Tableau

OCTOBER 14, 2021

IT faces hurdles in equipping people with the necessary insights to solve strategic problems quickly and act in their customers’ best interests; likewise, business units can struggle to find the right data when it’s needed most. Data management processes are not integrated into workflows, making data and analytics more challenging to scale.

Analytics 95

Analytics Analytics Tableau Data Governance 95

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Analytics data catalog. Data modeling. Data preparation. Metadata management.

Tableau 102

Tableau Data Quality Analytics Analytics 102

Tableau

APRIL 18, 2022

What if the problem isn’t in the volume of data, but rather where it is located—and how hard it is to gather? Nine out of 10 IT leaders report that these disconnects, or data silos, create significant business challenges.* Analytics data catalog. Data modeling. Data preparation. Metadata management.

Tableau 98

Tableau Data Quality Analytics Analytics 98

Alation

MAY 31, 2023

They collaborate with IT professionals, business stakeholders, and data analysts to design effective data infrastructure aligned with the organization’s goals. Their broad range of responsibilities include: Design and implement data architecture. Maintain data models and documentation.

Data Governance 52

Data Governance Data Quality Data Silos Data Modeling 52

DrivenData Labs

MARCH 30, 2023

Teams' solutions were judged on the following criteria: Privacy : Information leakage possible from the PPFL model during training and inference. Ability to clearly evidence privacy guarantees offered by solution in a form accessible to a regulator and/or data owner audience.

Machine Learning 162

Machine Learning Machine Learning Data Science Algorithm 162

phData

FEBRUARY 13, 2024

By centralizing SAP ERP data in Snowflake, organizations can gain deeper insights into key business metrics, trends, and performance indicators, enabling more informed decision-making, strategic planning, and operational optimization. SAP is relatively easy to work with.

Analytics 59

Analytics Analytics Data Scientist Data Modeling 59

Pickl AI

JUNE 28, 2023

What is Data Mining? In today’s data-driven world, organizations collect vast amounts of data from various sources. Information like customer interactions, and sales transactions plays a pivotal role in decision-making. But, this data is often stored in disparate systems and formats. Wrapping It Up !!!

Data Mining 52

Data Mining Data Mining Data Mining ETL 52

IBM Journey to AI blog

SEPTEMBER 5, 2024

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. Managing this level of oversight requires adept handling of large volumes of data.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Dataversity

JULY 1, 2022

Whether you’re sitting on a ton of untapped data or you’re not extracting value from your data because of organizational restrictions, you may be aware by now of the endless possibilities of a mature data model. The post 3 Signs That Your Data Is Trapped in Silos appeared first on DATAVERSITY.

Data Modeling 52

Data Modeling Data Models Data Silos 52

phData

MAY 13, 2024

Even if organizations survive a migration to S/4 and HANA cloud, licensing and performance constraints make it difficult to perform advanced analytics on this data within the SAP environment. Challenges With Moving SAP Data Given all of the advantages detailed above, if it was easy to move your SAP data to Snowflake, we would not be here.

Database 52

Database Analytics Analytics Machine Learning 52

Dataversity

APRIL 5, 2021

However, most enterprises are hampered by data strategies that leave teams flat-footed when […]. The post Why the Next Generation of Data Management Begins with Data Fabrics appeared first on DATAVERSITY. Click to learn more about author Kendall Clark. The mandate for IT to deliver business value has never been stronger.

Internet of Things 97

Internet of Things Data Silos Data Lakes Data Warehouse 97

phData

SEPTEMBER 13, 2023

Wouldn’t it be amazing to truly unlock the full potential of your data and start driving informed decision-making that ultimately will gain your business the upper hand from your most formidable competitors? This is the easiest and fastest way to onboard your data into Snowflake. The good news is that this reality is possible!

Tableau 52

Tableau Data Pipeline Data Silos Analytics 52

phData

AUGUST 11, 2023

Patients can choose to provide healthcare businesses with access to their information. Through the consent of patients, the information is easily processable using data engineering in healthcare. The widespread sharing of information amongst multiple institutions allows us to achieve the desired results quicker.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Flipboard

FEBRUARY 12, 2025

Neurosymbolic AI techniques, especially knowledge graph, will see a renaissance since they can provide both learning objectives for foundation models and context to significantly improve the performance of generative AI while reducing hallucinations. We will also see a greater variety of foundation models that fulfill different purposes.

AI 65

AI AI Data Silos Artificial Intelligence 65

phData

SEPTEMBER 1, 2023

Healthcare and Life Sciences (HCLS) companies face a multitude of challenges when it comes to managing and analyzing data. From the sheer volume of information to the complexity of data sources and the need for real-time insights, HCLS companies constantly need to adapt and overcome these challenges to stay ahead of the competition.

Analytics 52

Analytics Analytics Data Analysis Data Analysis 52

Heartbeat

DECEMBER 7, 2023

This team should consist of experts who know the business domain where the data comes from and should be something other than general-purpose Information and Communication Technologies (ICT) teams. Data should be created using standardized data models, definitions, and quality requirements. How does it?

Data Governance 52

Data Governance Deep Learning Deep Learning Artificial Intelligence 52

Pickl AI

SEPTEMBER 12, 2024

Comparison with Traditional Machine Learning Approaches In traditional Machine Learning, all data is aggregated and stored in a central repository where it is used to train models. This centralised method poses significant privacy risks, especially when dealing with sensitive information.

Machine Learning 52

Machine Learning Machine Learning Clustering Algorithm 52

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.

Database 52

Database Business Intelligence Business Intelligence Data Silos 52

Mlearning.ai

SEPTEMBER 4, 2023

It was just a few months ago that a bug in ChatGPT revealed data from other users.[7] More realistically, on-premises solutions can offer greater control over data, ensuring that sensitive information never leaves the organization’s physical boundaries. Scaling Laws for Neural Language Models.” 34] See note below.[35]

AI 52

AI AI Azure Machine Learning 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

IBM Journey to AI blog

OCTOBER 30, 2023

A successful public health response to a future pandemic will rely on collecting and managing critical data, investing in smart, capable and flexible data modernization systems, and preparing people with the proper knowledge and skills. Lesson 1: Use a data model built for public health.

Data Modeling 78

Data Modeling Data Models Data Silos Machine Learning 78