Wayne Yaddow

MARCH 5, 2025

In this post, well see the fundamental procedures, tools, and techniques that data engineers, data scientists, and QA/testing teams use to ensure high-quality data as soon as its deployed. First, we look at how unit and integration tests uncover transformation errors at an early stage.

Testing 52

Testing Data Transformation Statistics Metadata 52

Alation

OCTOBER 26, 2021

Although operations and sales departments tend to champion the use of data for business insight 3 , we’ve found that finance departments are often the first adopters of the Alation Data Catalog within an organization. This is because accurate data is “table stakes” for finance teams. What is most critical to the business?

Finance 52

Finance Data Transformation Enterprise Metrics 52

DataKitchen

JULY 27, 2023

Here are a few examples that we have seen of how this can be done: Batch ETL with Azure Data Factory and Azure Databricks: In this pattern, Azure Data Factory is used to orchestrate and schedule batch ETL processes. Azure Blob Storage serves as the data lake to store raw data. Azure Machine Learning).

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

DataKitchen

OCTOBER 5, 2022

DataOps Observability can help you ensure that your complex data pipelines and processes are accurate and that they deliver as designed. Observability also validates that your data transformations, models, and reports are performing as expected. to monitor your data operations. without replacing staff or systems?to

Testing 173

Testing Risk Data Processing Statistics 173

AWS Big Data

DECEMBER 4, 2024

Globally, financial institutions have been experiencing similar issues, prompting a widespread reassessment of traditional data management approaches. With this approach, each node in ANZ maintains its divisional alignment and adherence to data risk and governance standards and policies to manage local data products and data assets.

Metadata 105

Metadata Data Governance Data Quality Data-driven 105

datapine

SEPTEMBER 29, 2022

Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data. This is also the point where data quality rules should be reviewed again. date, month, and year).

Data Quality 241

Data Quality Metrics Data-driven Management 241

datapine

MAY 2, 2023

However, if you underestimate how many vehicles a particular route or delivery will require, then you run the risk of giving customers a late shipment, which negatively affects your client relationships and brand image. After examining their data, UPS found that trucks turning left were costing them a lot of money.

Big Data 275

Big Data Internet of Things Cost-Benefit Optimization 275

Cloudera

JUNE 1, 2021

In summary, the next chapter for Cloudera will allow us to concentrate our efforts on strategic business opportunities and take thoughtful risks that help accelerate growth. Datacoral powers fast and easy data transformations for any type of data via a robust multi-tenant SaaS architecture that runs in AWS.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

AWS Big Data

SEPTEMBER 19, 2023

Chime’s Risk Analysis team constantly monitors trends in our data to find patterns that indicate fraudulent transactions. However, with a minimum data freshness of 10 minutes, this architecture inherently didn’t align with the near real-time fraud detection use case.

Analytics 109

Analytics Risk Big Data Machine Learning 109

O'Reilly on Data

JANUARY 17, 2023

Instead of invoking the open-source scikit-learn or Keras calls to build models, your team now goes from Pandas data transforms straight to … the API calls for AWS AutoPilot or GCP Vertex AI. That is: what model risk does the company face?) It does not exist in the code. AutoML drives this point home.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

DataKitchen

OCTOBER 5, 2022

DataOps Observability can help you ensure that your complex data pipelines and processes are accurate and that they deliver as designed. Observability also validates that your data transformations, models, and reports are performing as expected. to monitor your data operations. without replacing staff or systems?to

Testing 130

Testing Risk Data Processing Statistics 130

CIO Business Intelligence

FEBRUARY 20, 2024

In this way, manufacturers would be able to reduce risk, increase resilience and agility, boost productivity, and minimise their environmental footprint. The data transformation imperative What Denso and other industry leaders realise is that for IT-OT convergence to be realised, and the benefits of AI unlocked, data transformation is vital.

Data Architecture 98

Data Architecture Unstructured Data Manufacturing IT 98

erwin

APRIL 29, 2020

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. Data lineage offers proof that the data provided is reflected accurately.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

TDAN

JUNE 15, 2021

We have seen an impressive amount of hype and hoopla about “data as an asset” over the past few years. And one of the side effects of the COVID-19 pandemic has been an acceleration of data transformation in organisations of all sizes.

Data Transformation 98

Data Transformation Management Risk Management Risk 98

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

TDAN

MARCH 16, 2021

Taking Stock A year ago, organisations of all sizes around the world were catapulted into a cycle of digital and data transformation that saw many industries achieve in a matter of weeks in what would otherwise have taken many years to achieve. Small businesses pivoted to doing business online in a way that they might […].

Data Transformation 98

Data Transformation Risk Management Risk Data Governance 98

CIO Business Intelligence

AUGUST 3, 2023

But reaching all these goals, as well as using enterprise data for generative AI to streamline the business and develop new services, requires a proper foundation. Each of the acquired companies had multiple data sets with different primary keys, says Hepworth. “We

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

IBM Big Data Hub

JANUARY 9, 2024

Companies still often accept the risk of using internal data when exploring large language models (LLMs) because this contextual data is what enables LLMs to change from general-purpose to domain-specific knowledge. To mitigate risks, it’s important to run as many data integration processes as possible on internal servers.

Enterprise 71

Enterprise Data Integration Data Quality Contextual Data 71

Wayne Yaddow

MARCH 3, 2025

When implementing automated validation, AI-driven regression testing, real-time canary pipelines, synthetic data generation, freshness enforcement, KPI tracking, and CI/CD automation, organizations can shift from reactive data observability to proactive data quality assurance. Summary: Why thisorder?

Data Quality 52

Data Quality Strategy ROI Testing 52

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. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

erwin

SEPTEMBER 17, 2020

Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data. Business terms and data policies should be implemented through standardized and documented business rules.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

DataKitchen

OCTOBER 25, 2022

When considering how organizations handle serious risk, you could look to NASA. The space agency created and still uses “mission control” where many screens share detailed data about all aspects of a space flight. Any data operation, regardless of size, complexity, or degree of risk, can benefit from DataOps Observability.

Testing 130

Testing Data-driven Visualization Dashboards 130

erwin

MARCH 4, 2021

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? It is frequently used for risk analysis.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

Sisense

AUGUST 28, 2020

Data integrity looks at the whole life cycle for your data and considers the processes around how it’s generated, stored, accessed, and applied to accomplish specific business tasks. Throughout that life cycle, a good data integrity program aims to ensure that data is available, complete and accurate.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

CIO Business Intelligence

JUNE 23, 2022

The concept of supply chain visibility and sourcing applies to data supply chains just as well as physical supply chain management. Understanding the sources of data, any transformation activities that take place as well as the “customer lead time” helps organizations identify and mitigate risks. Supply chain complexity.

Data Quality 98

Data Quality Key Performance Indicator Metrics KPI 98

Cloudera

OCTOBER 8, 2024

A major risk is data exposure — AI systems must be designed to align with company ethics and meet strict regulatory standards without compromising functionality. Ensuring that AI systems prevent breaches of client confidentiality, personally identifiable information (PII), and data security is crucial for mitigating these risks.

Optimization 75

Optimization Metrics Experimentation Enterprise 75

David Menninger's Analyst Perspectives

NOVEMBER 7, 2024

This does away with the need for analysts to repeatedly perform data extraction, enrichment or transformation motions from the required source systems, all but eliminating the substantial amount of time analysts and business users spend routinely on data preparation.

Forecasting 130

Forecasting Machine Learning Finance Predictive Analytics 130

Octopai

JUNE 22, 2023

It allows them to explore, manipulate, and analyze data without heavy reliance on IT or data specialists. This approach promotes agility and empowers business users to make faster, data-driven decisions. On the other hand, centralized data management emphasizes a more structured and governed approach.

Management 52

Management Data-driven Data Quality Data Governance 52

AWS Big Data

APRIL 12, 2023

However, SHJs have drawbacks, such as risk of out of memory errors due to its inability to spill to disk, which prevents them from being aggressively used across Spark in place of SMJs by default. About the Authors Melody Yang is a Senior Big Data Solution Architect for Amazon EMR at AWS.

Testing 103

Testing Big Data Metadata Optimization 103

AWS Big Data

OCTOBER 11, 2023

This is due to the complexity of the JSON structure, contracts, and the risk evaluation process on the payor side. Due to this low complexity, the solution uses AWS serverless services to ingest the data, transform it, and make it available for analytics.

Visualization 119

Visualization Dashboards Data-driven Gap analysis 119

AWS Big Data

SEPTEMBER 13, 2024

Over the years, CFM has received many awards for their flagship product Stratus, a multi-strategy investment program that delivers decorrelated returns through a diversified investment approach while seeking a risk profile that is less volatile than traditional market indexes. It was first opened to investors in 1995.

Interactive 107

Interactive Strategy Cost-Benefit Data Governance 107

Octopai

DECEMBER 11, 2022

Banks didn’t accurately assess their credit and operational risk and hold enough capital reserves, leading to the Great Recession of 2008-2009. Let’s take a look at several regulatory standards and explore automated data lineage’s role in smoothing and improving data compliance. Data lineage and financial risk data compliance.

Insurance 52

Insurance Risk Metadata Visualization 52

Octopai

NOVEMBER 23, 2020

In fact, the LIBOR transition program marks one of the largest data transformation obstacles ever seen in financial services. Building an inventory of what will be affected is a huge undertaking across all of the data, reports, and structures that must be accounted for. A New Approach to Enterprise Business Intelligence.

Metadata 72

Metadata Enterprise Business Intelligence Data Governance 72

Sisense

AUGUST 6, 2020

AI can add value to your product/service in many ways, including: Improved business performance Reduced costs Increased customer satisfaction Improved brand value Risk reduction (reduced human error, fraud reduction, spam reduction) Improved convenience and accessibility of products. What are the right KPIs and outputs for your product?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Octopai

JUNE 9, 2024

The inability to trace data lineage accurately made it difficult to demonstrate compliance during audits. This situation posed legal risks and threatened the organization’s reputation. The lack of trust in data created inertia.

IT 52

IT Data-driven Predictive Analytics Data Strategy 52

IBM Big Data Hub

MARCH 14, 2024

For existing IBM on-premises database customers, transitioning to AWS is seamless, offering risk-free, like-for-like upgrades. Integrate seamlessly with watsonx.data SaaS and other IBM and AWS services like IBM data fabric, Amazon S3, Amazon EMR, AWS Glue and more to scale analytics and AI workloads across the enterprise. Existing

Cost-Benefit 75

Cost-Benefit Metadata Optimization Management 75