CIO Business Intelligence

NOVEMBER 4, 2024

Over the next one to three years, 84% of businesses plan to increase investments in their data science and engineering teams, with a focus on generative AI, prompt engineering (45%), and data science/data analytics (44%), identified as the top areas requiring more AI expertise.

Marketing 128

Marketing Data Quality Data Governance Digital Transformation 128

CIO Business Intelligence

MARCH 24, 2023

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

Data Governance 142

Data Governance Management Metadata Data Quality 142

AWS Big Data

DECEMBER 19, 2024

Initially, the data inventories of different services were siloed within isolated environments, making data discovery and sharing across services manual and time-consuming for all teams involved. Implementing robust data governance is challenging. Oghosa Omorisiagbon is a Senior Data Engineer at HEMA.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

O'Reilly on Data

JANUARY 29, 2019

In other words, could we see a roadmap for transitioning from legacy cases (perhaps some business intelligence) toward data science practices, and from there into the tooling required for more substantial AI adoption? Data scientists and data engineers are in demand.

Deep Learning 271

Deep Learning Machine Learning Data Science Metadata 271

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs.

Testing 304

Testing Machine Learning Consulting Data Science 304

David Menninger's Analyst Perspectives

JANUARY 14, 2021

But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. In our ongoing benchmark research project , we are researching the ways in which organizations work with big data and the challenges they face.

Machine Learning 232

Machine Learning Data Governance Big Data Data Science 232

DataKitchen

DECEMBER 7, 2021

Disrupting Data Governance: A Call to Action, by Laura B. If your data nerd is all about bucking the status quo, Disrupting Data Governance is the book for them. ???. The old adage “if ain’t broke don’t fix it” doesn’t apply to data governance. Author Laura B.

Data-driven 246

Data-driven Data Governance Big Data Data Science 246

CIO Business Intelligence

MARCH 25, 2025

Data debt that undermines decision-making In Digital Trailblazer , I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

Dataiku

MARCH 18, 2022

In our recent Product Days session, AI / Governance: A Two-Way Street , our host François Sergot, Product Manager at Dataiku, had the opportunity to meet with Aaron Kalb, Co-Founder and CDAO at Alation to discuss a hot topic in the data science community — AI and data governance.

Data Governance 105

Data Governance Data Processing Data Science Management 105

Rocket-Powered Data Science

MARCH 20, 2023

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere is not just for data managers.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Machine Learning 325

O'Reilly on Data

MAY 21, 2019

You also need solutions that let you understand what data you have and who can access it. About a third of the respondents in the survey indicated they are interested in data governance systems and data catalogs. 58% of survey respondents indicated they are building or evaluating data science platforms.

Machine Learning 279

Machine Learning Technology Deep Learning Data Science 279

Jen Stirrup

NOVEMBER 15, 2022

How can systems thinking and data science solve digital transformation problems? Understandably, organizations focus on the data and the technology since data retrieval is often viewed as a data problem. However, the thrust here is not to diminish data science or data engineering.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

KDnuggets

AUGUST 30, 2022

Let’s dive in and understand the ins and outs of data observability and data governance - the two keys to a more robust data foundation.

Data Governance 108

Data Governance Data Science 108

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 349

Enterprise Deep Learning Data Governance Risk 349

O'Reilly on Data

JANUARY 8, 2019

Beyond investments in narrowing the skills gap, companies are beginning to put processes in place for their data science projects, for example creating analytics centers of excellence that centralize capabilities and share best practices. Automation in data science and data. Burgeoning IoT technologies.

Machine Learning 272

Machine Learning IoT Internet of Things Data Science 272

O'Reilly on Data

FEBRUARY 4, 2019

Our survey showed that companies are beginning to build some of the foundational pieces needed to sustain ML and AI within their organizations: Solutions, including those for data governance, data lineage management, data integration and ETL, need to integrate with existing big data technologies used within companies.

Machine Learning 279

Machine Learning Enterprise IoT Big Data 279

O'Reilly on Data

FEBRUARY 11, 2019

Data lineage, data catalog, and data governance solutions can increase usage of data systems by enhancing trustworthiness of data. Moving forward, tracking data provenance is going to be important for security, compliance, and for auditing and debugging ML systems. Data Platforms.

Big Data 272

Big Data Technology Machine Learning Deep Learning 272

Domino Data Lab

MAY 31, 2022

What is Data Governance? Data governance refers to the process of managing enterprise data with the aim of making data more accessible, reliable, usable, secure, and compliant across an organization.

Data Governance 98

Data Governance Data Quality Enterprise Management 98

Data Virtualization

JUNE 16, 2022

Reading Time: 6 minutes Data Governance as a concept and practice has been around for as long as data management has been around. It, however is gaining prominence and interest in recent years due to the increasing volume of data that needs to be.

Data Governance 98

Data Governance Data Integration Management Data Lake 98

Analytics Vidhya

NOVEMBER 21, 2022

This article was published as a part of the Data Science Blogathon. Introduction Currently, most businesses and big-scale companies are generating and storing a large amount of data in their data storage. Many companies are there which are completely data-driven.

Data-driven 343

Data-driven Data Science Publishing Analytics 343

O'Reilly on Data

APRIL 2, 2019

A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. Related resources : “What are machine learning engineers?” : a new role focused on creating data products and making data science work in production.

Machine Learning 271

Machine Learning Modeling Data Science Software 271

CIO Business Intelligence

JANUARY 21, 2025

The World Economic Forum shares some risks with AI agents , including improving transparency, establishing ethical guidelines, prioritizing data governance, improving security, and increasing education. Placing an AI bet on marketing is often a force multiplier as it can drive data governance and security investments.

Cost-Benefit 142

Cost-Benefit Data-driven Strategy Marketing 142

TDAN

DECEMBER 1, 2021

Migrating data to the public cloud offers a wide range of benefits for enterprises; data teams can more easily access their data, write, and test data science models, evaluate new data platforms and test applications, run POCs, and deploy in production.

Data Governance 83

Data Governance Testing Data Science Enterprise 83

O'Reilly on Data

FEBRUARY 11, 2020

They have too many different data sources and too much inconsistent data. They don’t have the resources they need to clean up data quality problems. The building blocks of data governance are often lacking within organizations. In other words, the sheer preponderance of data sources isn’t a bug: it’s a feature.

Data Quality 363

Data Quality Metadata Data Governance Publishing 363

Analytics Vidhya

JANUARY 16, 2023

This article was published as a part of the Data Science Blogathon. Introduction Artificial intelligence (AI) is rapidly becoming a fundamental part of our daily lives, from self-driving cars to virtual personal assistants. The use of AI […].

Data Science 319

Data Science Publishing Technology Analytics 319

CIO Business Intelligence

NOVEMBER 27, 2024

Good data governance has always involved dealing with errors and inconsistencies in datasets, as well as indexing and classifying that structured data by removing duplicates, correcting typos, standardizing and validating the format and type of data, and augmenting incomplete information or detecting unusual and impossible variations in the data.

Enterprise 141

Enterprise Data Quality Structured Data Modeling 141

Cloudera

SEPTEMBER 23, 2022

CDP One is an all-in-one data lakehouse Software as a Service (SaaS) offering that enables fast and easy self-service analytics and exploratory data science on any type of data. The post Data Governance and Strategy for the Global Enterprise appeared first on Cloudera Blog.

Data Governance 109

Data Governance Strategy Enterprise Cost-Benefit 109

Cloudera

OCTOBER 1, 2022

According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations. . As a result, growing global compliance and regulations for data are top of mind for enterprises that conduct business worldwide. – From a recent episode of the TWIML AI Podcast.

Data Governance 75

Data Governance Strategy Enterprise Machine Learning 75

Domino Data Lab

JULY 12, 2022

Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method.

Data Science 98

Data Science Recreation/Entertainment Testing Modeling 98

KDnuggets

OCTOBER 15, 2019

The way we control our data isn’t working. Data is as vulnerable as ever. Download this white paper, which outlines lessons about how data science and governance programs can, if implemented properly, reinforce each other’s objective.

Data Governance 74

Data Governance Data Science 74

Cloudera

AUGUST 26, 2021

This means that there is out of the box support for Ozone storage in services like Apache Hive , Apache Impala, Apache Spark, and Apache Nifi, as well as in Private Cloud experiences like Cloudera Machine Learning (CML) and Data Warehousing Experience (DWX). If you want to see how well Ozone works at scale, this is a great read.

Data Science 113

Data Science Forecasting Metadata Machine Learning 113

CIO Business Intelligence

DECEMBER 20, 2024

Effective enterprise data architectures should align with business goals. To do this, organizations should identify the data they need to collect, analyze, and store based on strategic objectives. Ensure data governance and compliance. Choose the right tools and technologies.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

DataKitchen

JANUARY 5, 2021

Execution of this mission requires the contribution of several groups: data center/IT, data engineering, data science, data visualization, and data governance. Each of the roles mentioned above views the world through a preferred set of tools: Data Center/IT – Servers, storage, software.

Testing 147

Testing Data Governance Metadata Visualization 147

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 279

Machine Learning Software Metadata Testing 279

DataKitchen

NOVEMBER 18, 2021

In our survey, data engineers cited the following as causes of burnout: The relentless flow of errors. Restrictive data governance Policies. For see the entire results of the data engineering survey, please visit “ 2021 Data Engineering Survey: Burned-Out Data Engineers are Calling for DataOps.”.

Testing 246

Testing Data Governance Measurement Software 246

David Menninger's Analyst Perspectives

MAY 5, 2021

AutoML can also enable organizations to make data science initiatives more accessible across the organization. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models.

Machine Learning 229

Machine Learning Data Science Modeling Management 229

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” Data Leadership. The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Science Data Strategy 142