IBM Journey to AI blog

OCTOBER 22, 2024

The data universe is expected to grow exponentially with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase pressure to manage cloud costs efficiently and complicate governance of AI and data workloads.

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IBM Journey to AI blog

MAY 9, 2023

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1]

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IBM Journey to AI blog

OCTOBER 30, 2023

Thus, DB2 PureScale on AWS equips this insurance company to innovate and make data-driven decisions rapidly, maintaining a competitive edge in a saturated market. The platform provides an intelligent, self-service data ecosystem that enhances data governance, quality and usability.

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Alation

MARCH 29, 2022

The 1980s ushered in the antithesis of this version of computing — personal computing and distributed database management — but also introduced duplicated data and enterprise data silos. During the 1990s, attempts were made to tackle challenges including: Inefficient data silos. Subscribe to Alation's Blog.

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Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. It ensures the data is accurate and reliable, leading to better decision-making.

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Dataversity

DECEMBER 14, 2020

It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every […].

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phData

APRIL 18, 2023

Data, technology, and improved trade execution could all be utilized by businesses to increase investment returns, spur innovation, and provide better investor experiences. The data-sharing features of Snowflake enable enterprises to integrate their data without creating any data silos or building new technology capabilities.

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phData

SEPTEMBER 15, 2023

In order to unlock the potential of these tools, your CRM data must remain synced between Salesforce and Snowflake. Salesforce Sync Out offers an excellent and cost-efficient solution for seamlessly ingesting Salesforce data into Snowflake. Warehouse for loading the data (start with XSMALL or SMALL warehouses).

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phData

FEBRUARY 15, 2024

With the advent of cloud data warehouses and the ability to (seemingly) infinitely scale analytics on an organization’s data, centralizing and using that data to discover what drives customer engagement has become a top priority for executives across all industries and verticals. What Are the Challenges With Fan 360?

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IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

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phData

DECEMBER 4, 2024

In this blog, we aim to demystify generative AI for manufacturing companies , offering a clear path to implementing generative AI use cases in your business. Like most Gen AI use cases, the first step to achieving customer service automation is to clean and centralize all information in a data warehouse for your AI to work from.

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DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. According to Flexera 1 , 92% of enterprises have a multi-cloud strategy, while 80% have a hybrid cloud strategy.

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IBM Journey to AI blog

FEBRUARY 19, 2024

Figure 2: The data product lifecycle The banking industry, for example, faces the following challenges: Competition from agile and innovative financial technology and challenger banks. Organizational data silos that impede a unified customer experience. High degree of regulatory control. Need to protect sensitive information.

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IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Learn more about the benefits of data fabric and IBM Cloud Pak for Data.

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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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Pickl AI

JUNE 28, 2023

But, this data is often stored in disparate systems and formats. Here comes the role of Data Mining. Read this blog to know more about Data Integration in Data Mining, The process encompasses various techniques that help filter useful data from the resource. Thus enabling better decision-making.

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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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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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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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Alation

APRIL 19, 2022

Data modernization is the process of transferring data to modern cloud-based databases from outdated or siloed legacy databases, including structured and unstructured data. In that sense, data modernization is synonymous with cloud migration. Subscribe to Alation's Blog.

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Dataversity

AUGUST 5, 2021

According to Gartner, data fabric is an architecture and set of data services that provides consistent functionality across a variety of environments, from on-premises to the cloud. Data fabric simplifies and integrates on-premises and cloud Data Management by accelerating digital transformation.

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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.

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phData

JULY 14, 2023

However, the data that can shed light on these insights are often fragmented across various platforms and services, posing challenges in creating comprehensive customer profiles. In this blog, I’ll briefly overview the Adtech industry, how the Snowflake Data Cloud fits into it, and showcase Adteck companies already making use of it.

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phData

FEBRUARY 27, 2024

The Snowflake Data Cloud is a cloud-based data warehouse that is becoming increasingly popular among businesses of all sizes. In this blog, we’ll review the pros and cons of single accounts versus multiple accounts and use cases for both. Establish data governance guidelines.

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Alation

JANUARY 27, 2022

The proliferation of data sources means there is an increase in data volume that must be analyzed. Large volumes of data have led to the development of data lakes , data warehouses, and data management systems. Despite its immense value, a variety of data can create more work.

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Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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phData

AUGUST 11, 2023

In the long run, hospitals can save a lot more lives and operational expenses when applying a data engineering strategy. In this blog, we will cover the advancements made in data engineering technology that aims to help navigate the challenges and opportunities of implementing a data engineering strategy in the healthcare industry.

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Dataversity

NOVEMBER 1, 2021

Many in enterprise Data Management know the challenges that rapid business growth can present. Whether through acquisition or organic growth, the amount of enterprise data coming into the organization can feel exponential as the business hires more people, opens new locations, and serves new customers. The enterprise […].

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phData

NOVEMBER 13, 2023

In this blog, we’ll spotlight the transformative announcements that emerged from the Coalesce Conference. Join us as we navigate the key takeaways defining the future of data transformation. dbt Mesh Enterprises today face the challenge of managing massive, intricate data projects that can slow down innovation.

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Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. SageMaker Unified Studio provides a unified experience for using data, analytics, and AI capabilities.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Instead, a core component of decentralized clinical trials is a secure, scalable data infrastructure with strong data analytics capabilities. Amazon Redshift is a fully managed cloud data warehouse that trial scientists can use to perform analytics.

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Pickl AI

APRIL 8, 2025

Summary: Dimensions in a data warehouse provide context to facts. Types of Dimensions in Data Warehouse include conformed, role-playing, slowly changing, junk, and degenerate dimensions. Understanding these types is crucial for efficient data warehouse design. Improves data quality and consistency.

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