Business Analytics, Data Engineering and Industry

The future of data: A 5-pillar approach to modern data management

CIO

DECEMBER 11, 2024

This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process. Operational errors because of manual management of data platforms can be extremely costly in the long run.

Data

Data Technical Review Software Review Weak Development Team

What is data analytics? Analyzing and managing data for decisions

CIO

JUNE 7, 2022

More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Data analytics vs. business analytics.

Analytics

Analytics Data Analysis Business Analytics

Achieving Business Analytics Success

Datavail

MARCH 31, 2022

Organizations are now devising digital analytics algorithms to inform their future strategies as well as keep them apprised of day-to-day activities. Those that also apply directives from their data to operationalize their systems will be at the forefront of their industry. The Significance of Strategy. Contact us today.

Business Analytics

Business Analytics Analytics Artificial Inteligence Strategy

Webinars

Going Beyond Chatbots: Connecting AI to Your Tools, Systems, & Data

Automation, Evolved: Your New Playbook for Smarter Knowledge Work

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Apiumhub among top IT industry leaders in Code Europe event

Apiumhub

AUGUST 12, 2021

Happy to announce that you may find Apiumhub among top IT industry leaders in Code Europe event. Code Europe serves as a platform for the exchange of best practices and experiences between enthusiasts and world-class experts of the new technology industry. Save the date! About Code Europe event. Twitter: ??

Industry

Industry Technical Advisors CTO Coach Azure

Change The Way You Do ML With Applied ML Prototypes

Cloudera

FEBRUARY 25, 2021

This means that ML development teams can tackle their own ML business use cases more quickly, from those involving churn modeling, to sentiment analysis, to anomaly detection and beyond. AMPs capture industry-specific, use-case-specific, and application-specific best practices to take the guesswork out of ML projects. Active Learning.

Artificial Inteligence

Artificial Inteligence Machine Learning Enterprise Telecommunications

The Multifaceted Value Proposition of the Cloudera Data Platform

Cloudera

FEBRUARY 22, 2021

This category describes the unique ability of CDP to accelerate deployment of use cases (and, as a result, the associated business value) by: . Delivering a robust security and governance framework through SDX to support a growing number of users leveraging the data platform.

Data

Data Analytics Government Technical Review

Altexsoft - Untitled Article

Altexsoft

JANUARY 14, 2021

What specialists and their expertise level are required to handle a data warehouse? However, all of the warehouse products available require some technical expertise to run, including data engineering and, in some cases, DevOps. It comprises data warehouse clusters with compute nodes split up into node slices.

Backup

Backup Azure Software Review Architecture

Supply Chain Analytics: Opportunities in Data Analysis and Business Intelligence

Altexsoft

FEBRUARY 8, 2021

Supply chain practitioners and CEOs surveyed by 6river share that the main challenges of the industry are: keeping up with the rapidly changing customer demand, dealing with delays and disruptions, inefficient planning, lack of automation, rising costs (of transportation, labor, etc.), Make sure to take everything into account.

Business Intelligence

Business Intelligence Analytics Analysis Data

Data Virtualization: Process, Components, Benefits, and Available Tools

Altexsoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. Multiple BI tools require access to the same data sources.

Virtualization

Virtualization Tools Data Architecture

Top Data science books you should definitely read

Apiumhub

APRIL 1, 2021

The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment. Business Analytics: The Science Of Data – Driven Decision Making by U Dinesh Kumar.

Artificial Inteligence

Artificial Inteligence Data Handbook Machine Learning

Technology Trends for 2025

O'Reilly Media - Ideas

JANUARY 14, 2025

Its been an exciting year, dominated by a constant stream of breakthroughs and announcements in AI, and complicated by industry-wide layoffs. Our data shows how our users are reacting to changes in the industry: Which skills do they need to brush up on? Data engineers build the infrastructure to collect, store, and analyze data.

Trends

Trends Technology Security Artificial Inteligence

Technology Trends for 2023

O'Reilly Media - Ideas

MARCH 1, 2023

Whether or not we’re actually in a recession, many in our industry perceive us to be so, and that perception can be self-fulfilling. Despite talk of “internet time,” our industry doesn’t change radically from day to day, month to month, or even year to year. Data engineering was the dominant topic by far, growing 35% year over year.

Trends

Trends Technical Review Technology Software Review

Topics to watch at the Strata Data Conference in New York 2019

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

Conference Data Data Engineering Big Data

Data’s dark secret: Why poor quality cripples AI and growth

CIO

APRIL 8, 2025

Its a strategic imperative that demands the focus of both technology and business leaders. In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries. I aim to outline pragmatic strategies to elevate data quality into an enterprise-wide capability.

Weak Development Team

Weak Development Team Technical Review Technical Advisors Systems Review

Using Large Language Models on Amazon Bedrock for multi-step task execution

AWS Machine Learning - AI

APRIL 2, 2025

Traditionally, answering these queries required the expertise of business intelligence specialists and data engineers, often resulting in time-consuming processes and potential bottlenecks. About the Authors Bruno Klein is a Senior Machine Learning Engineer with AWS Professional Services Analytics Practice.

Artificial Inteligence

Artificial Inteligence Machine Learning Analytics AWS

CTO Universe

The future of data: A 5-pillar approach to modern data management

What is data analytics? Analyzing and managing data for decisions

Achieving Business Analytics Success

Webinars

Apiumhub among top IT industry leaders in Code Europe event

Change The Way You Do ML With Applied ML Prototypes

The Multifaceted Value Proposition of the Cloudera Data Platform

Altexsoft - Untitled Article

Supply Chain Analytics: Opportunities in Data Analysis and Business Intelligence

Data Virtualization: Process, Components, Benefits, and Available Tools

Top Data science books you should definitely read

Technology Trends for 2025

Technology Trends for 2023

Topics to watch at the Strata Data Conference in New York 2019

Data’s dark secret: Why poor quality cripples AI and growth

Using Large Language Models on Amazon Bedrock for multi-step task execution

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