Precisely

MARCH 6, 2023

Read Financial Services ESG Reporting: The essential role of data Financial organizations are now assessing how they can derive more environmental, social and governance insights, while optimizing reporting through their existing infrastructure.

Data Integration 52

Data Integration Government Data Governance Datasets 52

Precisely

JANUARY 9, 2023

With global data creation projected to grow to more than 180 zettabytes by 2025 , it’s not surprising that more organizations than ever are looking to harness their ever-growing datasets to drive more confident business decisions.

Data Integration 81

Data Integration Data Governance Government Data 81

Ascend.io

MAY 23, 2023

A prime example of such patterns is orphaned datasets. These are datasets that exist in a database or data storage system but no longer have a relevant link or relationship to other data, to any of the analytics, or to the main application — making them a deceptively challenging issue to tackle.

Datasets 52

Datasets Data Pipeline Metadata Database 52

Precisely

JULY 17, 2023

And yet less than half (46%) rate their ability to trust data for decision-making as “high” or “very high.” Accurate, confident decision-making requires trusted data. And trusted data needs data integrity – maximum accuracy, consistency, and context. That’s where the Precisely Data Integrity Suite comes in.

Data Integration 52

Data Integration Data Cleanse Data Governance Government 52

Precisely

MARCH 23, 2023

You need a flexible framework to efficiently identify, understand, and link the underlying data elements required for accurate, consistent, and contextualized ESG reporting. In summary: your ESG data needs data integrity. The post Why You Need Data Integrity for ESG Reporting appeared first on Precisely.

Data Integration 52

Data Integration Government High Quality Data Data 52

Ascend.io

APRIL 12, 2023

So when we talk about making data usable, we’re having a conversation about data integrity. Data integrity is the overall readiness to make confident business decisions with trustworthy data, repeatedly and consistently. Data integrity is vital to every company’s survival and growth.

Data Pipeline 52

Data Pipeline Data Integration Datasets Data 52

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. Deploy, execute, and scale natively in modern cloud architectures To meet the need for data quality in the cloud head on, we’ve developed the Precisely Data Integrity Suite.

Data Integration 52

Data Integration High Quality Data BI Data 52

Grouparoo

DECEMBER 7, 2021

Integrity is a critical aspect of data processing; if the integrity of the data is unknown, the trustworthiness of the information it contains is unknown. What is Data Integrity? Data integrity is the accuracy and consistency over the lifetime of the content and format of a data item.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

Precisely

MAY 16, 2024

Ensure your data is accurate, consistent, and contextualized to enable trustworthy AI systems that avoid biases, improve accuracy and reliability, and boost contextual relevance and nuance. Adopt strategic practices in data integration, quality management, governance, spatial analytics, and data enrichment.

Amazon Web Services 59

Amazon Web Services AWS Data Integration Datasets 59

Monte Carlo

MARCH 24, 2023

However, the data is not valid because the height information is incorrect – penguins have the height data for giraffes, and vice versa. The data doesn’t accurately represent the real heights of the animals, so it lacks validity. What is Data Integrity? How Do You Maintain Data Integrity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Data Engineering Podcast

JULY 3, 2022

Random data doesn’t do it — and production data is not safe (or legal) for developers to use. What if you could mimic your entire production database to create a realistic dataset with zero sensitive data? Random data doesn’t do it — and production data is not safe (or legal) for developers to use.

Data Integration 100

Data Integration MongoDB Scala MySQL 100

Christophe Blefari

OCTOBER 20, 2023

a lea prepare command that creates database objects that needs to be created (dataset, schema, etc.). 25 million Creative Commons image dataset released — Fondant, an open-source processing framework, released publicly available images from web crawling with their associated license. What are the main differences?

Generalist 130

Generalist Entertainment NoSQL Datasets 130

Databand.ai

MAY 30, 2023

Data quality can be influenced by various factors, such as data collection methods, data entry processes, data storage, and data integration. Maintaining high data quality is crucial for organizations to gain valuable insights, make informed decisions, and achieve their goals.

Data Cleanse 85

Data Cleanse Datasets Data Governance Data Validation 85

ThoughtSpot

MARCH 5, 2024

When created, Snowflake materializes query results into a persistent table structure that refreshes whenever underlying data changes. These tables provide a centralized location to host both your raw data and transformed datasets optimized for AI-powered analytics with ThoughtSpot.

BI 94

BI Datasets Raw Data SQL 94

Knowledge Hut

APRIL 23, 2024

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Big data offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Data Engineering Weekly

APRIL 7, 2024

We index only top-tier tables, promoting the use of these higher-quality datasets. I strongly believe the concept of Data Product will play a bigger role in data engineering. It is evident that it will become the foundation of trusted sources, which is essential to taking advantage of advancements from LLMs.

Data Engineering 70

Data Engineering Data Engineer Engineering Unstructured Data 70

Hevo

MARCH 28, 2024

However, deploying and managing them requires dealing with a huge data volume. This is where data integration tools come into the picture. Data integration tools help export, transform, and load datasets from diverse sources, simplifying data analysis. But […]

AWS 40

AWS Data Integration Datasets Data Analysis 40

Precisely

JUNE 5, 2023

As more organizations prioritize data-driven decision-making, the pressure mounts for data teams to provide the highest quality data possible for the business. Reach new levels of data quality and deeper analysis – faster So then, what are the options for data practitioners?

Datasets 52

Datasets Data Integration Data Cloud 52

Precisely

MARCH 7, 2024

Insurance carriers need to avoid those scenarios by proactively managing data quality. They also need data observability tools that allow them to trace errors back to their source and rectify the problem. eBook Achieving Data Integrity: A Guide for Insurers Technology-driven insights and capabilities depend on trusted data.

Insurance 72

Insurance Process Medical Data Governance 72

Knowledge Hut

MARCH 27, 2024

Primarily used for organizing and optimizing data to perform specific operations within a program efficiently. Relationships Allows the establishment of relationships between different tables, supporting data integrity and normalization. Flexibility: Offers scalability to manage extensive datasets efficiently.

Database 52

Database Algorithm Relational Database PostgreSQL 52

Precisely

NOVEMBER 20, 2023

The answers lie in data integrity and the contextual richness of the data that fuels your AI. If machine learning models have been trained on untrustworthy data, fixing the problem can be expensive and time-consuming. Contextual data. Data integrity is multifaceted.

Raw Data 121

Raw Data Insurance Data Retail 121

Knowledge Hut

APRIL 23, 2024

These abilities are necessary to ensure that data is organized, secure, and readily accessible. A solid foundation in database management enables professionals to deal with large datasets and interpret intricate data structures. This allows businesses to extract valuable insights from their data.

Database 52

Database Management Relational Database SQL 52

Data Engineering Weekly

MAY 5, 2024

The motivation for Machine Unlearning is critical from the privacy perspective and for model correction, fixing outdated knowledge, and access revocation of the training dataset. A key thought-provoking moment for me while reading the article is this quote.

Data Engineering 67

Data Engineering Data Engineer Engineering Google Cloud 67

Precisely

APRIL 11, 2023

It’s critical that business analysts have the data they need and that IT has the appropriate metadata associated with those datasets for seamless replication into the cloud. That’s why a data catalog is critical to any organization – particularly if you run analysis and reports in cloud data platforms.

Accessible 52

Accessible Accessibility Metadata Data Integration 52

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Volume Big Data refers to large volumes of data, typically in the order of terabytes or petabytes. It involves processing and analyzing massive datasets that cannot be managed with traditional data processing techniques.

Big Data 52

Big Data Datasets Data Analysis Media 52

Precisely

SEPTEMBER 11, 2023

At Precisely, we recognize the value and potential of AI to help our customers work faster and smarter, and make more powerful, confident decisions grounded in trusted data – supporting our overall mission of unlocking data integrity for organizations of all kinds.

Metadata 52

Metadata Data Integration Datasets Data Analysis Tools 52

Precisely

SEPTEMBER 25, 2023

Read Turning Raw Data into Meaningful Insights Even though organizations value data-driven decision-making more than ever before, data quality remains a major barrier across industries. So how does the data validation process help on the journey to better data quality and ultimately, data integrity?

Data Validation 100

Data Validation Process Raw Data Data Cleanse 100

Databand.ai

AUGUST 30, 2023

Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content. This is part of a series of articles about data quality. In this article: Why Are Data Testing Tools Important?

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Precisely

SEPTEMBER 12, 2023

As a proud member of the Connect with Confluent program , we help organizations going through digital transformation and IT infrastructure modernization break down data silos and power their streaming data pipelines with trusted data. That’s where our Precisely data integrity solutions come in.

Kafka 52

Kafka Data Integration Data Pipeline Datasets 52

Databand.ai

AUGUST 30, 2023

Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content. This is part of a series of articles about data quality. In this article: Why are data testing tools important?

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Striim

MAY 1, 2024

The UPS DeliveryDefense program utilizes a sophisticated technical setup, starting with the direct upload of varied datasets into BigQuery. This platform acts as the primary structured data repository in Google Cloud. These enriched datasets are merged in BigQuery for seamless Google Cloud integration.

Google Cloud 52

Google Cloud Insurance Finance Machine Learning 52

Precisely

FEBRUARY 23, 2024

Perhaps one of the most important advantages of high data quality arises from the use of advanced analytics for risk assessment. Using curated datasets that include information about insect and disease damage to vegetation, fire experts can gain a better understanding of conditions on the ground.

Insurance 64

Insurance Data Validation Government High Quality Data 64

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. Is the business logic producing correct outcomes?

Raw Data 40

Raw Data Data Ingestion Datasets Data 40

Analytics Vidhya

AUGUST 22, 2023

Introduction In today’s data-driven world, seamless data integration plays a crucial role in driving business decisions and innovation. Two prominent methodologies have emerged to facilitate this process: Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT).

Data Integration 185

Data Integration Process Datasets Data 185

Knowledge Hut

JANUARY 30, 2024

In summary, data extraction is a fundamental step in data-driven decision-making and analytics, enabling the exploration and utilization of valuable insights within an organization's data ecosystem. What is the purpose of extracting data? The process of discovering patterns, trends, and insights within large datasets.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

FreshBI

SEPTEMBER 11, 2023

Power BI, Microsoft's cutting-edge business analytics solution, empowers users to visualize data and seamlessly distribute insights. However, the complex process of data preparation, modeling, and report creation can be time and resource consuming, especially when handling intricate datasets.

BI 59

BI Consulting Datasets Data Ingestion 59

Towards Data Science

FEBRUARY 16, 2024

Strategically enhancing address mapping during data integration using geocoding and string matching Many individuals in the big data industry may encounter the following scenario: Is the acronym “TIL” equivalent to the phrase “Today I learned” when extracting these two entries from distinct systems?

Food 64

Food Data Ingestion Python Data Science 64