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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Precisely

FEBRUARY 12, 2024

All that time spent on data preparation has an opportunity cost associated with it. Data Governance Drives Insights Data governance provides an important framework. Location-based data is often subject to additional regulatory requirements as well, further adding to the challenges of spatial data governance.

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Precisely

JULY 18, 2024

This satisfies the needs of data owners, who require a simple way to make data products available to users and keep them up to date, and data users who demand user-friendly, self-service methods for finding and accessing trusted data.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52

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 […].

DataOps 59

DataOps Data Pipeline Data Warehouse Big Data 59

Pickl AI

JULY 24, 2024

Employ data validation and error handling mechanisms during data entry to prevent issues from propagating. Data profiling provides valuable insights into data characteristics, enabling identification of potential quality problems.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

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

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Pickl AI

JULY 16, 2024

Data Preparation AIOps thrives on clean, consistent, and readily accessible data. Here’s what you need to consider: Data integration: Ensure your data from various IT systems (applications, networks, security tools) is integrated and readily accessible for AIOps tools to analyze.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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. Choose Data sources and import the assets by choosing Run.

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Precisely

SEPTEMBER 26, 2024

Without proper data preparation, you risk issues like bias and hallucination, inaccurate predictions, poor model performance, and more. “If If you do not have AI-ready data, then you’re more than likely to experience some of these challenges,” says Cotroneo. Is it contextualized with necessary third-party data?

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AI AI Data Governance Data Quality 75