IBM Big Data Hub

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

Ontotext

JUNE 22, 2023

All derived facts can be further put into context with structured data, which improves data quality and presents researchers with clear evidence and provenance for all insights Then, Ontotext’s Target Discovery provides deeper insights into the data stored in this highly-interlinked knowledge graph, where long sequences of relations can be mined.

Metrics 52

Metrics Statistics Visualization Metadata 52

CIO Business Intelligence

JUNE 14, 2023

Admittedly, it’s still pretty difficult to visualize this difference. In the case of intelligent operations, real-time data informs immediate operational decisions. An airline carrier needs to know how many gates are open and how many passengers are on each plane – metrics that change from moment to moment. Consider data types.

Unstructured Data 62

Unstructured Data IT Manufacturing Visualization 62

AWS Big Data

MARCH 26, 2024

The following figure shows some of the metrics derived from the study. You can use the same capabilities to serve financial reporting, measure operational performance, or even monetize data assets. The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development.

Metadata 59

Metadata Data Quality Statistics Data Science 59

Alation

FEBRUARY 20, 2020

By changing the cost structure of collecting data, it increased the volume of data stored in every organization. Additionally, Hadoop removed the requirement to model or structure data when writing to a physical store.

Data Lake 52

Data Lake Metadata Structured Data Big Data 52

FineReport

MARCH 7, 2024

Data Acquisition I categorize data sources into three types: (1) First-party data: User factual data, such as financial products purchased at a certain institution, time of purchase, issuing branch, name, phone number, or operational data, such as user behavioral data on a financial app. (2)

Dashboards 52

Dashboards Reporting Advertising Statistics 52

AWS Big Data

MARCH 25, 2024

Stream processing, however, can enable the chatbot to access real-time data and adapt to changes in availability and price, providing the best guidance to the customer and enhancing the customer experience. When the model finds an anomaly or abnormal metric value, it should immediately produce an alert and notify the operator.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

Jet Global

MAY 9, 2024

Real-Time Analytics Pipelines : These pipelines process and analyze data in real-time or near-real-time to support decision-making in applications such as fraud detection, monitoring IoT devices, and providing personalized recommendations. For example, migrating customer data from an on-premises database to a cloud-based CRM system.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

AWS Big Data

MARCH 7, 2023

You can use simple SQL to analyze structured and semi-structured data across data warehouses, data marts, operational databases, and data lakes to deliver the best price performance at any scale. Data in Amazon S3 can be easily queried in place using SQL with Amazon Redshift Spectrum.

Analytics 74

Analytics Data Warehouse Data Lake Metadata 74

O'Reilly on Data

APRIL 19, 2021

We also asked what kinds of data our “mature” respondents are using. Most (83%) are using structured data (logfiles, time series data, geospatial data). form data). We’d expect most business applications to involve structured data, form data, or text data of some kind.

Enterprise 353

Enterprise Modeling Risk Manufacturing 353

Octopai

FEBRUARY 8, 2020

The two teams (Lockheed Martin and NASA Jet Propulsion Laboratory) that built the thrusters miscommunicated units (English to metric). But when you have a complete set of BI tools , you can get to know your data from multiple angles and drive improved decisions on how to use the data. . So, the software miscalculated.

Metadata 63

Metadata Key Performance Indicator Unstructured Data Business Intelligence 63

Sisense

JUNE 8, 2020

“This ability to pull data sets together from a cloud-native environment for better transparency and faster decision making, in turn, can give analysts the speed they need to answer questions quickly and more accurately with more data collaboration,” Lee added. Deeper insights from bigger data sets.

Data Warehouse 89

Data Warehouse Data-driven Dashboards Sales 89

AWS Big Data

DECEMBER 21, 2023

For the downstream consumption by all departments across the organization, smava’s Data Platform team prepares curated data products following the extract, load, and transform (ELT) pattern. This is the Data Mart stage. The data products from the Business Vault and Data Mart stages are now available for consumers.

Data Lake 91

Data Lake Data Warehouse Data-driven B2B 91

Sisense

MAY 11, 2021

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

IBM Big Data Hub

AUGUST 28, 2023

Data extraction Once you’ve assigned numerical values, you will apply one or more text-mining techniques to the structured data to extract insights from social media data. Data analysis and interpretation The next step is to examine the extracted patterns, trends and insights to develop meaningful conclusions.

Data mining 54

Data mining Machine Learning Deep Learning Marketing 54

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? There are essentially four types encountered: image/video, audio, text, and structured data. Note how model visualization is bubbling up to the top, which has implications for model interpretability, cyber threats, etc. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49