DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

erwin

MAY 6, 2020

In these times of great uncertainty and massive disruption, is your enterprise data helping you drive better business outcomes? A data-driven approach has never been more valuable to addressing the complex yet foundational questions enterprises must answer. Create always-available and always-transparent data pipelines.

Cost-Benefit 94

Cost-Benefit Data Governance Uncertainty Data-driven 94

CIO Business Intelligence

APRIL 28, 2022

Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission. Particularly in Asia Pacific , revenues for big data and analytics solutions providers hit US$22.6bn in 2020 , with financial services companies ranking among their biggest clients.

Analytics 98

Analytics Insurance Big Data Uncertainty 98

DataKitchen

OCTOBER 2, 2023

One of the most pressing issues is the ownership of databases by multiple data teams, each with its governance protocols, leading to a volatile data environment rife with inconsistencies and errors.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

CIO Business Intelligence

JULY 10, 2024

Then there’s the broader stuff like economic uncertainty, which means really interesting choices about where you invest in technology, and the short- and long-term trade offs, hybrid workplaces, global workplaces, mobility, and how to get new tech like AI, gen AI, IoT, and quantum right and humming.

Strategy 118

Strategy Cost-Benefit Uncertainty Technology 118

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.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Cloudera

OCTOBER 8, 2020

Businesses are now faced with more data, and from more sources, than ever before. But knowing what to do with that data, and how to do it, is another thing entirely. . Poor data quality costs upwards of $3.1 Ninety-five percent of businesses cite the need to manage unstructured data as a real problem.

Digital Transformation 106

Digital Transformation Unstructured Data Experimentation Uncertainty 106

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. That might mean a piece of data is an outlier. Is rain 40% likely?

Uncertainty 52

Uncertainty Machine Learning Advertising Risk 52

IBM Big Data Hub

SEPTEMBER 11, 2023

Government executives face several uncertainties as they embark on their journeys of modernization. and quality (how does this impact service delivery, business process and data quality?). frequency (how many occurrences?), time (how much time is lost?)

Data-driven 85

Data-driven IT Measurement Cost-Benefit 85

Jet Global

MARCH 13, 2020

In a Deloitte survey, 67 percent of the 1,048 executives who participated said they were uncomfortable accessing data using the tools they have available. They prefer to ask an accountant or someone from IT to retrieve data for them. Worse, those consequences are now more probable with data volumes rising rapidly.

Finance 64

Finance Snapshot Reporting Advertising 64

Jet Global

JULY 5, 2023

The Impact of Market Uncertainty This year, Finance decision-makers are feeling pressure from both internal and external sources. With heightened scrutiny on organizations and leaders, organizations can’t afford such a high risk of error. And manual processes increase the likelihood of reporting mistakes.

Finance 52

Finance Uncertainty Reporting Marketing 52

Domino Data Lab

JUNE 23, 2019

If you have a user facing product, the data that you had when you prototype the model may be very different from what you actually have in production. This really rewards companies with an experimental culture where they can take intelligent risks and they’re comfortable with those uncertainties.

Management 110

Management Machine Learning Experimentation Metrics 110

Peter James Thomas

AUGUST 12, 2018

However, often the biggest stumbling block is a human one, getting people to buy in to the idea that the care and attention they pay to data capture will pay dividends later in the process. These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.

Metrics 49

Metrics Statistics Data Quality Measurement 49

CIO Business Intelligence

APRIL 1, 2024

Deloitte 2 meanwhile found that 41% of business and technology leaders said a lack of talent, governance, and risks are barriers to broader GenAI adoption. However such fear, uncertainty, and doubt (FUD) can make it harder for IT to secure the necessary budget and resources to build services. Right-size your model(s).

Uncertainty 142

Uncertainty Data Lake Consulting Risk 142

Domino Data Lab

JULY 22, 2019

What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things. You see these drivers involving risk and cost, but also opportunity. One is data quality, cleaning up data, the lack of labelled data.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Anmut

OCTOBER 29, 2020

As a result, concerns of data governance and data quality were ignored. The direct consequence of bad quality data is misinformed decision making based on inaccurate information; the quality of the solutions is driven by the quality of the data.

Data Governance 52

Data Governance Data Collection Data-driven Statistics 52

CIO Business Intelligence

APRIL 22, 2025

These core leadership capabilities empower executives to navigate uncertainty, lead with empathy and foster resilience in their organizations. Success depends on understanding data needs, measuring ROI, fostering organizational AI fluency and partnering with ethically aligned ecosystems. IQ ensures preparedness; EQ enables agility.

Technology 106

Technology Uncertainty Experimentation Risk Management 106

CIO Business Intelligence

FEBRUARY 25, 2025

Typically, election years bring fear, uncertainty, and doubt, causing a slowdown in hiring, Doyle says. AI adoption, IT outsourcing, and cybersecurity risks are fundamentally reshaping expectations. Cloud security and third-party risk is also a must, with bad actors becoming more sophisticated, Hackley says.

Uncertainty 126

Uncertainty Cost-Benefit Data-driven Marketing 126

CIO Business Intelligence

MAY 14, 2025

AI initiatives often need centralized data lakes, while domain-driven models emphasize decentralized ownership. Ensuring data quality, lineage and compliance becomes more challenging as data is federated across domains but consumed centrally by AI models. On-demand data access. Complex governance.

Enterprise 115

Enterprise Data Architecture Data-driven Data Quality 115

Anmut

APRIL 14, 2025

Industries use established frameworks, like ISO 55000, to align asset management with organisational goals supporting risk-based decisions, continuous improvement, and value realisation from assets. Missing context, ambiguity in business requirements, and a lack of accessibility makes tackling data issues complex.

Uncertainty 52

Uncertainty Data Quality Business Objectives Measurement 52

erwin

MAY 14, 2025

AI initiatives offer great opportunities for innovation, efficiency and increased competitivenessbut only when the underlying data supporting your efforts is trustworthy, well-governed and ready for intelligent use. Without confidence in the data and AI models you deploy, AI presents more risk to your business than help.

Modeling 52

Modeling Data Quality Uncertainty Risk 52

Jet Global

APRIL 18, 2025

Here, we discuss how you can empower your SAP operations teams through times of economic uncertainty. To reduce the risk of delays, seek out a technology solution that empowers operations and finance teams to generate their own reports with the ERP and other organizational data they have.

Finance 52

Finance Reporting Operational Reporting Uncertainty 52