CIO Business Intelligence

NOVEMBER 28, 2023

With organizations seeking to become more data-driven with business decisions, IT leaders must devise data strategies gear toward creating value from data no matter where — or in what form — it resides. Unstructured data resources can be extremely valuable for gaining business insights and solving problems.

Unstructured Data 125

Unstructured Data Data-driven Visualization Data Quality 125

CIO Business Intelligence

SEPTEMBER 14, 2023

While data engineers develop, test, and maintain data pipelines and data architectures, data scientists tease out insights from massive amounts of structured and unstructured data to shape or meet specific business needs and goals. Careers, Data Management, Data Mining, Data Science, Staff Management

Analytics 133

Analytics Data Science Unstructured Data Data mining 133

FineReport

JULY 28, 2021

Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information. Insurance Dashboard (by FineReport).

Business Intelligence 98

Business Intelligence Data Science Data Analytics Analytics 98

FineReport

JUNE 24, 2021

Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. On the opposite, it is more of a comprehensive application of data warehouse, OLAP, data mining, and so forth. All BI software capabilities, functionalities, and features focus on data.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

CIO Business Intelligence

APRIL 22, 2022

What is data science? Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models.

Data Science 139

Data Science Statistics Machine Learning Visualization 139

IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. What is text mining? For instance, a parsing model could identify the subject, verb and object of a complete sentence. You may need to iterate this process until the results are satisfactory.

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

CIO Business Intelligence

AUGUST 9, 2022

Data engineers and data scientists often work closely together but serve very different functions. Data engineers are responsible for developing, testing, and maintaining data pipelines and data architectures. Data engineer vs. data architect.

Analytics 126

Analytics Data Science Statistics Unstructured Data 126

Sisense

MARCH 14, 2021

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

FineReport

APRIL 26, 2023

For example, a computer manufacturing company could develop new models or add features to products that are in high demand. E-commerce giants like Alibaba and Amazon extensively use big data to understand the market. Unlike traditional databases, processing large data volumes can be quite challenging. Easy implementation.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Grooper

AUGUST 19, 2019

Connect the Dots Between Data Literacy, ISL, and the Requirements List. Data literacy is solved by a structured program of learning information as a second language (ISL). ISL eliminates data literacy by modeling the way we learn spoken language. Master data management. Data governance. Data pipelines.

Analytics 54

Analytics Machine Learning Data-driven Data Science 54

Rita Sallam

JULY 22, 2016

By contrast, traditional BI platforms are designed to support modular development of IT-produced analytic content, specialized tools and skills, and significant upfront data modeling, coupled with a predefined metadata layer, is required to access their analytic capabilities. Answer: Better than every other vendor?

Analytics 40

Analytics Business Intelligence Metadata Statistics 40

Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Smart Data Collective

SEPTEMBER 26, 2021

Data is processed to generate information, which can be later used for creating better business strategies and increasing the company’s competitive edge. Working with massive structured and unstructured data sets can turn out to be complicated. Data storage is a key component of any successful organization.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

Jet Global

NOVEMBER 7, 2023

The Challenges of Extracting Enterprise Data Currently, various use cases require data extraction from your OCA ERP, including data warehousing, data harmonization, feeding downstream systems for analytical or operational purposes, leveraging data mining, predictive analysis, and AI-driven or augmented BI disciplines.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52