Pickl AI

JANUARY 28, 2025

Summary: Understanding Business Intelligence Architecture is essential for organizations seeking to harness data effectively. This framework includes components like data sources, integration, storage, analysis, visualization, and information delivery. What is Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

phData

SEPTEMBER 19, 2023

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

In today’s digital world, data is king. Organizations that can capture, store, format, and analyze data and apply the business intelligence gained through that analysis to their products or services can enjoy significant competitive advantages. But, the amount of data companies must manage is growing at a staggering rate.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Dataconomy

SEPTEMBER 4, 2023

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence. Understand what insights you need to gain from your data to drive business growth and strategy.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

ODSC - Open Data Science

FEBRUARY 24, 2023

Data Quality Now that you’ve learned more about your data and cleaned it up, it’s time to ensure the quality of your data is up to par. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

IBM Journey to AI blog

JULY 3, 2024

This includes integration with your data warehouse engines, which now must balance real-time data processing and decision-making with cost-effective object storage, open source technologies and a shared metadata layer to share data seamlessly with your data lakehouse.

AI 45

AI AI Data Quality Database 45

Alation

JULY 19, 2022

Finally, a data catalog can help data scientists find answers to their questions (and avoid re-asking questions that have already been answered). Modern data catalogs surface a wide range of data asset types. Modern data catalogs also facilitate data quality checks.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

ODSC - Open Data Science

JANUARY 30, 2024

Think of it as building plumbing for data to flow smoothly throughout the organization. EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

AWS Machine Learning Blog

NOVEMBER 15, 2023

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement.

AWS 117

AWS Analytics Analytics ML 117

IBM Journey to AI blog

DECEMBER 7, 2023

Automated data preparation and cleansing : AI-powered data preparation tools will automate data cleaning, transformation and normalization, reducing the time and effort required for manual data preparation and improving data quality.

Data Preparation 72

Data Preparation Database AI AI 72

IBM Journey to AI blog

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

AI 93

AI AI Data Scientist Data Governance 93

Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. Business intelligence reports. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

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.

AWS 93

AWS Database ETL AI 93

DagsHub

NOVEMBER 11, 2024

In general, this data has no clear structure because it may manifest real-world complexity, such as the subtlety of language or the details in a picture. Advanced methods are needed to process unstructured data, but its unstructured nature comes from how easily it is made and shared in today's digital world.

AI 52

AI AI Data Lakes Database 52

Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

MARCH 21, 2023

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So

Analytics 52

Analytics Analytics Data Silos Big Data 52

Alation

JANUARY 26, 2023

Lack of data governance can summon a whole range of problems, including: Lack of consistency For data to be useful, it should be consistent across all areas. A field might not be entered in the same way across different departments, which makes the data difficult to find and affects the accuracy of business intelligence (BI).

Data Governance 52

Data Governance Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

MAY 17, 2023

Here are some specific reasons why they are important: Data Integration: Organizations can integrate data from various sources using ETL pipelines. This provides data scientists with a unified view of the data and helps them decide how the model should be trained, values for hyperparameters, etc.

ETL 59

ETL Data Pipeline ML ML 59

The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

IBM Journey to AI blog

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. Then, it applies these insights to automate and orchestrate the data lifecycle.

Data Lakes 73

Data Lakes AI AI Data Governance 73