IBM Journey to AI blog

JANUARY 17, 2023

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. The rapid growth of data continues to proceed unabated and is now accompanied by not only the issue of siloed data but a plethora of different repositories across numerous clouds.

Data Governance 58

Data Governance Data Science AI Data Silos 58

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. The data lake can then refine, enrich, index, and analyze that data. Interested in attending an ODSC event?

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Pickl AI

JULY 16, 2024

Here’s how it differentiates itself from traditional IT operations methods: Data-driven It thrives on collecting and processing vast amounts of data from diverse sources – applications, networks, infrastructure, and user behavior. By analyzing this data , it identifies patterns and anomalies that might escape human observation.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

DECEMBER 4, 2024

Implementing Generative AI can be difficult as there are some hurdles to overcome for any business to get up and running: Data Quality You get the same quality output as the data you use for any AI system, so having accurate and unbiased data is of the utmost importance.

AI 52

AI AI Data Warehouse Data Quality 52

Heartbeat

DECEMBER 7, 2023

What is Data Mesh? Data Mesh is a new data set that enables units or cross-functional teams to decentralize and manage their data domains while collaborating to maintain data quality and consistency across the organization — architecture and governance approach. on Twitter: "Data is addictive!

Data Governance 52

Data Governance Deep Learning Deep Learning Artificial Intelligence 52

Flipboard

FEBRUARY 12, 2025

Instead, AI agents will proactively respond to business events such as incoming customer inquiries, supply chain disruptions, or demand surges. This will only worsen, and companies must learn to adapt their models to unique, content-rich data sources. In the future, users will not even need to trigger an action.

AI 65

AI AI Data Silos Artificial Intelligence 65

phData

SEPTEMBER 27, 2024

Methods that allow our customer data models to be as dynamic and flexible as the customers they represent. In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

AWS Machine Learning Blog

APRIL 3, 2025

By analyzing their data, organizations can identify patterns in sales cycles, optimize inventory management, or help tailor products or services to meet customer needs more effectively. Lambda enables serverless, event-driven data processing tasks, allowing for real-time transformations and calculations as data arrives.

AWS 74

AWS Data Governance Data Silos SQL 74

Precisely

FEBRUARY 9, 2024

They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good data quality will be self-sustaining.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

phData

NOVEMBER 13, 2023

Enhanced Collaboration: dbt Mesh fosters a collaborative environment by using cross-project references, making it easy for teams to share, reference, and build upon each other’s work, eliminating the risk of data silos. These jobs can be triggered via schedule or events, ensuring your data assets are always up-to-date.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

Alation

OCTOBER 12, 2021

Enterprise data analytics enables businesses to answer questions like these. It empowers analysts to model scenarios, forecast change, and predict impact of real or imagined events. Having a data analytics strategy is a key to delivering answers to these questions and enabling data to drive the success of your business.

Analytics 52

Analytics Analytics Data Governance DataOps 52

Dataconomy

APRIL 6, 2023

While data democratization has many benefits, such as improved decision-making and enhanced innovation, it also presents a number of challenges. From lack of data literacy to data silos and security concerns, there are many obstacles that organizations need to overcome in order to successfully democratize their data.

Data Governance 118

Data Governance Data Silos Data Analysis Data Analysis 118

Alation

APRIL 19, 2022

Efficient Data Processing. To use data, you need the ability to collect and correlate it efficiently. Data modernization reduces the time it takes data users to find high-value data and analyze events. In a “work from anywhere” world, people need to access data from wherever they are, including from home.

Data Governance 52

Data Governance Cloud Data Database Data Silos 52

Dataconomy

APRIL 6, 2023

While data democratization has many benefits, such as improved decision-making and enhanced innovation, it also presents a number of challenges. From lack of data literacy to data silos and security concerns, there are many obstacles that organizations need to overcome in order to successfully democratize their data.

Data Governance 70

Data Governance Data Silos Data Analysis Data Analysis 70

Flipboard

MARCH 21, 2025

Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems. Unmanaged assets : For unmanaged assets, permissions are handled externally.

SQL 136

SQL Data Analyst Data Warehouse AWS 136

Pickl AI

APRIL 8, 2025

Similarly, in a data warehouse , dimensions are the descriptive attributes that help categorize and analyse facts (events or transactions). Just as a librarian uses these attributes to organize books, businesses use dimensions to organize and analyse their data. Benefits: Reduces redundancy in the data warehouse.

Data Warehouse 52

Data Warehouse Analytics Analytics Data Silos 52