O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. Machine learning adds uncertainty.

Management 327

Management Machine Learning Experimentation Metrics 327

CIO Business Intelligence

MAY 29, 2024

A search engine using AI would recognize that the differences between the query and the material description are referring to the same product. Not only that, but AI can analyze multiple data fields and apply historical trends to discriminate between materials that have very similar product descriptions.

Sales 58

Sales Modeling Machine Learning Measurement 58

CIO Business Intelligence

FEBRUARY 9, 2024

Like many other professional sports leagues, the NFL has been at the leading edge of data-driven transformation for years. Digital Athlete is a platform that leverages AI and machine learning (ML) to predict from plays and body positions which players are at the highest risk of injury.

Recreation/Entertainment 133

Recreation/Entertainment Data-driven Risk Digital Transformation 133

DataRobot

AUGUST 25, 2021

In a recent blog, we talked about how, at DataRobot , we organize trust in an AI system into three main categories: trust in the performance in your AI/machine learning model , trust in the operations of your AI system, and trust in the ethics of your modelling workflow, both to design the AI system and to integrate it with your business process.

Metrics 98

Metrics Measurement Machine Learning Data Collection 98

Smart Data Collective

OCTOBER 16, 2023

Smart devices use sensors to collect data and upload it to the Internet. Examples include CCTV records, automated vacuum cleaners, weather station data, and other sensor-generated data. All in all, big data refers to massive data collections obtained from various sources.

Big Data 101

Big Data IoT Cost-Benefit Advertising 101

Smart Data Collective

JUNE 6, 2021

Before diving into whether or not Google BigQuery is the future of big data analytics, it’s vital to firstly understand what “big data analytics” actually means. If your company revolves around the manufacturing of goods or services, for example, big data can aid you in the development of your products. References.

Big Data 132

Big Data Data Analytics Analytics Cost-Benefit 132

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Data analytics is a task that resides under the data science umbrella and is done to query, interpret and visualize datasets.

Data Science 80

Data Science Data Analytics Prescriptive Analytics Analytics 80

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Alex Ratner on “Creating large training data sets quickly”.

Machine Learning 255

Machine Learning Data Quality Statistics Modeling 255

Smart Data Collective

APRIL 8, 2020

Data warehouse, also known as a decision support database, refers to a central repository, which holds information derived from one or more data sources, such as transactional systems and relational databases. The data collected in the system may in the form of unstructured, semi-structured, or structured data.

Technology 134

Technology Data Warehouse Big Data Machine Learning 134

IBM Big Data Hub

JANUARY 15, 2024

Traditional AI primarily relies on algorithms and extensive labeled data sets to train models through machine learning. Machine learning-trained generative AI might help in drafting new contracts based on this research.

Machine Learning 65

Machine Learning Digital Transformation Modeling Data Collection 65

IBM Big Data Hub

OCTOBER 16, 2023

By looking critically at these examples, and at successes in overcoming bias, data scientists can begin to build a roadmap for identifying and preventing bias in their machine learning models. In all, debiasing is proving to be among the most daunting obstacles, and certainly the most socially fraught, to date.”

Advertising 72

Advertising Machine Learning Data Architecture Cost-Benefit 72

AWS Big Data

NOVEMBER 15, 2023

For more information on this foundation, refer to A Detailed Overview of the Cost Intelligence Dashboard. Additionally, it manages table definitions in the AWS Glue Data Catalog , containing references to data sources and targets of extract, transform, and load (ETL) jobs in AWS Glue.

Dashboards 97

Dashboards Analytics Metadata Data Warehouse 97

IBM Big Data Hub

FEBRUARY 7, 2024

” Similar to “carbon neutral” in the context of emissions, nature positive refers to stopping, avoiding and reversing environmental destruction. They’re also turning to a new perspective: “nature positive.” The smart factories that make up Industry 4.0

Reporting 93

Reporting Internet of Things Cost-Benefit Data-driven 93

IBM Big Data Hub

MARCH 27, 2024

Some companies attempt to estimate Scope 3 emissions by collecting data from suppliers and manually categorizing data, but progress is hindered by challenges such as large supplier base, depth of supply chains, complex data collection processes and substantial resource requirements.

Experimentation 77

Experimentation Modeling Deep Learning Data-driven 77

Smart Data Collective

MAY 29, 2023

AI translation systems, particularly machine translation (MT), are not immune to this, and we should always confront and overcome this challenge. Understanding Bias in AI Translation Bias in AI translation refers to the distortion or favoritism present in the output results of machine translation systems.

Metrics 68

Metrics Measurement Data Collection Strategy 68

Smart Data Collective

AUGUST 26, 2021

These data-driven predictions also tend to be surprisingly accurate. Simply put, it involves a diverse array of tech innovations, from artificial intelligence and machine learning to the internet of things (IoT) and wireless communication networks. Big Data Analytics & Weather Forecasting: Understanding the Connection.

Forecasting 129

Forecasting Data Analytics Analytics IoT 129

IBM Big Data Hub

APRIL 22, 2024

The US City of Atlanta , for example, uses IBM data collection, machine learning and AI to monitor public transit tunnel ventilation systems and predict potential failures that could put passengers at risk. This will help advance progress by optimizing resources used.

IoT 80

IoT Internet of Things Cost-Benefit Reporting 80

CIO Business Intelligence

JANUARY 20, 2023

BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts, and maps to provide users with detailed intelligence about the state of the business. Whereas BI studies historical data to guide business decision-making, business analytics is about looking forward.

Business Intelligence 143

Business Intelligence Dashboards Data mining OLAP 143

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. Use MLOps for scalability The development of machine learning (ML) models is notoriously error-prone and time-consuming.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

datapine

NOVEMBER 19, 2019

AI refers to the autonomous intelligent behavior of software or machines that have a human-like ability to make decisions and to improve over time by learning from experience. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Cloudera

AUGUST 17, 2020

Next, Hue is used to extract all support cases from Impala where there is a reference to a Knowledge Article in the CDM fields and store it into Parquet files. This is done for easy transport and reference from Spark. Data Collection Request – A sentence requesting data from the customer.

Testing 86

Testing Machine Learning Interactive Metrics 86

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business.

Marketing 361

Marketing Experimentation Metrics Testing 361

datapine

JANUARY 6, 2022

Exclusive Bonus Content: Download Our Free Data Analysis Guide. Explore our free guide with 5 essential tips for your own data analysis. What Is Data Interpretation? Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. Dependable.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Alation

MAY 16, 2023

Data Integration A data pipeline can be used to gather data from various disparate sources in one data store. This makes it easier to compare and contrast information and provides organizations with a unified view of their data. It’s worth noting that a data pipeline may have more than one data source.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Domino Data Lab

AUGUST 12, 2021

Data exploration is a very important step before jumping onto the machine learning wagon. It enables us to build context around the data at hand and lets us develop appropriate models that then can be interpreted correctly. Taking a closer look at the data you will notice that some columns have questions marks ?

Machine Learning 126

Machine Learning Reporting Statistics Visualization 126

CIO Business Intelligence

APRIL 12, 2022

For example, you could integrate dynamic condition data from a logger with QR codes on product labels (which contain pre-set information about the product) and let customer systems process and cross-reference this integrated data using the logger’s API.

IoT 105

IoT Analytics Manufacturing Cost-Benefit 105

IBM Big Data Hub

MARCH 20, 2024

A private cloud refers to a single-tenant cloud infrastructure operated exclusively for one company that is hosted on-premises at a company’s physical location. Big data analytics —the use of advanced analytic techniques against very large, diverse big data sets—has become crucial to business success.

Internet of Things 93

Internet of Things Cost-Benefit Data Processing IoT 93

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. This in turns makes the performance evaluation of the classifier difficult, and can also harm the learning of an algorithm that strives to maximise accuracy. References. A weighted nearest neighbor algorithm for learning with symbolic features.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Jet Global

MAY 9, 2024

Data Migration Pipelines : These pipelines move data from one system to another, often for the purpose of upgrading systems or consolidating data sources. For example, migrating customer data from an on-premises database to a cloud-based CRM system. This leads to better decision-making and improved outcomes.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Cloudera

AUGUST 6, 2021

In reality, organizations live on a continuum, varying in how sophisticated their data is and the extents to which it influences management decisions. . Any article on what it means to be “Data Driven” makes references to Data Strategy, Data Culture and Decision Culture. Data Strategy.

Data-driven 104

Data-driven B2B Data Strategy Strategy 104

IBM Big Data Hub

NOVEMBER 14, 2023

Data products and data mesh Data products are assembled data from sources that can serve a set of functional needs that can be packaged into a consumable unit. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 108

Strategy Cost-Benefit Data-driven Measurement 108

erwin

JUNE 11, 2020

Data Intelligence is the analysis of multifaceted data to be used by companies to improve products and services offered and better support investments and business strategies in place. Data intelligence can encompass both internal and external business data and information. Transforming Industries with Data Intelligence.

Key Performance Indicator 103

Key Performance Indicator KPI Data-driven Machine Learning 103

Alation

OCTOBER 12, 2021

Nowadays, machine learning , AI, and augmented reality analytics are speeding up this process, so that collection and analysis are always on. Folks can work faster, and with more agility, unearthing insights from their data instantly to stay competitive. Assess data risk and craft plans to mitigate that risk.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Strong patterns, if found, will likely generalize to make accurate predictions on future data. Machine learning provides the technical basis for data mining.

Data mining 77

Data mining KDD Data Science Forecasting 77

CIO Business Intelligence

JUNE 7, 2023

Like the NFL, the NBA CTO opted to partner with Microsoft to leverage its Azure cloud platform, which Bhagavathula says contained all the digital components necessary to build the association’s streaming platform, while providing a cloud data lake and machine learning models the NBA could capitalize on for next-generation applications.

Digital Transformation 52

Digital Transformation Recreation/Entertainment Machine Learning Data Lake 52