial.

Machine Learning

Machine learning is the iterative process a computer uses to identify patterns in a dataset given specific constraints.

MLOps Machine Learning Operations

MLOps is a cross-functional, collaborative, and iterative process that operationalizes data science by managing machine learning (ML) and other types of models to be reusable software artifacts that can be deployed and continuously monitored via a repeatable process.

Predictive Analytics

Predictive analytics is a type of data analysis that uses machine learning, statistical algorithms, and other techniques to predict what will happen in the future.

Prescriptive Analytics

Prescriptive analytics answers the question “What should/can be done?” by using machine learning, graph analysis, simulation, heuristics, and other methods.

Supervised vs Unsupervised Learning

Supervised and unsupervised learning models work in unique ways to help businesses better engage with their consumers.

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Data Science and Data Analytics Glossary