Pickl AI

OCTOBER 10, 2023

In this blog, we are going to unfold the two key aspects of data management that is Data Observability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications.

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Marktechpost

NOVEMBER 5, 2023

In this study, researchers from the Allen Institute for AI, the University of Washington and the University of California propose to use a collection of tools called WIMBD: WHAT’S IN MY BIG DATA, which helps practitioners rapidly examine massive language datasets to research the content of large text corpora. Data quality (e.g.,

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Flipboard

MARCH 13, 2023

Presented by BMC Poor data quality costs organizations an average $12.9 Organizations are beginning to recognize that not only does it have a direct impact on revenue over the long term, but poor data quality also increases the complexity of data ecosystems, and directly impacts the … million a year.

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Flipboard

JUNE 5, 2023

True data quality simplification requires transformation of both code and data, because the two are inextricably linked. Code sprawl and data siloing both imply bad habits that should be the exception, rather than the norm.

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AI News

DECEMBER 13, 2023

Phi-2’s achievements are underpinned by two key aspects: Training data quality: Microsoft emphasises the critical role of training data quality in model performance. Phi-2 leverages “textbook-quality” data, focusing on synthetic datasets designed to impart common sense reasoning and general knowledge.

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AI News

SEPTEMBER 24, 2024

The success of this AI-powered underwater vehicle at Seagreen wind farm not only demonstrates the potential of autonomous technology in offshore wind inspections but also sets a new standard for safety, efficiency, and data quality in the industry. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

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Analytics Vidhya

NOVEMBER 8, 2023

In the data-driven world […] The post Monitoring Data Quality for Your Big Data Pipelines Made Easy appeared first on Analytics Vidhya. Determine success by the precision of your charts, the equipment’s dependability, and your crew’s expertise. A single mistake, glitch, or slip-up could endanger the trip.

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Flipboard

JUNE 15, 2023

Data Engineers: We look into Data Engineering, which combines three core practices around Data Management, Software Engineering, and I&O. This focuses …

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AI News

NOVEMBER 21, 2023

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. Companies have struggled with data quality and data hygiene.

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Unite.AI

DECEMBER 23, 2023

Some of the APM strategies employed for this include: Predictive Maintenance: By using modern AI/ML capabilities to analyze big data , this strategy can monitor an asset’s health and forecast maintenance. Root Cause Analysis (RCA): This strategy emphasizes understanding the root causes of asset failures in a structured manner.

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IBM Journey to AI blog

FEBRUARY 28, 2024

Sourcing teams are automating processes like data analysis as well as supplier relationship management and transaction management. This helps reduce errors to improve data quality and response times to questions, which improves customer and supplier satisfaction.

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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Marktechpost

JUNE 5, 2024

In addition, organizations that rely on data must prioritize data quality review. Data profiling is a crucial tool. For evaluating data quality. Data profiling gives your company the tools to spot patterns, anticipate consumer actions, and create a solid data governance plan.

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ODSC - Open Data Science

APRIL 28, 2023

But before AI/ML can contribute to enterprise-level transformation, organizations must first address the problems with the integrity of the data driving AI/ML outcomes. The truth is, companies need trusted data, not just big data. That’s why any discussion about AI/ML is also a discussion about data integrity.

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Pickl AI

OCTOBER 6, 2024

This phase is crucial for enhancing data quality and preparing it for analysis. Transformation involves various activities that help convert raw data into a format suitable for reporting and analytics. Normalisation: Standardising data formats and structures, ensuring consistency across various data sources.

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Marktechpost

APRIL 1, 2024

Existing research emphasizes the significance of distributed processing and data quality control for enhancing LLMs. Utilizing frameworks like Slurm and Spark enables efficient big data management, while data quality improvements through deduplication, decontamination, and sentence length adjustments refine training datasets.

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IBM Journey to AI blog

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

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Pickl AI

JUNE 18, 2023

Hadoop has become a highly familiar term because of the advent of big data in the digital world and establishing its position successfully. The technological development through Big Data has been able to change the approach of data analysis vehemently. It offers several advantages for handling big data effectively.

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Pickl AI

JUNE 4, 2024

Big Data Analytics This involves analyzing massive datasets that are too large and complex for traditional data analysis methods. Big Data Analytics is used in healthcare to improve operational efficiency, identify fraud, and conduct large-scale population health studies.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

To quickly explore the loan data, choose Get data insights and select the loan_status target column and Classification problem type. The generated Data Quality and Insight report provides key statistics, visualizations, and feature importance analyses. Now you have a balanced target column. Huong Nguyen is a Sr.

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TransOrg Analytics

AUGUST 7, 2024

Challenges in Credit Risk Modeling Despite its importance, credit risk modelling faces several challenges: Data Quality and Availability: Accurate credit risk modelling relies on high-quality, comprehensive data. Only complete or updated data can lead to reliable predictions and informed decision-making.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

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Pickl AI

AUGUST 1, 2023

Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data. Data Integration: Combining data from multiple sources to create a unified view for analysis and decision-making.

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ODSC - Open Data Science

JANUARY 2, 2024

Join us in the city of Boston on April 24th for a full day of talks on a wide range of topics, including Data Engineering, Machine Learning, Cloud Data Services, Big Data Services, Data Pipelines and Integration, Monitoring and Management, Data Quality and Governance, and Data Exploration.

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Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Pragnakalp

APRIL 29, 2024

Step 3: Load and process the PDF data For this blog, we will use a PDF file to perform the QnA on it. We’ve selected a research paper titled “DEEP LEARNING APPLICATIONS AND CHALLENGES IN BIG DATA ANALYTICS,” which can be accessed at the following link: [link] Please download the PDF and place it in your working directory.

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ODSC - Open Data Science

APRIL 3, 2023

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

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Unite.AI

SEPTEMBER 22, 2023

Jay Mishra is the Chief Operating Officer (COO) at Astera Software , a rapidly-growing provider of enterprise-ready data solutions. I would say modern tool sets that are designed keeping in view the requirements of the new age data that we are receiving have changed in in past few years and the volume of course has changed.

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Pickl AI

JUNE 24, 2024

Introduction In the age of big data, where information flows like a relentless river, the ability to extract meaningful insights is paramount. Association rule mining (ARM) emerges as a powerful tool in this data-driven landscape, uncovering hidden patterns and relationships between seemingly disparate pieces of information.

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ODSC - Open Data Science

SEPTEMBER 29, 2023

Such growth makes it difficult for many enterprises to leverage big data; they end up spending valuable time and resources just trying to manage data and less time analyzing it. What are the big differentiators between HPCC Systems and other big data tools? Spark is indeed a popular big data tool.

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Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

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Pickl AI

JANUARY 23, 2024

18,00,000 Chief Data Officer As custodians of data governance, Chief Data Officers oversee the organisation’s data strategy. They enforce policies, ensuring data quality, security, and compliance. 6,30,000 Big Data Analyst Big Data Analysts manage vast datasets to extract valuable insights.

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AI News

SEPTEMBER 27, 2024

First is clear alignment of the data strategy with the business goals, making sure the technology teams are working on what matters the most to the business. Second, is data quality and accessibility, the quality of the data is critical. Poor data quality will lead to inaccurate insights.

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TransOrg Analytics

AUGUST 21, 2024

Such success stories have largely depended on Data Engineering processes. This article explores how data engineering can improve Customer 360 initiatives for AWS data engineering , big data engineering, and data analytics companies. What Are Customer 360 Initiatives?

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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. Big Data Analytics The ever-growing volume of healthcare data presents valuable insights.

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Pickl AI

AUGUST 21, 2024

This efficiency saves time and resources in data collection efforts. Improved Data Quality The interplay between crawling and scraping can enhance the overall quality of the data collected, as crawlers can help filter out irrelevant or duplicate content.

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Towards AI

JUNE 25, 2024

This is the first one, where we look at some functions for data quality checks, which are the initial steps I take in EDA. 🧰 The dummy data While Spark is famous for its ability to work with big data, for demo purposes, I have created a small dataset with an obvious duplicate issue. Let’s get started.

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