IBM Journey to AI blog

MAY 9, 2024

Conversely, confidence in the accuracy and consistency of your data can minimize the risk of adverse health outcomes, rather than merely reacting to or causing them. Also, using predictive analytics can help identify trends, patterns and potential future health risks in your patients.

Data Governance 85

Data Governance Data Silos Data Quality Predictive Analytics 85

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

Analytics 69

Analytics Analytics Data Silos Data Pipeline 69

IBM Data Science in Practice

NOVEMBER 28, 2022

As organizations steer their business strategies to become data-driven decision-making organizations, data and analytics are more crucial than ever before. How can organizations get a holistic view of data when it’s distributed across data silos? Implementing a data fabric architecture is the answer.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

Pickl AI

DECEMBER 10, 2024

Summary : Data Analytics trends like generative AI, edge computing, and Explainable AI redefine insights and decision-making. Businesses harness these innovations for real-time analytics, operational efficiency, and data democratisation, ensuring competitiveness in 2025. billion by 2030, with an impressive CAGR of 27.3%

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

AWS 93

AWS Database ETL AI 93

phData

APRIL 18, 2023

This is due to a fragmented ecosystem of data silos, a lack of real-time fraud detection capabilities, and manual or delayed customer analytics, which results in many false positives. Snowflake Marketplace offers data from leading industry providers such as Axiom, S&P Global, and FactSet.

Data Silos 52

Data Silos ETL Clustering Analytics 52

Precisely

JUNE 2, 2023

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. At the same time, there is a stronger push for real-time analytics and real-time customer access to data.

Data Silos 52

Data Silos ETL Data Quality Data Pipeline 52

Pickl AI

NOVEMBER 15, 2023

Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources. With all data in one place, businesses can break down data silos and gain holistic insights.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Deciding on KPIs to gauge a data architecture’s effectiveness.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

IBM Journey to AI blog

JULY 3, 2024

This allows for easier integration with your existing technology investments while eliminating data silos and accelerating data-driven transformation. The following four components help build an open and trusted data foundation.

AI 45

AI AI Data Quality Database 45

ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability. All of this happens within the data lake environment to produce consistent results and remove the possibility of data silos.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

phData

SEPTEMBER 27, 2024

It enables advanced analytics, makes debugging your marketing automations easier, provides natural audit trails for compliance, and allows for flexible, evolving customer data models. So next time you’re designing your customer data architecture in your CDP, don’t just think about the current state of your customers.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Flipboard

DECEMBER 4, 2024

The use of separate data warehouses and lakes has created data silos, leading to problems such as lack of interoperability, duplicate governance efforts, complex architectures, and slower time to value. You can use Amazon SageMaker Lakehouse to achieve unified access to data in both data warehouses and data lakes.

Data Lakes 139

Data Lakes Data Warehouse AWS Database 139

Alation

OCTOBER 19, 2021

Although organizations don’t set out to intentionally create data silos, they are likely to arise naturally over time. This can make collaboration across departments difficult, leading to inconsistent data quality , a lack of communication and visibility, and higher costs over time (among other issues). What Are Data Silos?

Data Silos 52

Data Silos ETL Data Governance Cloud Data 52

phData

NOVEMBER 9, 2023

The primary objective of this idea is to democratize data and make it transparent by breaking down data silos that cause friction when solving business problems. What Components Make up the Snowflake Data Cloud? As this happens, spending shifts from ELT to analytics.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

Women in Big Data

MARCH 5, 2025

I worked extensively with ETL processes, PostgreSQL, and later, enterprise-scale data systems. Ive always had a logical, data-driven mindset, constantly digging deeper into metrics and questioning assumptions. When I discovered the field of data analytics, it felt like a perfect fit.

ML 52

ML ML Big Data Big Data 52

Flipboard

MARCH 21, 2025

The ability for organizations to quickly analyze data across multiple sources is crucial for maintaining a competitive advantage. Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems.

SQL 136

SQL Data Analyst Data Warehouse AWS 136