IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way. And that makes sense.

AI 93

AI AI Data Scientist Data Governance 93

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

Alation

JUNE 9, 2021

In this four-part blog series on data culture, we’re exploring what a data culture is and the benefits of building one, and then drilling down to explore each of the three pillars of data culture – data search & discovery, data literacy, and data governance – in more depth.

Data Governance 59

Data Governance Database Cloud Data Machine Learning 59

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Data Governance 59

Data Governance ML ML Cloud Data 59

Alation

FEBRUARY 13, 2020

Modern data catalogs—originated to help data analysts find and evaluate data—continue to meet the needs of analysts, but they have expanded their reach. They are now central to data stewardship, data curation, and data governance—all metadata dependent activities.

Data Lakes 52

Data Lakes Data Governance Data Science Data Analyst 52

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Pickl AI

AUGUST 9, 2024

Data Lake vs. Data Warehouse Distinguishing between these two storage paradigms and understanding their use cases. Students should learn how data lake s can store raw data in its native format, while data warehouses are optimised for structured data.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Pickl AI

SEPTEMBER 17, 2024

NoSQL Databases NoSQL databases like MongoDB or Cassandra are designed to handle unstructured or semi-structured data efficiently. Data Lakes Data lakes are centralised repositories that allow organisations to store all their structured and unstructured data at any scale.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

OCTOBER 10, 2024

Role of Data Transformation in Analytics, Machine Learning, and BI In Data Analytics, transformation helps prepare data for various operations, including filtering, sorting, and summarisation, making the data more accessible and useful for Analysts. Why Are Data Transformation Tools Important?

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

DagsHub

FEBRUARY 29, 2024

While data preparation for machine learning may not be the most “glamorous” aspect of a data scientist’s job, it is the one that has the greatest impact on the quality of model performance and consequently the business impact of the machine learning product or service.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Alation

FEBRUARY 20, 2020

At the start of our journey, we had no idea what combination of search, descriptions, crawling, indexing, interface design, and algorithms would enable people to most easily find, understand and trust data. Today, CDOs in a wide range of industries have a mechanism for empowering their organizations to leverage data.

Internet of Things 52

Internet of Things Data Lakes Analytics Analytics 52

IBM Journey to AI blog

JULY 7, 2023

An integrated data protection system can protect your assets by monitoring them, automating access control, setting up notifications, and auditing your password management. Put into place data protection tools such as data encryption algorithms, key management, redaction, data masking and erasure, and data resiliency.

Data Governance 40

Data Governance Data Lakes Database Cloud Computing 40

Pickl AI

DECEMBER 11, 2024

Begin by identifying bottlenecks in your existing pipeline, such as duplicate data collection points or slow processing times. Implement tools that allow real-time data integration and transformation to maintain accuracy and timeliness.

Data Silos 52

Data Silos Data Pipeline Artificial Intelligence Artificial Intelligence 52

Alation

AUGUST 2, 2022

Customer centricity requires modernized data and IT infrastructures. Too often, companies manage data in spreadsheets or individual databases. This means that you’re likely missing valuable insights that could be gleaned from data lakes and data analytics. They started by: Finding the right customer data.

Data Silos 52

Data Silos Data Lakes Data Analyst Data Scientist 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 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. Both persistent staging and data lakes involve storing large amounts of raw data. Looking for purchase data? New user sign-up?

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

Database 106

Database AWS Clustering Data Lakes 106