CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer vs. data architect.

Analytics 119

Analytics Data Science Statistics Unstructured Data 119

FineReport

MARCH 24, 2024

Data Analyst Job Description: Major Tasks and Duties Data analysts collaborate with management to prioritize information needs, collect and interpret business-critical data, and report findings. Each language serves distinct purposes, from performance-oriented applications to web development and data science.

Statistics 52

Statistics Data mining Visualization Reporting 52

Sisense

OCTOBER 29, 2020

“Because AVs collect data in public where there is little ‘reasonable expectation of privacy’, they are not subject to many of the privacy laws in the U.S. The data collected by AVs in the U.S. will likely be owned by the collector of the data, not the data subject. and abroad,” she explained. Advertising?

Machine Learning 90

Machine Learning Data Collection Cost-Benefit Advertising 90

erwin

JUNE 11, 2020

Data intelligence transforms the way industries operate by enabling businesses to hasten the process of analyzing and understanding the derived information with its more understandable models and aggregated trends. How Business Benefits from Data Intelligence. Data quality management.

Key Performance Indicator 103

Key Performance Indicator KPI Data-driven Machine Learning 103

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Data analysts in one organization might be called data scientists or statisticians in another.

Statistics 97

Statistics Metrics Visualization Finance 97

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

FineReport

JULY 18, 2022

Most data analysts are very familiar with Excel because of its simple operation and powerful data collection, storage, and analysis. Key features: Excel has basic features such as data calculation which is suitable for simple data analysis. Python enjoys strong portability. RapidMiner. From RapidMiner. From KNIME.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

The Unofficial Google Data Science Blog

JULY 18, 2023

Measurement challenges Assessing reliability is essentially a process of data collection and analysis. To do this, we collect multiple measurements for each unit of observation, and we determine if these measurements are closely related. Consuming this data, we’ll often care about the mean of multiple labels.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

JUNE 23, 2019

It’s harder for many folks in this role who don’t have any data or machine learning background and are thrown or thrust into shipping something like this. They lack probably what a lot of folks in this room have in terms of domain expertise around data, machine learning, and how these models work.

Management 110

Management Machine Learning Experimentation Metrics 110

Andrew White

JANUARY 9, 2022

Data mesh seems to mean a network of systems and hubs connected to help cope with silos. Perhaps a data mesh is what a pure architecture might design. There are many tactics to employ that might help – including data literacy; storytelling; use of our value pyramid/business value model; use of our decision intelligence model.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Peter James Thomas

AUGUST 29, 2018

My role encompasses being the business driver for the data platform that we are rolling out across the organisation and its success in terms of the data going onto the platform and the curation of that data in a governed state, depending on the consumer requirements. Data democratisation is enabled by the data platform.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Cloudera

OCTOBER 1, 2022

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. . Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use. Infrastructure.

Data Governance 75

Data Governance Strategy Enterprise Machine Learning 75

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 318

Enterprise Deep Learning Data Governance Risk 318

CIO Business Intelligence

NOVEMBER 28, 2023

We use this data to power AI models that help companies better define how employees are collaborating in their organization, what topics come up most frequently in messages, and whether there is equity in recognition awards across the organization.” Quality is job one. Another key to success is to prioritize data quality.

Unstructured Data 120

Unstructured Data Data-driven Visualization Data Quality 120

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

datapine

JANUARY 31, 2022

As Dan Jeavons Data Science Manager at Shell stated: “what we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”. The goals were multiple: revenue growth, customer satisfaction, and speed of service.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 352

Machine Learning Statistics Data Quality Modeling 352

Alation

FEBRUARY 17, 2023

Our organization is struggling with the guardrails between the data governance and management concepts. What frameworks and operating models have you seen work well? Some data seems more analytical, while other is operational (external facing). So what’s the outcome of data governance at the consumption level?

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

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 266

Machine Learning Software Metadata Testing 266

Alation

OCTOBER 12, 2021

Enterprise data analytics enables businesses to answer questions like these. It empowers analysts to model scenarios, forecast change, and predict impact of real or imagined events. Having a data analytics strategy is a key to delivering answers to these questions and enabling data to drive the success of your business.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52