AWS Machine Learning Blog

DECEMBER 29, 2022

The use of RStudio on SageMaker and Amazon Redshift can be helpful for efficiently performing analysis on large data sets in the cloud. However, working with data in the cloud can present challenges, such as the need to remove organizational data silos, maintain security and compliance, and reduce complexity by standardizing tooling.

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IBM Data Science in Practice

APRIL 26, 2024

In this blog, we explore how the introduction of SQL Asset Type enhances the metadata enrichment process within the IBM Knowledge Catalog , enhancing data governance and consumption. Understanding Data Fabric and IBM Knowledge Catalog A data fabric is an architectural blueprint that helps transcending traditional data silos and complexities.

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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.

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Ocean Protocol

JANUARY 5, 2023

The competition runs for 4 weeks, from January 5th through January 31st 2023, and offers contestants $40,000 in bounties to defend Web3 by fighting Sybil attackers and resisting re-centralization at the data layer. By giving power back to data owners, Ocean resolves the tradeoff between using private data and the risks of exposing it.

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Ocean Protocol

MARCH 7, 2023

Calling all Oceaners, data scientists, and traders! It is time to take part in decentralized data science with Ocean Protocol! About Predict-ETH Competition Ocean Protocol’s Predict-ETH data challenge is an exciting opportunity for data scientists to showcase their skills and potentially win big.

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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. This may include customization, integration, analytics, reporting, and expert assistance from data scientists or domain experts.

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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.

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Tableau

SEPTEMBER 14, 2021

They have access to trusted, live data sources and use data often as they experiment, test, innovate, and learn. Eliminate business and data silos to increase collaboration. Data in isolation isn’t useful.

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Tableau

SEPTEMBER 14, 2021

They have access to trusted, live data sources and use data often as they experiment, test, innovate, and learn. Eliminate business and data silos to increase collaboration. Data in isolation isn’t useful.

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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.

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Precisely

JULY 15, 2024

Even without a specific architecture in mind, you’re building toward a framework that enables the right person to access the right data at the right time. However, complex architectures and data silos make that difficult. It’s time to rethink how you manage data to democratize it and make it more accessible.

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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.

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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.

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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.”.

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phData

FEBRUARY 13, 2024

Now that the data is in Snowflake, your organization will also have access to the myriad of AI tools , such as Snowpark , that work within Snowflake. SAP also has limited support for known programming languages such as Java, Python, and Scala, making creating data applications with advanced analytics difficult.

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Precisely

FEBRUARY 12, 2024

Before business users can tap into the value of their data to deliver positive outcomes, that data must be complete, contextual, timely, accurate, and available. In other words, the data needs to be freed from its silos. Data scientists spend most of their time combining, harmonizing, and validating disparate data sets.

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Alation

DECEMBER 7, 2021

With this integration, organizations can fully harness the power of their metadata to maintain pristine data pipelines and serve high quality data to a broader range of users. One major obstacle presented to data quality is data silos , as they obstruct transparency and make collaboration tough. Unified Teams.

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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.

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Alation

APRIL 12, 2022

It makes the process of looking for data assets as familiar as shopping on Amazon. Reducing Data Silos. A perennial headache for CDOs is the continued proliferation of data silos. Different lines of business or function within the organization often maintain their own data environments, creating insular bubbles.

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Ocean Protocol

APRIL 14, 2023

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. Ocean Protocol’s technology allows data to be published, discovered, and consumed in a secure, privacy-preserving manner.

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IBM Journey to AI blog

FEBRUARY 2, 2023

This functionality provides access to data by storing it in an open format, increasing flexibility for data exploration and ML modeling used by data scientists, facilitating governed data use of unstructured data, improving collaboration, and reducing data silos with simplified data lake integration.

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ODSC - Open Data Science

JUNE 7, 2023

This means that customers can easily create secure and scalable Hadoop-based data lakes that can quickly process large amounts of data with simplicity and data security in mind. Snowflake Snowflake is a cross-cloud platform that looks to break down data silos.

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DrivenData Labs

MARCH 30, 2023

Our framework involves three key components: (1) model personalization for capturing data heterogeneity across data silos, (2) local noisy gradient descent for silo-specific, node-level differential privacy in contact graphs, and (3) model mean-regularization to balance privacy-heterogeneity trade-offs and minimize the loss of accuracy.

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The MLOps Blog

MAY 17, 2023

ML heavily relies on ETL pipelines as the accuracy and effectiveness of a model are directly impacted by the quality of the training data. 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.

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Alation

DECEMBER 13, 2022

People come to the data catalog to find trusted data, understand it, and use it wisely. Today a modern catalog hosts a wide range of users (like business leaders, data scientists and engineers) and supports an even wider set of use cases (like data governance , self-service , and cloud migration ).

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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.

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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.

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Pickl AI

APRIL 14, 2023

The Data Platform can be: On-premises Cloud-based Hybrid Data Platforms are incredibly useful for enterprise management in several ways: Ref: [link] Integration and Consolidation These platforms help in organizing and integrating data from different sources.

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Pickl AI

JULY 16, 2024

Integration with existing infrastructure: Ensure the chosen AIOps solution integrates seamlessly with your existing IT systems to avoid data silos and ensure smooth operation. Scalability: Consider the size and complexity of your IT environment and choose tools that can scale to accommodate future growth.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

Master data management (MDM) MDM tools keep an organization’s master data—such as customer, product or supplier information—consistent and up-to-date across systems and departments, preventing data silos and providing a unified view of critical data entities.

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Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. 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.

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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.

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Heartbeat

DECEMBER 7, 2023

In this case, the formation of data silos is prevented, and we provide the most efficient and fast use of decentralized, federated, and simultaneous interoperability with data mesh. This approach is very similar to the microservice architecture in software. How does it? Let’s continue by understanding the four basic principles.

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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.

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Pickl AI

OCTOBER 6, 2024

This structured environment ensures that the data is organized and optimized for analytical queries, making it easier for users to derive insights. However, this process can lead to data silos, where the original raw data is not retained for future analysis.

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Pickl AI

DECEMBER 4, 2023

Marketing Targeted Campaigns Increases campaign effectiveness and ROI Data silos leading to inconsistent information. Implementing integrated data management systems. 6,20000 Analytical skills, proficiency in Data Analysis tools (e.g., Adopting agile risk management frameworks. 12,00000 Programming (e.g.,

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AWS Machine Learning Blog

MARCH 15, 2024

This is a guest blog post written by Nitin Kumar, a Lead Data Scientist at T and T Consulting Services, Inc. Duration of data informs on long-term variations and patterns in the dataset that would otherwise go undetected and lead to biased and ill-informed predictions. Much of this work comes down to the data.”

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