TechCrunch

OCTOBER 20, 2021

Its new Data Intelligence product enables organizations to make all of their mobile data accessible and actionable by their business intelligence and data science teams so that other departments, like engineering, product and marketing, can use that data to make better decisions about areas like new products and marketing campaigns.

Mobile 246

Mobile Data Weak Development Team Software 246

Altexsoft

FEBRUARY 8, 2021

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Gorilla Logic

MAY 19, 2021

Monetize data with technologies such as artificial intelligence (AI), machine learning (ML), blockchain, advanced data analytics , and more. Create value from the Internet of Things (IoT) and connected enterprise. Some of the most common include cloud, IoT, big data, AI/ML, mobile, and more.

Software Review 94

Software Review Technical Review Software Development Team Review 94

ParkMyCloud

MAY 29, 2019

An educational path to becoming a Cloud Consultant ranges from studying different programming languages to getting certified – although not necessarily required – in a single or multi-cloud computing systems. Business Intelligence Analyst. IoT Engineer. Data Detective. Man-Machine Teaming Manager.

Trends 72

Trends Cloud IoT Artificial Inteligence 72

TechCrunch

AUGUST 24, 2022

We’ll update this if we learn more. The capital and relocation speaks not just to key moment for the company, but also for the area of machine learning and wider trends impacting Chinese-founded startups. The total raised by the company is now $113 million.

Open Source 199

Open Source Artificial Inteligence Comparison B2C 199

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Who needs a data lake?

Architecture 52

Architecture Data Storage Machine Learning 52

Altexsoft

DECEMBER 9, 2020

Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Predictive analytics creates probable forecasts of what will happen in the future, using machine learning techniques to operate big data volumes. Analytics maturity model. Predictive analytics.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Traditional data warehouse platform architecture. Another type of data storage — a data lake — tried to address these and other issues.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

SEPTEMBER 12, 2018

It’s a big week for us, as many Clouderans descend on New York for the Strata Data Conference. The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Enterprise Machine Learning: .

Artificial Inteligence 45

Artificial Inteligence Data Machine Learning Pharmaceuticals 45

Altexsoft

SEPTEMBER 23, 2021

The main purpose of a DW is to enable analytics: It is designed to source raw historical data, apply transformations, and store it in a structured format. This type of storage is a standard part of any business intelligence (BI) system, an analytical interface where users can query data to make business decisions.

Architecture 52

Architecture Data Storage IoT 52

Altexsoft

JUNE 14, 2021

Lemonade uses Natural Action Synthesis and Natural Language Processing to ensure that Maya gets smarter the more it chats. This is possible because their machine learning model is retrained almost daily. Your vision on personalization may not work for every business model.

Insurance 88

Insurance Machine Learning Artificial Inteligence Policies 88

Apiumhub

AUGUST 28, 2018

It is a cloud-based big data analytics platform, built to improve data-driven decision making. It then deploys machine learning algorithms to better predict customer needs. Digital Risks is an insurance specialist built for tech companies, offering a flexible, pay monthly Insurance-as-a-Service model.

Insurance 96

Insurance Technical Review Artificial Inteligence Policies 96

Sunflower Lab

APRIL 2, 2024

Use Cases of Power BI Predictive Analytics in Different Industries Use Case: Predictive Maintenance in Healthcare Business Scenario: A hospital aims to reduce equipment downtime and maintenance costs by implementing predictive maintenance. Explore our Power BI solutions How Can Power BI Be Used for Predictive Analytics?

Analytics 40

Analytics Machine Learning Artificial Inteligence Analysis 40

Altexsoft

AUGUST 31, 2020

The data is already being stored digitally: more than 95 percent of health facilities report implementing Electronic Health Records (EHR) systems. Large part of data comes from widely adopted IoT devices used in 60 percent of hospitals in the US today. In 2017, Penn launched a trigger system Palliative Connect.

Healthcare 72

Healthcare Analytics Examples Data 72

N2Growth Blog

JANUARY 23, 2018

Businesses must also take advantage of customer telemetry, Big Data, generated by activity on websites, mobile devices, and social media, to create a more personalized experience — both in-house and online. But not every business knows how to convert that data into actionable insights.

Innovation 84

Innovation Strategy Big Data Advertising 84

Altexsoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Data availability. ELT vs ETL.

Tools 52

Tools Software Review Systems Review Testing 52

Altexsoft

MARCH 14, 2023

Data sources The first component of the MDS architecture is a place, or in most cases, places where your data originates from. Data may come from hundreds (or sometimes thousands) of different sources, including computers, smartphones, websites, social media networks, eCommerce platforms, and IoT devices.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Altexsoft

FEBRUARY 22, 2022

Such an approach requires a great deal of investment since a whole ecosystem has to be created, including IoT sensors installation, acquiring specialized software, creating and maintaining machine learning (ML) models, engaging IT and data science specialists , and so on. Processing data.

Software Review 52

Software Review Technical Review Software Technology 52

Openxcell

JANUARY 6, 2023

Instead of making substantial investments in databases, software, and hardware, businesses prefer to access their computing power over the internet or in the cloud. Just a few of the existing cloud services include servers, storage, databases, networking, software, analytics, and business intelligence.

Architecture 52

Architecture Cloud Storage Google Cloud 52

Hu's Place - HitachiVantara

FEBRUARY 15, 2019

In the case of Hybrid and multi-cloud, we are mixing up multiple clouds which increases operational and data management complexity. Operational policies and methods are different and aggregation of data across multiple clouds boundaries makes it difficult for governance, analytics, and business intelligence.

Cloud 49

Cloud AWS Azure Data Center 49

Altexsoft

APRIL 13, 2023

“Control towers are the artificial intelligence (AI) of supply chain. The readings from IoT sensors and other connected devices (e.g., External data sources To deliver comprehensive visibility and make accurate forecasts, your SCCT must collect data from external sources.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

TechCrunch

JUNE 25, 2021

That whole model is breaking down.” He acknowledges that traditional big data warehousing works quite well for business intelligence and analytics use cases. But that’s not real-time and also involves moving a lot of data from where it’s generated to a centralized warehouse.

Machine Learning 210

Machine Learning Artificial Inteligence Big Data Business Intelligence 210

Altexsoft

JANUARY 22, 2020

Similar to a real world stream of water, continuous transition of data received the name streaming , and now it exists in different forms. Media streaming is one of them, but it’s only a visible part of an iceberg where data streaming is used. As a result, it became possible to provide real-time analytics by processing streamed data.

Analytics 20

Analytics Data IoT Analysis 20

O'Reilly Media - Ideas

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

Conference 8

Conference Data Data Engineering Artificial Inteligence 8

Altexsoft

OCTOBER 24, 2019

With a data warehouse, an enterprise is able to manage huge data sets, without administering multiple databases. Such practice is a futureproof way of storing data for business intelligence (BI) , which is a set of methods/technologies of transforming raw data into actionable insights.

Architecture 50

Architecture Enterprise Data Technical Review 50

Altexsoft

OCTOBER 17, 2019

Low-quality data can also impede and slow down the integration of business intelligence and ML-powered predictive analytics. US company executives that participated in the Data trust pulse survey by PricewaterhouseCoopers noted that unreliable data is one of the obstacles to monetizing data.

Tools 16

Tools Data Technical Advisors Technical Review 16