AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 111

Metadata Data Lake Visualization Data Transformation 111

erwin

APRIL 29, 2020

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

CIO Business Intelligence

JUNE 23, 2022

Yet as companies fight for skilled analyst roles to utilize data to make better decisions , they often fall short in improving the data supply chain and resulting data quality. Without a solid data supply-chain management practices in place, data quality often suffers. First mile/last mile impacts.

Data Quality 98

Data Quality Key Performance Indicator Metrics KPI 98

AWS Big Data

NOVEMBER 9, 2023

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes. usr/local/airflow/.local/bin/dbt

Data Warehouse 126

Data Warehouse Testing Data Quality Reporting 126

CIO Business Intelligence

DECEMBER 22, 2022

Databricks Lakehouse Platform: a data management platform that unifies data warehousing and AI use cases Datafold: A data quality platform for detecting and fixing data quality issues DataKitchen: A data observability and automation platform that orchestrates end-to-end multi-tool, multi-environment data pipelines Dbt: A data transformation tool for (..)

Analytics 130

Analytics Machine Learning Software Data mining 130

CIO Business Intelligence

JANUARY 24, 2024

“Establishing data governance rules helps organizations comply with these regulations, reducing the risk of legal and financial penalties. Clear governance rules can also help ensure data quality by defining standards for data collection, storage, and formatting, which can improve the accuracy and reliability of your analysis.”

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

IBM Big Data Hub

JANUARY 9, 2024

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. How does Data Virtualization manage data quality requirements?

Visualization 141

Visualization Cost-Benefit Big Data Prescriptive Analytics 141

CIO Business Intelligence

FEBRUARY 16, 2023

But to augment its various businesses with ML and AI, Iyengar’s team first had to break down data silos within the organization and transform the company’s data operations. Digitizing was our first stake at the table in our data journey,” he says. The offensive side?

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. Data engineers are crucial for schema conversion and data transformation, and DBAs can handle cluster configuration and workload monitoring. Platform architects define a well-architected platform.

Data Warehouse 113

Data Warehouse KPI Optimization Cost-Benefit 113

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

CIO Business Intelligence

DECEMBER 6, 2023

Airbus was conceiving an ambitious plan to develop an open aviation data platform, Skywise, as a single platform of reference for all major aviation players that would enable them to improve their operational performance and business results and support Airbus’ own digital transformation.

Digital Transformation 116

Digital Transformation Software Cost-Benefit Manufacturing 116

Smart Data Collective

APRIL 29, 2020

The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. Microsoft Azure.

Management 88

Management Data Warehouse Digital Transformation Dashboards 88

AWS Big Data

FEBRUARY 8, 2023

It’s common to ingest multiple data sources into Amazon Redshift to perform analytics. Often, each data source will have its own processes of creating and maintaining data, which can lead to data quality challenges within and across sources. Answering questions as simple as “How many unique customers do we have?”

Data Quality 97

Data Quality Testing Data Warehouse Unstructured Data 97

erwin

MARCH 4, 2021

In addition to drivers like digital transformation and compliance, it’s really important to look at the effect of poor data on enterprise efficiency/productivity.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

Jen Stirrup

SEPTEMBER 30, 2021

Data Understanding is a crucial aspect of all of these areas, and the process will not proceed properly without it. From the perspective of CRISP-DM, this piece involves a number of activities: Collecting Initial Data Describing Data Exploring Data Verifying Data Quality.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

IBM Big Data Hub

FEBRUARY 9, 2023

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

Data Governance 76

Data Governance Business Objectives Data Quality Measurement 76

AWS Big Data

JULY 27, 2023

DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code. The over 200 transformations it provides are now available to be used in an AWS Glue Studio visual job. Now that we identified the data quality issues to address, we need to decide how to deal with each case.

Visualization 98

Visualization Cost-Benefit Data Quality Interactive 98

Sisense

OCTOBER 15, 2020

Every data professional knows that ensuring data quality is vital to producing usable query results. Streaming data can be extra challenging in this regard, as it tends to be “dirty,” with new fields that are added without warning and frequent mistakes in the data collection process.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Octopai

JUNE 22, 2023

On the other hand, centralized data management emphasizes a more structured and governed approach. Data is managed and controlled by a dedicated team of data professionals, ensuring data quality, security, and compliance. This approach offers greater control and reduces the risk of data inconsistencies.

Management 52

Management Data-driven Data Quality Data Governance 52

Alation

OCTOBER 18, 2022

Yet every dbt transformation contains vital metadata that is not captured – until now. Making this data visible in the data catalog will let data teams share their work, support re-use, and empower everyone to better understand and trust data. Data Transformation in the Modern Data Stack.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

AWS Big Data

DECEMBER 21, 2023

AWS Glue provides both visual and code-based interfaces to make data integration effortless. Using a native AWS Glue connector increases agility, simplifies data movement, and improves data quality. This enables organizations to streamline data integration and analytics with OpenSearch Service.

Analytics 111

Analytics IT Data Lake Visualization 111

Ontotext

NOVEMBER 18, 2021

OntoRefine is a data transformation tool that lets you unite plenty of data formats and get them into your triplestore. Ensuring data quality with SHACL. Our previous step covers data format differences. Dealing with spreadsheets via OntoRefine. Surveyors already use spreadsheets. Everyone makes mistakes.

Visualization 98

Visualization Reporting Metadata Enterprise 98

Ontotext

MARCH 20, 2024

Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. Studies reveal that businesses lose significant time and opportunities due to missing integrations and poor data quality and accessibility.

Data-driven 52

Data-driven Strategy Sales Data Integration 52

AWS Big Data

APRIL 19, 2023

Background The success of a data-driven organization recognizes data as a key enabler to increase and sustain innovation. The goal of a data product is to solve the long-standing issue of data silos and data quality. It follows what is called a distributed system architecture.

Technology 105

Technology Data-driven Machine Learning Sales 105

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. So questions linger about whether transformed data can be trusted.

Data Governance 40

Data Governance Risk Metadata Management 40

Octopai

JUNE 9, 2024

For HealthCo, this meant they could finally see how data moved from its source through various transformations to its final destination. This visibility was crucial for identifying and rectifying data quality issues quickly, ensuring consistent and reliable insights.

IT 52

IT Data-driven Predictive Analytics Data Strategy 52

Octopai

JANUARY 19, 2021

With Octopai’s support and analysis of Azure Data Factory, enterprises can now view complete end-to-end data lineage from Azure Data Factory all the way through to reporting for the first time ever.

Metadata 52

Metadata ROI Optimization Machine Learning 52

Ontotext

MARCH 27, 2024

It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards. Increasingly, organizations are using both.

Modeling 53

Modeling Metadata Data Quality Enterprise 53

DataKitchen

DECEMBER 9, 2022

Some of the key benefits of DataOps include: Improved speed and reliability: By automating and streamlining data-related tasks and processes, DataOps can help organizations to accelerate the development and deployment of data-driven solutions, and to improve the reliability of their data analytics and machine learning initiatives.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

Octopai

AUGUST 11, 2024

Just as a navigation app provides a detailed map of roads, guiding you from your starting point to your destination while highlighting every turn and intersection, data flow lineage offers a comprehensive view of data movement and transformations throughout its lifecycle.

Metadata 40

Metadata Visualization Data Quality Data Governance 40

Alation

MARCH 22, 2022

As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. It synthesizes all we’ve learned about agile, data quality , and ETL/ELT. They can better understand data transformations, checks, and normalization. Transparency is key.

Metadata 52

Metadata Cost-Benefit Data Quality Data Lake 52

Alation

AUGUST 11, 2022

But there are only so many data engineers available in the market today; there’s a big skills shortage. So to get away from that lack of data engineers, what data mesh says is, ‘Take those business logic data transformation capabilities and move that to the domains.’ Let’s take data privacy as an example.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

IBM Big Data Hub

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.

Cost-Benefit 75

Cost-Benefit Metadata Optimization Management 75

AWS Big Data

MARCH 3, 2023

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

Software 52

Software Data Lake Testing Cost-Benefit 52