Dataversity

OCTOBER 25, 2023

We exist in a diversified era of data tools up and down the stack – from storage to algorithm testing to stunning business insights.

Data Quality 104

Data Quality Data Preparation Algorithm Data Silos 104

IBM Journey to AI blog

APRIL 15, 2024

Organizations gain the ability to effortlessly modify and scale their data in response to shifting business demands, leading to greater agility and adaptability. This holistic view empowers businesses to make data-driven decisions, optimize processes and gain a competitive edge.

Analytics 81

Analytics Analytics Data Silos AI 81

Ocean Protocol

MARCH 6, 2023

Launched in January 2023, contestants of the ETH price prediction data challenge were asked to engage with the Ocean.py This challenge aimed to activate relevant communities of Web3-native data scientists and guide them towards potential use cases such as community-owned algorithms via data NFTs and DeFi protocol design.

Data Scientist 59

Data Scientist Data Silos Algorithm 59

Ocean Protocol

FEBRUARY 1, 2023

Participants are encouraged to use Ocean Protocol’s technology to decentralize access to their algorithms, and share their solutions with privacy-preserving technology. Catalonia) over the past three decades and develop algorithms to predict air pollutant concentrations. The submission deadline is February 14 @ 23:59 PM UTC.

Algorithm 52

Algorithm Data Silos Data Scientist 52

Ocean Protocol

JANUARY 5, 2023

The Ocean Protocol — Gitcoin DataBuilder hackathon seeks to protect public goods funding and data decentralization by encouraging data scientists around the globe to develop insightful anti-Sybil algorithms and implement them into composable legos while using end-to-end decentralized technologies.

Data Scientist 52

Data Scientist Data Silos Algorithm Data Science 52

IBM Journey to AI blog

OCTOBER 30, 2023

Real-time data analytics helps in quick decision-making, while advanced forecasting algorithms predict product demand across diverse locations. AWS’s scalable infrastructure allows for rapid, large-scale implementation, ensuring agility and data security.

AWS 95

AWS AI AI Data Warehouse 95

Becoming Human

MARCH 16, 2023

AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed.

AI 139

AI AI Deep Learning Deep Learning 139

AWS Machine Learning Blog

JANUARY 13, 2023

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

AWS 93

AWS Analytics Analytics Machine Learning 93

Dataconomy

APRIL 27, 2023

The role of digit-computers in the digital age Handle multi-user access & data integrity OLTP systems must be able to handle multiple users accessing the same data simultaneously while ensuring data integrity. Building in these characteristics at a later stage can be costly and resource-intensive.

Database 159

Database Data Scientist Data Mining Data Mining 159

Ocean Protocol

DECEMBER 27, 2022

Launched in November 2022, contestants of the ETH price prediction data challenge were asked to engage with Ocean.py This challenge aimed to activate relevant communities of Web3-native data scientists and guide them towards potential use cases such as community-owned algorithms via data NFTs and DeFi protocol design.

Data Scientist 52

Data Scientist Data Silos Data Pipeline Algorithm 52

Dataconomy

JULY 19, 2023

Assess the uniqueness and viability of the AI algorithms being used, as well as their potential applications in real-world scenarios. Its privacy-preserving features make it ideal for applications that require sensitive data, such as healthcare and financial services. Data security : Data can be vulnerable to security risks.

AI 172

AI AI Artificial Intelligence Artificial Intelligence 172

DrivenData Labs

MARCH 30, 2023

Modeling ¶ Most teams experimented with a variety of modeling algorithms, and many noted that the privacy techniques in their solutions could be paired with more than one family of machine learning models. We are excited to take on this challenge and continue pushing the boundaries of machine learning research.

Machine Learning 162

Machine Learning Machine Learning Data Science Algorithm 162

Alation

OCTOBER 5, 2021

Yet there is often lack of awareness of the trustworthiness (or lack thereof) of the data that these algorithms are being trained on. It is commonplace for a company to create an enterprise data governance strategy that fails to even consider the end user. Roadblock #3: Silos Breed Misunderstanding.

Data Governance 96

Data Governance Data Quality Data Silos Analytics 96

IBM Journey to AI blog

OCTOBER 13, 2023

In addition, digital transformation initiatives have created the proliferation of applications, creating data siloes. It can allow you to empower users from all areas of your business to identify and act on situations in the moment, helping them avoid getting lost in algorithms, heavy code, or disparate data sources.

Data Silos 83

Data Silos Data Analysis Data Analysis Algorithm 83

Women in Big Data

FEBRUARY 1, 2023

She goes on to explain the one of the most beneficial features of One Data’s enabling technology, One Data Cartography , is record linkage combined with data quality. This feature enables holistic and seamless data tracking across system boundaries, based on algorithms and automatic checks for quality anomalies.

Big Data 52

Big Data Big Data Data Silos Data Quality 52

ODSC - Open Data Science

MAY 17, 2023

In other cases, the details of why an AI algorithm reached a certain outcome are not publicly available because the companies that developed them consider the information proprietary. Critics warn such algorithms could erode democracy. That’s often called the “black-box problem.”

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Data Silos 52

Alation

MAY 2, 2023

Some of the key benefits of this include: Simplified data governance Data governance and data analytics support each other, and a strong data governance strategy is integral to ensuring that data analytics are reliable and actionable for decision-makers.

Analytics 52

Analytics Analytics Data Silos Data Governance 52

Pickl AI

DECEMBER 11, 2024

Efficiency emphasises streamlined processes to reduce redundancies and waste, maximising value from every data point. Common Challenges with Traditional Data Management Traditional data management systems often grapple with data silos, which isolate critical information across departments, hindering collaboration and transparency.

Data Silos 52

Data Silos Data Pipeline Artificial Intelligence Artificial Intelligence 52

Precisely

FEBRUARY 12, 2024

They use advanced algorithms to proactively identify and resolve network issues, reducing downtime and improving service to their subscribers. Location-based data is often subject to additional regulatory requirements as well, further adding to the challenges of spatial data governance.

Data Governance 64

Data Governance Analytics Analytics Machine Learning 64

Ocean Protocol

APRIL 13, 2023

About Ocean Protocol Ocean Protocol is an ecosystem of open source data sharing tools for the blockchain. Ocean Protocol is spearheading the movement to unlock a New Data Economy in Web3 by breaking down data silos and opening access to high quality data.

Data Silos 52

Data Silos Machine Learning Machine Learning Predictive Analytics 52

phData

FEBRUARY 14, 2023

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

Data Governance 52

Data Governance Data Silos Predictive Analytics Data Scientist 52

Precisely

SEPTEMBER 26, 2024

However, achieving success in AI projects isn’t just about deploying advanced algorithms or machine learning models. The real challenge lies in ensuring that the data powering your projects is AI-ready. Above all, you must remember that trusted AI starts with trusted data. Is it contextualized with necessary third-party data?

AI 75

AI AI Data Governance Data Quality 75

Dataconomy

MAY 31, 2023

Integration capabilities allow businesses to connect their SolaaS solution with their existing software ecosystem, ensuring smooth data exchange and eliminating data silos. Together, humans and algorithms collaborate to provide an effective and enduring solution.

Data Scientist 113

Data Scientist Cloud Computing Analytics Analytics 113

Pickl AI

DECEMBER 4, 2023

So, what is Data Intelligence with an example? For example, an e-commerce company uses Data Intelligence to analyze customer behavior on their website. Through advanced analytics and Machine Learning algorithms, they identify patterns such as popular products, peak shopping times, and customer preferences.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

ODSC - Open Data Science

JULY 13, 2023

For example, feeding an algorithm statistics about consumer purchasing behavior from stores in one location might lead to poor optimization in another because the data might not be applicable. A retailer must connect data silos across the entire organization for proper consolidation. The underlying issue is quality.

Data Science 52

Data Science Machine Learning Machine Learning Data Silos 52

Alation

MAY 2, 2023

Some of the key benefits of this include: Simplified data governance Data governance and data analytics support each other, and a strong data governance strategy is integral to ensuring that data analytics are reliable and actionable for decision-makers.

Analytics 40

Analytics Analytics Data Silos Data Governance 40

Pickl AI

SEPTEMBER 12, 2024

Introduction Machine Learning has evolved significantly, from basic algorithms to advanced models that drive today’s AI innovations. It also enables models to be trained on diverse data sources, potentially leading to better generalisation and performance. Examples include Google’s predictive text and healthcare applications.

Machine Learning 52

Machine Learning Machine Learning Clustering Algorithm 52

AWS Machine Learning Blog

JANUARY 13, 2023

Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a centralized data repository.

AWS 88

AWS Analytics Analytics Machine Learning 88

Flipboard

FEBRUARY 12, 2025

This means figuring out the best result out of many possible outcomes, which is almost impossible to hardcode in an RPA algorithm with classical automation methods. Agents will be more adaptable and robust than conventional robotic process automation (RPA) for longtail and highly extensive tasks.

AI 65

AI AI Data Silos Artificial Intelligence 65

The MLOps Blog

MAY 17, 2023

These pipelines assist data scientists in saving time and effort by ensuring that the data is clean, properly formatted, and ready for use in machine learning tasks. Moreover, ETL pipelines play a crucial role in breaking down data silos and establishing a single source of truth.

ETL 59

ETL Data Pipeline ML ML 59

Pickl AI

DECEMBER 10, 2024

Unified Data Fabric Unified data fabric solutions enable seamless access to data across diverse environments, including multi-cloud and on-premise systems. These solutions break down data silos, making it easier to integrate and analyse data from various sources in real-time.

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

Alation

AUGUST 2, 2022

The problem many companies face is that each department has its own data, technologies, and information handling processes. This causes data silos to form, which can inhibit data visibility and collaboration, and lead to integrity issues that make it harder to share and use data.

Data Silos 52

Data Silos Data Lakes Data Analyst Data Scientist 52

Pickl AI

JULY 16, 2024

User Experience Monitoring (UEM): User behavior data, application responsiveness Event Correlation and Anomaly Detection The collected data is a massive stream of events. It employs sophisticated algorithms to identify patterns, trends, and anomalies that deviate from established baselines.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Alation

NOVEMBER 16, 2021

Data growth, shrinking talent pool, data silos – legacy & modern, hybrid & cloud, and multiple tools – add to their challenges. According to Gartner, “Through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.”.

Data Governance 52

Data Governance Data Scientist SQL Data Silos 52

Pickl AI

JUNE 13, 2024

Rules Engine This is the brain of the CDSS, employing complex algorithms to analyze patient data against the knowledge base. The rules engine acts like a tireless medical consultant, constantly evaluating patient data and recommending the most suitable course of action based on the latest medical knowledge.

Big Data Analytics 52

Big Data Analytics Big Data Analytics Big Data Big Data 52

phData

SEPTEMBER 27, 2024

Let’s break down why this is so powerful for us marketers: Data Preservation : By keeping a copy of your raw customer data, you preserve the original context and granularity. It’s like being able to go back and re-examine every piece of evidence in your customer relationship journey.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

DrivenData Labs

SEPTEMBER 10, 2024

Find, curate, or contribute data to create representative and open datasets that can be used for early prediction of AD/ADRD. Advance algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions. Dr. Reid also teaches Data Science at the University of California at Berkeley.

Machine Learning 147

Machine Learning Machine Learning Computer Science Computer Science 147