Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. This calls for additional planning, documentation, and testing.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage.

Marketing 362

Marketing Experimentation Metrics Testing 362

erwin

JANUARY 11, 2019

A company can’t effectively implement data governance – documenting and applying business rules and processes, analyzing the impact of changes and conducting audits – if it fails at data management. The problem usually starts by relying on manual integration methods for data preparation and mapping.

Data Governance 40

Data Governance Risk Metadata Management 40

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

MARCH 7, 2024

Public LLMs in the sandbox: Safely test publicly available Large Language Models (LLMs) in a sandbox environment, separate from the production setting, to assess their impact without risking operational disruptions. Many businesses are in the exploratory phases, creating unique business cases and testing their theory on uses.

Digital Transformation 72

Digital Transformation Machine Learning Risk Testing 72

O'Reilly on Data

OCTOBER 22, 2020

It’s also a good indirect measure of training data quality: a team that does not know where their data originated is likely to not know other important details about the data as well. ML models directly involved in critical safety systems are generally easy to identify as areas of concern.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

Alation

JULY 15, 2022

I recently led an online session, Data Monetisation and Governance , looking at the evolution of data governance , defining data ethics (from the Turing Institute ), and touching on the balancing act between using data to monetise (by increasing revenue, decreasing spend, or mitigating risk) and meeting ethical obligations.

Cost-Benefit 89

Cost-Benefit Data Governance ROI Risk 89

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Data Quality Metadata Data Governance 52

Ontotext

MAY 2, 2024

Data products – optimization of data assets that drive business, improving data quality and interoperability. The semantic layer approach allowed the Pharma company to flip their applications and business units upside down and see what data-driven insights they could get.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

erwin

APRIL 15, 2020

If we reflect on the coronavirus example, we can ask: How do individual countries report their data? Do individual countries use the same testing protocols? Are infections universally defined the same way (based on widely administered tests or only hospital admissions)? Data Governance Methods for Data Distancing.

Data Governance 134

Data Governance Metadata Testing Reporting 134

DataRobot

JUNE 1, 2021

In an earlier post, I shared the four foundations of trusted performance in AI : data quality, accuracy, robustness and stability, and speed. You should first identify potential compliance risks, with each additional step again tested against risks. Protecting Sensitive Data.

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against. Operations.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

Ontotext

JUNE 22, 2023

The long wait comes from the need for extensive testing in order to ensure that a drug is safe and efficient before it can be available to those who need it. This will minimize the risk of investing resources in fundamentally wrong assumptions and will give researchers more confidence to choose top targets for validation.

Metrics 52

Metrics Statistics Visualization Metadata 52

O'Reilly on Data

NOVEMBER 28, 2023

Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing. And there are tools for archiving and indexing prompts for reuse, vector databases for retrieving documents that an AI can use to answer a question, and much more. Only 4% pointed to lower head counts.

Enterprise 271

Enterprise Modeling Testing Reporting 271

Cloudera

AUGUST 31, 2021

Smart DwH Mover helps in accelerating data warehouse migration. Smart Data Validator helps in extensive data reconciliation and testing. Here is the flow of events during migration by leveraging tools from Smart Data Transition Toolkit. This document is produced by consultants at Accenture as general guidance.

Data Warehouse 80

Data Warehouse Cost-Benefit Metadata Data-driven 80

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

datapine

APRIL 15, 2020

Your Chance: Want to test an agile business intelligence solution? Working software over comprehensive documentation. Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. Test BI in a small group and deploy the software internally.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. Define the exact calculation for the target variable or create a couple options to test. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 78

Insurance Risk Data-driven Modeling 78

erwin

FEBRUARY 19, 2021

A strong data management strategy and supporting technology enables the data quality the business requires, including data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage). Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

CIO Business Intelligence

MARCH 14, 2024

AI has already found its way into many government departments, with the most common use of AI identified by the NAO being to support operational decision-making through automated document analysis, digital assistant functionality, and image recognition. Safety standards There is an element of caution to government departments’ approach.

Testing 108

Testing Risk Digital Transformation Statistics 108

Domino Data Lab

JULY 21, 2019

how modern approaches can be used to obtain better quality data, and at a lower cost, to help support evidence-based policy decisions. unique challenges of sharing confidential government microdata and the importance of access in generating high quality inference. What am I talking about with the new types of data?

Metadata 65

Metadata Statistics Risk Machine Learning 65

CIO Business Intelligence

AUGUST 30, 2022

When a large organization depends on a highly customized ERP system, any change invites a host of potential perils from go-live failures to endless testing cycles. The process is risky enough when upgrading a single legacy ERP – with two , the risks more than double. The Strategy – ESOAR lets Sony roar.

Recreation/Entertainment 85

Recreation/Entertainment IT Finance Strategy 85

CIO Business Intelligence

SEPTEMBER 2, 2022

When a large organization depends on a highly customized ERP system, any change invites a host of potential perils from go-live failures to endless testing cycles. The process is risky enough when upgrading a single legacy ERP – with two , the risks more than double. The Strategy – ESOAR lets Sony roar.

Recreation/Entertainment 83

Recreation/Entertainment IT Finance Strategy 83

DataRobot Blog

DECEMBER 20, 2022

The use of artificial intelligence (AI) in the investment sector is proving to be a significant disruptor, catalyzing the connection between the different players and delivering a more vivid picture of the future risk and opportunities across all different market segments. You can understand the data and model’s behavior at any time.

Management 59

Management Machine Learning Optimization Modeling 59

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. and this book will give you an insight into their data collecting procedures and the reasons behind them. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

APRIL 15, 2024

Proceeding with caution While H&R Block’s leadership and board were enticed by the possibilities of gen AI, Lowden notes he had to address some concerns before they fully bought into the project, especially with regard to safety and data privacy. “No No one wants to implement AI that has a high risk of bias,” he says. “We

Testing 80

Testing Experimentation Data Quality Machine Learning 80

DataKitchen

AUGUST 8, 2023

On 20 July 2023, Gartner released the article “ Innovation Insight: Data Observability Enables Proactive Data Quality ” by Melody Chien. It alerts data and analytics leaders to issues with their data before they multiply. It alerts data and analytics leaders to issues with their data before they multiply.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

erwin

DECEMBER 13, 2018

As organizations become data-driven and awash in an overwhelming amount of data from multiple data sources (AI, IOT, ML, etc.), organizations will need to get a better handle on data quality and focus on data management processes and practices.

Data Governance 91

Data Governance IoT Internet of Things Data-driven 91

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MARCH 24, 2020

The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

AWS Big Data

AUGUST 14, 2023

At the same time, unstructured approaches to data mesh management that don’t have a vision for what types of products should exist and how to ensure they are developed are at high risk of creating the same effect through simple neglect. When do new data products get created, and who is allowed to create them?

Management 75

Management Risk Measurement Optimization 75

Jet Global

SEPTEMBER 1, 2023

But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 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 Sales 52