CIO Business Intelligence

JUNE 28, 2022

For example, they may not be easy to apply or simple to comprehend but thanks to bench scientists and mathematicians alike, companies now have a range of logistical frameworks for analyzing data and coming to conclusions. More importantly, we also have statistical models that draw error bars that delineate the limits of our analysis.

Management 136

Management Internet of Things Statistics Data-driven 136

Alation

JUNE 2, 2022

A data catalog is a central hub for XAI and understanding data and related models. While “operational exhaust” arrived primarily as structured data, today’s corpus of data can include so-called unstructured data. Other Technologies. Recently, Judea Pearl said, “All ML is just curve fitting.”

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

BizAcuity

OCTOBER 21, 2019

Text analytics helps to draw the insights from the unstructured data. Text Analytics – is a process of turning unstructured text – available in the form of tweets, comments, reviews, etc. into structured data to develop actionable managerial insights to enhance their operations.

Analytics 40

Analytics Unstructured Data Data mining Structured Data 40

Cloudera

JUNE 25, 2021

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. HDFS files which are used by tables.

Data Lake 72

Data Lake Metadata Unstructured Data Management 72

Analytics Vidhya

MAY 29, 2023

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Both are associated with data usage but differ from each other.

Data mining 261

Data mining Data Collection Strategy Data Analytics 261

Smarten

MAY 17, 2017

Smart Data Visualization goes beyond data display to suggest options for visualization and plotting for certain types of data, based on the nature, dimensions and trends inherent in the data. What is the difference between viewing structured data and compiling and analyzing unstructured data?

Visualization 40

Visualization Business Intelligence Unstructured Data Interactive 40

Domino Data Lab

AUGUST 9, 2021

Machine learning identifies patterns in data using algorithms that are primarily based on traditional methods of statistical learning. It’s most helpful in analyzing structured data. Based on the concept of neural networks, it’s useful for analyzing images, videos, text and other unstructured data.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Cloudera

NOVEMBER 25, 2020

Sample and treatment history data is mostly structured, using analytics engines that use well-known, standard SQL. Interview notes, patient information, and treatment history is a mixed set of semi-structured and unstructured data, often only accessed using proprietary, or less known, techniques and languages.

Data Warehouse 59

Data Warehouse Unstructured Data Analytics Visualization 59

CIO Business Intelligence

APRIL 4, 2023

Let’s look at the data architecture journey to understand why and how data lakehouses help to solve complexity, value and security. Traditionally, data warehouses have stored curated, structured data to support analytics and business intelligence, with fast, easy access to data. Want to learn more?

Data Architecture 52

Data Architecture Data Lake Data Warehouse Cost-Benefit 52

Sisense

JUNE 30, 2020

Data is usually visualized in a pictorial or graphical form such as charts, graphs, lists, maps, and comprehensive dashboards that combine these multiple formats. Data visualization is used to make the consuming, interpreting, and understanding data as simple as possible, and to make it easier to derive insights from data.

Visualization 93

Visualization Analytics Dashboards Data-driven 93

Cloudera

JULY 26, 2021

A common pitfall in the development of data platforms is that they are built around the boundaries of point solutions and are constrained by the technological limitations (e.g., a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g.,

Strategy 118

Strategy Marketing Unstructured Data Data Science 118