IBM Journey to AI blog

OCTOBER 22, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges, specifically as the growth of data spans multiple formats: structured, semistructured and unstructured.

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Precisely

JUNE 27, 2024

Minimize data input The less data that you have going into ETL process, the faster and cleaner your results are likely to be. That’s why you want to strip out any unnecessary data as early in the ETL process as possible. Maximize data quality The old saying “crap in, crap out” applies to ETL integration.

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Alation

OCTOBER 5, 2021

It’s on Data Governance Leaders to identify the issues with the business process that causes users to act in these ways. Inconsistencies in expectations can create enormous negative issues regarding data quality and governance. Roadblock #3: Silos Breed Misunderstanding. Picking the Right Data Governance Tools.

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IBM Journey to AI blog

JULY 3, 2024

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata.

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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. Insufficient skills, limited budgets, and poor data quality also present significant challenges. To learn more, read our ebook.

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IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. This situation will exacerbate data silos, increase costs and complicate the governance of AI and data workloads.

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Pickl AI

JUNE 28, 2023

Understanding Data Integration in Data Mining Data integration is the process of combining data from different sources. Thus creating a consolidated view of the data while eliminating data silos. It involves mapping and transforming data elements to align with a unified schema.

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AWS Machine Learning Blog

MARCH 15, 2024

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. Breaking down these data silos to unite the untapped potential of the scattered data can save and transform many lives. Much of this work comes down to the data.”

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phData

SEPTEMBER 20, 2023

A data mesh is a decentralized approach to data architecture that’s been gaining traction as a solution to the challenges posed by large and complex data ecosystems. It’s all about breaking down data silos, empowering domain teams to take ownership of their data, and fostering a culture of data collaboration.

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Precisely

FEBRUARY 2, 2023

This requires access to data from across business systems when they need it. Data silos and slow batch delivery of data will not do. Stale data and inconsistencies can distort the perception of what is really happening in the business leading to uncertainty and delay.

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Pickl AI

OCTOBER 6, 2024

By employing ETL, businesses ensure that their data is reliable, accurate, and ready for analysis. This process is essential in environments where data originates from various systems, such as databases , applications, and web services. The key is to ensure that all relevant data is captured for further processing.

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Pickl AI

NOVEMBER 15, 2023

What Is Data Lake? A Data Lake is a centralized repository that allows businesses to store vast volumes of structured and unstructured data at any scale. Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible.

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Pickl AI

DECEMBER 4, 2023

Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence. Think of data governance as the rules and regulations governing the kingdom of information. It ensures data quality , integrity, and compliance.

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Precisely

JULY 18, 2024

Here are four aspects of a data management approach that you should consider to increase the success of an architecture: Break down data silos by automating the integration of essential data – from legacy mainframes and midrange systems, databases, apps, and more – into your logical data warehouse or data lake.

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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.

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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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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.

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Pickl AI

JUNE 13, 2024

Here, we have highlighted the concerning issues like usability, data quality, and clinician trust. Data Quality The accuracy of CDSS recommendations hinges on the quality of patient data fed into the system. This can create data silos and hinder the flow of information within a healthcare organization.

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phData

SEPTEMBER 27, 2024

. “ This sounds great in theory, but how does it work in practice with customer data or something like a ‘composable CDP’? Well, implementing transitional modeling does require a shift in how we think about and work with customer data. It often involves specialized databases designed to handle this kind of atomic, temporal data.

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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). Technology.

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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.

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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. Deploy jobs are designed to build into production databases, running sequentially to prevent conflicts.

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Alation

APRIL 19, 2022

Data producers and consumers alike are working from home and hybrid locations more often. And in an increasingly remote workforce, people need to access data systems easily to do their jobs. This might mean that they’re accessing a database from a smartphone, computer, or tablet. Today, data dwells everywhere.

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Precisely

OCTOBER 12, 2023

Creating data observability routines to inform key users of any changes or exceptions that crop up within the data, enabling a more proactive approach to compliance. Regulations like GDPR and CCPA, for example, require institutions to permit their customers to remove select types of personal information from internal databases.

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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. You will see a new database dev@ in the managed Amazon Redshift Serverless workgroup.

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phData

JUNE 20, 2024

Establishing a data culture changes this paradigm. Data pipelines are standardized to ingest data to Snowflake to provide consistency and maintainability. Data transformation introduces data quality rules, such as with dbt or Matillion, to establish trust that data is ready for consumption.

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Data Governance Analytics Analytics Power BI 52

Dataconomy

MARCH 18, 2025

Data ingestion Data ingestion involves collecting various forms of datastructured, unstructured, and semi-structuredfrom multiple sources. This can include data at rest, such as databases, and data in motion, like live streaming data from IoT devices.

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