📊Business Intelligence: Advanced Analytics (Definitions)

"A subset of analytical techniques that, among other things, often uses statistical methods to identify and quantify the influence and significance of relationships between items of interest, groups similar items together, creates predictions, and identifies mathematical optimal or near-optimal answers to business problems." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Algorithms for complex analysis of either structured or unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text analytics, and other advanced data-mining techniques Advanced analytics does not include database query and reporting and OLAP cubes." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A subset of analytical techniques that, among other things, often uses statistical methods to identify and quantify the influence and significant of relationships between items of interest, group similar items together, create predictions, and identify mathematical optimal or near-optimal answers to business problems." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks. (Gartner)

"Analytic techniques and technologies that apply statistical and/or machine learning algorithms that allow firms to discover, evaluate, and optimize models that reveal and/or predict new insights." (Forrester)

"Advanced analytics describes data analysis that goes beyond simple mathematical calculations such as sums and averages, or filtering and sorting. Advanced analyses use mathematical and statistical formulas and algorithms to generate new information, to recognize patterns, and also to predict outcomes and their respective probabilities." (BI-Survey) [source]

"Advanced analytics is an umbrella term for a group of high-level methods and tools that can help you get more out of your data. The predictive capabilities of advanced analytics can be used to forecast trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as 'what-if' calculations, as well as to future-proof various aspects of their operations." (Sisense) [source]

📊Business Intelligence: Data Ingestion (Defintions)

"Data ingestion is the first step in the data engineering lifecycle. It involves gathering data from diverse sources such as databases, SaaS applications, file sources, APIs and IoT devices into a centralized repository like a data lake, data warehouse or lakehouse. This enables organizations to clean and unify the data to leverage analytics and AI for data-driven decision-making." (Databricks) [link]

"Data ingestion is the import and collection of data from databases, APIs, sensors, logs, files, or other sources into a centralized storage or computing system. Data ingestion and transformation renders massive collections of data accessible and usable for analysis, processing, and visualization. It’s a fundamental step in data management and analytics workflows, enabling organizations to glean insights from their data." (ScyllaDB) [link] 

"Data ingestion is the process of collecting data from one or more sources and loading it into a staging area or object store for further processing and analysis. Ingestion is the first step of analytics-related data pipelines, where data is collected, loaded and transformed for insights." (Fivetran) [link] 

"Data ingestion is the process of collecting and importing data files from various sources into a database for storage, processing and analysis." (IBM) [link]

"Data ingestion is the process of transporting data from one or more sources to a target site for further processing and analysis. This data can originate from a range of sources, including data lakes, IoT devices, on-premises databases, and SaaS apps, and end up in different target environments, such as cloud data warehouses or data marts." (Striim) [link] 

"Data ingestion is the process of importing large, assorted data files from multiple sources into a single, cloud-based storage medium - a data warehouse, data mart or database - where it can be accessed and analyzed." (Cognizant) [link] 

"Data ingestion is the process of moving and replicating data from data sources to destination such as a cloud data lake or cloud data warehouse." (Informatica) [link] 

"Data ingestion refers to the tools & processes used to collect data from various sources and move it to a target site, either in batches or in real-time." (Qlik) [link]

"Data ingestion refers to collecting and importing data from multiple sources and moving it to a destination to be stored, processed, and analyzed." (Teradata) [link] 

"The process of obtaining, importing, and processing data for later use or storage in a database. This process often involves altering individual files by editing their content and/or formatting them to fit into a larger document. An effective data ingestion methodology begins by validating the individual files, then prioritizes the sources for optimum processing, and finally validates the results. When numerous data sources exist in diverse formats (the sources may number in the hundreds and the formats in the dozens), maintaining reasonable speed and efficiency can become a major challenge. To that end, several vendors offer programs tailored to the task of data ingestion in specific applications or environments.' (CODATA)

📊Business Intelligence: Report Snapshot (Definitions)

"A SQL Server Reporting Services report that contains data that was queried at a particular point in time and has been stored on the Report Server." (Victor Isakov et al, "MCITP Administrator: Microsoft SQL Server 2005 Optimization and Maintenance (70-444) Study Guide", 2007)

"A report that contains data captured at a specific point in time. Since report snapshots hold datasets instead of queries, report snapshots can be used to limit processing costs by running the snapshot during off-peak times." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"A report that contains data captured at a specific point in time. A report snapshot is stored in an intermediate format containing retrieved data rather than a query and rendering definitions." (Jim Joseph et al, "Microsoft® SQL Server™ 2008 Reporting Services Unleashed", 2009)

"A static report that contains data captured at a specific point in time." (Microsoft, "SQL Server 2012 Glossary", 2012)