IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 59

Data mining Machine Learning Deep Learning Marketing 59

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. IBM Data Governance IBM Data Governance leverages machine learning to collect and curate data assets.

Data Governance 142

Data Governance Management Metadata Data Quality 142

BizAcuity

MARCH 1, 2019

Traditional methods of analyzing structured data are not designed to efficiently process these large amounts of real-time data that is collected from IoT devices. This is where AI-based analysis and response play a critical role in extracting optimal value from the data. and constantly report this data to backend.

IoT 56

IoT Internet of Things Big Data Data-driven 56

Alation

MAY 16, 2023

Data Integration A data pipeline can be used to gather data from various disparate sources in one data store. This makes it easier to compare and contrast information and provides organizations with a unified view of their data. It’s worth noting that a data pipeline may have more than one data source.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Timo Elliott

JANUARY 22, 2019

Machine learning everywhere. We’ve reached the third great wave of analytics, after semantic-layer business intelligence platforms in the 90s and data discovery in the 2000s. Augmented analytics platforms based on cloud technology and machine learning are breaking down the longest-standing barriers to analytics success.

Analytics 89

Analytics Machine Learning Unstructured Data Digital Transformation 89

Alation

MAY 16, 2023

Data Integration A data pipeline can be used to gather data from various disparate sources in one data store. This makes it easier to compare and contrast information and provides organizations with a unified view of their data. It’s worth noting that a data pipeline may have more than one data source.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Alation

FEBRUARY 20, 2020

By dramatically lowering the cost of storing data for analysis, it ushered in an era of massive data collection. By changing the cost structure of collecting data, it increased the volume of data stored in every organization.

Data Lake 52

Data Lake Metadata Structured Data Big Data 52

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 72

Data Strategy Strategy Data Warehouse Prescriptive Analytics 72

FineReport

JULY 18, 2022

Most data analysts are very familiar with Excel because of its simple operation and powerful data collection, storage, and analysis. Key features: Excel has basic features such as data calculation which is suitable for simple data analysis. Its core product Qlik Sense can connect data from numerous data sources.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 261

Machine Learning Software Testing Metadata 261

Jet Global

MAY 9, 2024

Data Migration Pipelines : These pipelines move data from one system to another, often for the purpose of upgrading systems or consolidating data sources. For example, migrating customer data from an on-premises database to a cloud-based CRM system. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52