Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 121

Predictive Analytics Analytics Predictive Modeling Data mining 121

Rocket-Powered Data Science

JULY 7, 2019

7) Security (airports, shopping malls, entertainment & sport events). Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…).

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

CIO Business Intelligence

MAY 30, 2024

To do that, McIntosh and OMES turned to process mining, a technique for analyzing event data to better understand and improve operational processes.

IT 127

IT Cost-Benefit Key Performance Indicator Dashboards 127

IBM Big Data Hub

SEPTEMBER 19, 2023

Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets. Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred.

Data Science 79

Data Science Data Analytics Prescriptive Analytics Analytics 79

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

DataKitchen

OCTOBER 11, 2021

The DataOps pipeline you have built has enough automated tests to catch errors, and error events are tied to some form of real-time alerts. Priya has more than 10 years of experience in IT, Data Warehousing, Reporting, Business Intelligence, Data Mining and Engineering, and DataOps. Priyanjna Sharma.

Testing 152

Testing Metadata Dashboards Statistics 152

CIO Business Intelligence

DECEMBER 22, 2022

DataOps in practice To make the most of DataOps, enterprises must evolve their data management strategies to deal with data at scale and in response to real-world events as they happen, according to Dunning and Friedman. They also note DataOps fits well with microservices architectures.

Analytics 126

Analytics Machine Learning Software Data Quality 126

IBM Big Data Hub

JULY 24, 2023

IT operations analytics (ITOA) vs. observability ITOA and observability share a common goal of using IT operations data to track and analyze how a system is performing to improve operational efficiency and effectiveness. It aims to understand what’s happening within a system by studying external data.

IT 56

IT Analytics Cost-Benefit Predictive Analytics 56

Smarten

MAY 24, 2018

ElegantJ BI, an innovative vendor in Business Intelligence, Augmented Analytics and Augmented Data Preparation, is pleased to announce its participation in the Gartner 2018 INDIA Data & Analytics Summit from 5 – 6th June 2018 in Mumbai, India. ElegantJ BI is proud to be a Silver Sponsor at this important event.

Analytics 40

Analytics Data Analytics Data mining Predictive Modeling 40

CIO Business Intelligence

MARCH 24, 2023

Varonis Data Governance Suite Varonis’s solution automates data protection and management tasks leveraging a scalable Metadata Framework that enables organizations to manage data access, view audit trails of every file and email event, identify data ownership across different business units, and find and classify sensitive data and documents.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Jedox

JULY 31, 2018

Part one of our blog series explored how people are the driving force behind the digital transformation and how it is fueled by artificial intelligence and machine learning. Now, we will take a deeper look into AI, Machine learning and other trending technologies and the evolution of data analytics from descriptive to prescriptive.

Technology 44

Technology Prescriptive Analytics Machine Learning Business Intelligence 44

Jet Global

JUNE 29, 2020

Advanced analytics is autonomous or semi-autonomous examination of data using techniques and tools that typically go beyond business intelligence (BI). It serves as an umbrella term for several sub-fields of analytics that work together using predictive capabilities.

Analytics 98

Analytics Reporting Cost-Benefit Data mining 98

erwin

JUNE 11, 2020

Data intelligence can encompass both internal and external business data and information. It also differs from business intelligence since its goal is to augment future endeavors and plans. Transforming Industries with Data Intelligence. Apply real-time data in marketing strategies.

Key Performance Indicator 103

Key Performance Indicator KPI Data-driven Machine Learning 103

Sisense

APRIL 26, 2020

Key points to keep in mind about semi-structured data: Falls under the heading of unstructured data, but it has some lower-degree organization (still falls short of relational databases) Can be coerced into useful and easy-to-leverage table formats Examples of semi-structured data include XML, JSON, Emails, NoSQL DBs, event tracking, and web pages.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

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

FineReport

APRIL 26, 2023

Unlike traditional databases, processing large data volumes can be quite challenging. With Big Data Analytics, businesses can make better and quicker decisions, model and forecast future events, and enhance their Business Intelligence. How to Choose the Right Big Data Analytics Tools?

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Cloudera

MAY 15, 2019

He is a successful architect of healthcare data warehouses, clinical and business intelligence tools, big data ecosystems, and a health information exchange. The Enterprise Data Cloud – A Healthcare Perspective.

Enterprise 48

Enterprise Data Lake Data mining IoT 48

Sisense

MAY 11, 2021

When it comes to data modeling, function determines form. Let’s say you want to subject a dataset to some form of anomaly detection; your model might take the form of a singular event stream that can be read by an anomaly detection service. The right data model + artificial intelligence = augmented analytics. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

BI-Survey

OCTOBER 23, 2019

New real-time security intelligence solutions are combining big data and advanced analytics to correlate security events across multiple data sources, providing early detection of suspicious activities, rich forensic analysis tools and highly automated remediation workflows. The most important current IT trends (n=332).

Big Data 49

Big Data Analytics Manufacturing Digital Transformation 49

Alation

AUGUST 26, 2021

For this reason, data intelligence software has increasingly leveraged artificial intelligence and machine learning (AI and ML) to automate curation activities, which deliver trustworthy data to those who need it. How Do Data Intelligence Tools Support Data Culture? BI and AI for Data Intelligence.

Metadata 64

Metadata Data Governance Dashboards Software 64

Jet Global

MAY 1, 2023

Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. Embedded Analytics Definition Embedded analytics are the integration of analytics content and capabilities within applications, such as business process applications (e.g., that gathers data from many sources.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52