Wayne Yaddow

FEBRUARY 6, 2025

Common challenges and practical mitigation strategies for reliable data transformations. Photo by Mika Baumeister on Unsplash Introduction Data transformations are important processes in data engineering, enabling organizations to structure, enrich, and integrate data for analytics , reporting, and operational decision-making.

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Testing Data Transformation Data-driven Manufacturing 52

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? In forecasting future events.

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. This includes the ETL processes that capture source data, the functional refinement and creation of data products, the aggregation for business metrics, and the consumption from analytics, business intelligence (BI), and ML.

Data Warehouse 105

Data Warehouse KPI Optimization Cost-Benefit 105

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 96

Data Lake Analytics Snapshot Data Quality 96

CIO Business Intelligence

JANUARY 24, 2024

At Vanguard, “data and analytics enable us to fulfill on our mission to provide investors with the best chance for investment success by enabling us to glean actionable insights to drive personalized client experiences, scale advice, optimize investment and business operations, and reduce risk,” Swann says.

Data Strategy 136

Data Strategy Strategy Unstructured Data Data Governance 136

DataKitchen

DECEMBER 9, 2022

By providing real-time visibility into the performance and behavior of data-related systems, DataOps observability enables organizations to identify and address issues before they become critical, and to optimize their data-related workflows for maximum efficiency and effectiveness.

Machine Learning 130

Machine Learning Data-driven Optimization Data Analytics 130

CIO Business Intelligence

DECEMBER 6, 2023

Notably, a partner with global reach can be particularly valuable to an organisation with operations with a global presence; since the structure of most multinational organisations is optimised to support their core business rather than initiatives like digital transformation.

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Digital Transformation Software Cost-Benefit Manufacturing 116

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?

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Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

Sisense

OCTOBER 15, 2020

Let’s look at a few ways that different industries take advantage of streaming data. How industries can benefit from streaming data. Automotive: Monitoring connected, autonomous cars in real time to optimize routes to avoid traffic and for diagnosis of mechanical issues. Optimizing object storage. Cleaning up dirty data.

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Dashboards IoT Optimization Internet of Things 98

Octopai

JUNE 9, 2024

By doing so, they aimed to drive innovation, optimize operations, and enhance patient care. They invested heavily in data infrastructure and hired a talented team of data scientists and analysts. 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. About Octopai: Octopai was founded in 2015 by BI professionals who realized the need for dynamic solutions in a stagnant market.

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Metadata ROI Machine Learning Data Quality 52

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.

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Visualization Cost-Benefit Data Quality Publishing 94

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. With Netezza support for 1.2

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Cost-Benefit Metadata Optimization Management 75

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

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.

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Modeling Metadata Data Quality Enterprise 53

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

OCTOBER 28, 2021

It defines how data can be collected and used within an organization, and empowers data teams to: Maintain compliance, even as laws change. Uncover intelligence from data. Protect data at the source. Put data into action to optimize the patient experience and adapt to changing business models.

Data Governance 52

Data Governance Measurement Data Quality Metrics 52

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

DataRobot Blog

MARCH 10, 2022

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

AWS Big Data

DECEMBER 4, 2024

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Metadata 98

Metadata Data Governance Data Quality Data-driven 98

DataKitchen

APRIL 26, 2021

At Workiva, they recognized that they are only as good as their data, so they centered their initial DataOps efforts around lowering errors. Hodges commented, “Our first focus was to up our game around data quality and lowering errors in production. GSK’s DataOps journey paralleled their data transformation journey.

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Measurement Metrics Data-driven Dashboards 246

Jet Global

MAY 28, 2019

Databases can be stored either on a local server or in the cloud and can be access for reporting in many different ways, through limited native tools included with the system collecting the data itself, to Excel exports or various direct connectivity options. Let’s look at why: Data Quality and Consistency. Enter the Warehouse.

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Data Warehouse Reporting Business Intelligence Sales 85

CIO Business Intelligence

NOVEMBER 7, 2023

Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. The aim is to normalize, aggregate, and eventually make available to analysts across the organization data that originates in various pockets of the enterprise.

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Data Warehouse Digital Transformation Data-driven Reporting 142

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

Smarten

AUGUST 4, 2023

Gartner predicts that, ‘data preparation will become a critical capability in more than 60% of data integration, analytics/BI, data science, data engineering and data lake enablement platforms.’

Data Lake 52

Data Lake Machine Learning Data Integration Data Quality 52

AWS Big Data

DECEMBER 17, 2024

Prevent the inclusion of invalid values in categorical data and process data without any data loss. Conduct data quality tests on anonymized data in compliance with data policies Conduct data quality tests to quickly identify and address data quality issues, maintaining high-quality data at all times.

Data Quality 74

Data Quality Testing Metrics Optimization 74

Alation

NOVEMBER 22, 2022

What Is Data Governance In The Public Sector? Effective data governance for the public sector enables entities to ensure data quality, enhance security, protect privacy, and meet compliance requirements. With so much focus on compliance, democratizing data for self-service analytics can present a challenge.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

AWS Big Data

APRIL 5, 2023

“Each of these tools were getting data from a different place, and that’s where it gets difficult,” says Jeroen Minnaert, head of data at Showpad. “If If each tool tells a different story because it has different data, we won’t have alignment within the business on what this data means.”

Dashboards 52

Dashboards Reporting Cost-Benefit Visualization 52

CIO Business Intelligence

MARCH 26, 2025

Give up on using traditional IT for AI The ultimate goal is to have AI-ready data, which means quality and consistent data with the right structures optimized to be effectively used in AI models and to produce the desired outcomes for a given application, says Beatriz Sanz Siz, global AI sector leader at EY.

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Data Quality Forecasting Unstructured Data Data-driven 129

BI-Survey

MARCH 6, 2025

For data management teams, achieving more with fewer resources has become a familiar challenge. While efficiency is a priority, data quality and security remain non-negotiable. Developing and maintaining data transformation pipelines are among the first tasks to be targeted for automation.

Data Warehouse 52

Data Warehouse Metadata Unstructured Data Data-driven 52

AWS Big Data

FEBRUARY 25, 2025

If data mapping has been enabled within the data processing job, then the structured data is prepared based on the given schema. This output is passed to next phase where data transformations and business validations can be applied. After this step, data is loaded to specified target. million records each.

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Data Processing Machine Learning Data-driven Cost-Benefit 53

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Visualization 52

Jet Global

AUGUST 24, 2023

Optimized Resource Allocation: Finance teams can strategically allocate resources in a hybrid ERP environment. This optimization leads to improved efficiency, reduced operational costs, and better resource utilization. Cost Optimization: The hybrid model allows finance teams to balance their expenses effectively.

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