O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models.

Enterprise 349

Enterprise Deep Learning Data Governance Risk 349

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture.

Management 363

Management Machine Learning Experimentation Metrics 363

CIO Business Intelligence

APRIL 9, 2025

Revisiting the foundation: Data trust and governance in enterprise analytics Despite broad adoption of analytics tools, the impact of these platforms remains tied to data quality and governance. Organizations are now moving past early GenAI experimentation toward operationalizing AI at scale for business impact.

Enterprise 52

Enterprise Analytics Experimentation Statistics 52

O'Reilly on Data

OCTOBER 13, 2020

It is entirely possible for an AI product’s output to be absolutely correct from the perspective of accuracy and data quality, but too slow to be even remotely useful. Continuous retraining : a data-driven approach that employs constant monitoring of the model’s key performance indicators and data quality thresholds.

Management 314

Management Machine Learning Metrics Modeling 314

CIO Business Intelligence

APRIL 25, 2024

Fits and starts As most CIOs have experienced, embracing emerging technologies comes with its share of experimentation and setbacks. Data quality Part of the struggle LinkedIn experienced with its job match effort boils down to a data quality issue from both sides: employers and potential employees.

IT 139

IT Cost-Benefit Experimentation Metrics 139

O'Reilly on Data

APRIL 19, 2021

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale.

Enterprise 364

Enterprise Risk Modeling Manufacturing 364

CIO Business Intelligence

SEPTEMBER 30, 2024

Like others, Bell’s data scientists face challenges such as data cleanliness and interoperability, and Mathematica will at times partner with other organizations to overcome those challenges.

Data Quality 116

Data Quality Experimentation Insurance Statistics 116

CIO Business Intelligence

DECEMBER 23, 2024

A new survey of SAP customer organizations shows that, despite AI experimentation, few have implemented AI and generative AI technologies across their enterprises.

Experimentation 59

Experimentation Optimization Digital Transformation Manufacturing 59

AWS Big Data

JANUARY 15, 2025

The data science and AI teams are able to explore and use new data sources as they become available through Amazon DataZone. Because Amazon DataZone integrates the data quality results, by subscribing to the data from Amazon DataZone, the teams can make sure that the data product meets consistent quality standards.

IoT 102

IoT Machine Learning Metadata Data-driven 102

CIO Business Intelligence

AUGUST 15, 2024

Many of those gen AI projects will fail because of poor data quality, inadequate risk controls, unclear business value , or escalating costs , Gartner predicts. CIOs should first launch internal projects with low public-facing exposure , which can mitigate risk and provide a controlled environment for experimentation.

ROI 143

ROI Cost-Benefit Experimentation Deep Learning 143

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Products should be ready-to-consume, easily accessible and responsive to the consumers’ needs.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

Occam's Razor

AUGUST 22, 2011

Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. Data Quality Sucks, Let's Just Get Over It. Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. Got Surveys?

KPI 125

KPI Metrics Measurement ROI 125

CIO Business Intelligence

NOVEMBER 13, 2023

Inadequate data management and governance Data is at the heart of digital transformation, and companies that don’t have adequate data management processes in place are likely to struggle. Ensuring data quality, privacy, and security is essential.

Digital Transformation 143

Digital Transformation Cost-Benefit Experimentation Measurement 143

CIO Business Intelligence

JUNE 14, 2023

Microsoft Certified Power BI Data Analyst Associate The Power BI Data Analyst Associate certification is a measure of a candidate’s proficiency with using Power Query and writing expressions by using Data Analysis Expressions (DAX).

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

CIO Business Intelligence

APRIL 15, 2024

Given the speed required, Lowden established a specialized team for the project to encourage a culture of experimentation and “moving fast to learn fast.” “You Artificial Intelligence, CIO, Data Management, Data Quality, Generative AI, IT Leadership, Microsoft Azure, Vendors and Providers

Testing 102

Testing Machine Learning Data Quality Experimentation 102

Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. Dealing with data quality doubt is every day and, sadly, very complex challenge for many, if not most, of us. How have you avoided the data quality quicksand trap?

Modeling 125

Modeling Metrics Data Quality Data-driven 125

CIO Business Intelligence

DECEMBER 19, 2023

If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation. Third, in the CDO Agenda: 2024: Navigating Data and Generative AI Frontiers , 57% of respondents haven’t changed their data environments to support generative AI.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

CIO Business Intelligence

AUGUST 14, 2024

After the excitement and experimentation of last year, CIOs are more deliberate about how they implement gen AI, making familiar ROI decisions, and often starting with customer support. It’s a cost most organizations have but don’t like paying for, yet they still want to provide a quality experience,” he says.

Enterprise 136

Enterprise Cost-Benefit Experimentation Modeling 136

Octopai

MAY 2, 2022

A successful data analytics team is one that can increase the quantity of data analytics products they develop in a given time while ensuring (and ideally, improving) the level of data quality. Enter DataOps. What is DataOps?

Manufacturing 98

Manufacturing Testing Data Quality Dashboards 98

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. ” There’s either incomplete data, missing tracking data or duplicative tracking data, things like that.

Management 110

Management Machine Learning Experimentation Metrics 110

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

Ontotext

FEBRUARY 14, 2024

If you ask it to generate a response, and maybe it hallucinates, you can then constrain the response it gives you, from the well-curated data in your graph. Data quality Knowledge graphs thrive on clean, well-structured data, and they rely on accurate relationships and meaningful connections. How do you do that?

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 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 95

Data Lake Analytics Snapshot Data Quality 95

Occam's Razor

DECEMBER 12, 2016

Data quality plays a role into this. And, most of the time, regardless of the size of the size of the company, you only know your code is not working post-launch when data is flowing in (not!). You got me, I am ignoring all the data layer and custom stuff! All that is great.

Analytics 142

Analytics Data-driven Strategy Consulting 142

Sanjeev Mohan

FEBRUARY 6, 2019

As you can tell, data governance is a hot topic but an area that many public cloud vendors are weak in. GCP has gained acceptance for development and experimentation and more enterprise customers are putting it into production.

Data Analytics 49

Data Analytics Analytics Data Governance Experimentation 49

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. Easy-to-experiment data development environment. Automated testing to ensure data quality. There are many inefficiencies that riddle a data pipeline and DataOps aims to deal with that. Simplicity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

Smarten

NOVEMBER 3, 2017

These tools should include: Self-Serve Data Preparation – Allows business users to perform Advanced Data Discovery and auto-suggests relationships, reveals the impact and importance of key factors, recommends data type casts, data quality improvements and more!

Analytics 40

Analytics Visualization Experimentation Cost-Benefit 40

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66

IBM Big Data Hub

JANUARY 17, 2023

As part of a data fabric, IBM’s data integration capability creates a roadmap that helps organizations connect data from disparate data sources, build data pipelines, remediate data issues, enrich data quality, and deliver integrated data to multicloud platforms. Data science and MLOps.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

Ontotext

JULY 6, 2023

When powered by a knowledge graph, the solution transforms software tasks into data tasks and enhances the efficiency of data processing and analysis. Experimentation with different technical analysis services becomes possible. It incorporates the knowledge of Subject Matter Experts and ensures accurate sentiment measurements.

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Occam's Razor

NOVEMBER 13, 2009

Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation. Some might argue, rightly so, that the most elusive thing to accomplish is to truly bring data democracy to your organization. You get a jump start. The thing you'll adore: Pages 190 – 192. Sure you've heard of A/B and multivariate testing.

Analytics 71

Analytics Metrics Measurement Testing 71

Alation

DECEMBER 14, 2021

These systems offer numerous web-centric features that bolster customer service and engagement, provide server scalability during periods of fluctuating traffic, and allow easy experimentation with new technologies and promotional strategies. Cloud-native technologies offer: Robust functionality, Seamless interconnectivity, and.

Cost-Benefit 52

Cost-Benefit Data Processing Optimization Consulting 52

CIO Business Intelligence

OCTOBER 18, 2024

CIOs view gen AI as a technology that is here to stay, and they are excited about innovating with it, but it will take time and extensive experimentation to deliver value from AI responsibly. First, CIOs — like so many in the IT industry — lack the experience to know what gen AI can actually do. What’s the plan? What’s the solution?

Experimentation 144

Experimentation Enterprise Technology Finance 144

CIO Business Intelligence

FEBRUARY 5, 2025

By focusing on domains where data quality is sufficient and success metrics are clear such as increased conversion rates, reduced downtime, or improved operational efficiency companies can more easily quantify the value AI brings. Break the project into manageable, experimental phases to learn and adapt quickly.

Cost-Benefit 117

Cost-Benefit ROI Metrics Measurement 117

Ontotext

DECEMBER 6, 2024

Building a RAG prototype is relatively easy, but making it production-ready is hard with organizations routinely getting stuck in experimentation mode. They can handle others, but quality and efficiency are subpar. GraphDB allows experimentation and optimization of the different tasks. Why not vanilla RAG?

Modeling 59

Modeling Cost-Benefit Experimentation Optimization 59