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 139

Digital Transformation Cost-Benefit Experimentation Measurement 139

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 307

Enterprise Deep Learning Data Governance Risk 307

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

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 124

Big Data Data Analytics Analytics Predictive Modeling 124

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 140

Digital Transformation Unstructured Data Machine Learning Risk Management 140

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 327

Management Machine Learning Experimentation Metrics 327

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 87

Manufacturing Testing Experimentation Data Quality 87

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 90

Data Lake Analytics Snapshot Optimization 90

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

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

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 Prescriptive Analytics 56

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 240

Management Metrics Machine Learning Modeling 240

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 353

Enterprise Modeling Risk Manufacturing 353

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 104

Digital Transformation Experimentation Unstructured Data Uncertainty 104

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

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

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

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

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 124

KPI Metrics Measurement ROI 124

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 Data-driven Machine Learning 66

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 124

Modeling Metrics Data Quality Data-driven 124

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

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 141

Analytics Data-driven Strategy Consulting 141

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