DataRobot Blog

APRIL 4, 2022

Media data (usually weekly): media costs, media ratings generated (TVRs, magazine copies, digital impressions, likes, shares, etc.), The standard practice is that the data should be aggregated into a weekly format and span at least the last two to three years (ideally around five years). Classical Modeling Considerations.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

O'Reilly on Data

JANUARY 17, 2023

Think about what the model results tell you: “Maybe a random forest isn’t the best tool to split this data, but XLNet is.” ” If none of your models performed well, that tells you that your dataset–your choice of raw data, feature selection, and feature engineering–is not amenable to machine learning.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

AWS Big Data

NOVEMBER 15, 2023

They can use their own toolsets or rely on provided blueprints to ingest the data from source systems. Once released, consumers use datasets from different providers for analysis, machine learning (ML) workloads, and visualization. The difference lies in when and where data transformation takes place.

Dashboards 98

Dashboards Analytics Metadata Data Warehouse 98

Sisense

AUGUST 6, 2020

AI and machine learning (ML) are not just catchy buzzwords; they’re vital to the future of our planet and your business. Doing it right can mean the difference between thriving in the new world of data and disappearing from it. An obvious mechanical answer is: use relevance as a metric.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

AWS Big Data

JULY 20, 2023

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. This ensures that the data is suitable for training purposes.

Data Lake 89

Data Lake Analytics Snapshot Optimization 89

AWS Big Data

FEBRUARY 21, 2023

We use Apache Spark as our main data processing engine and have over 1,000 Spark applications running over massive amounts of data every day. These Spark applications implement our business logic ranging from data transformation, machine learning (ML) model inference, to operational tasks.

Cost-Benefit 56

Cost-Benefit Informatics Optimization Management 56

Sisense

MAY 11, 2021

Data teams dealing with larger, faster-moving cloud datasets needed more robust tools to perform deeper analyses and set the stage for next-level applications like machine learning and natural language processing. Reporting Reporting contains the flattest and most cleaned version of our data.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Ontotext

DECEMBER 9, 2020

Are you having difficulty joining your knowledge graph APIs with other data sources? Maybe you spend an inordinate amount of time and effort managing operational concerns, deployments, monitoring, metrics and log collation? This leads to lots of small data fetches to/from GraphDB over the network.

Modeling 130

Modeling Management Optimization Metrics 130

DataKitchen

APRIL 26, 2021

Workiva also prioritized improving the data lifecycle of machine learning models, which otherwise can be very time consuming for the team to monitor and deploy. GSK’s DataOps journey paralleled their data transformation journey. Multiple Metrics for Success. At GSK, success is all about adoption.

Measurement 246

Measurement Metrics Data-driven Testing 246

AWS Big Data

NOVEMBER 7, 2023

Second, organizations still need transformations like cleansing, deduplication, and combining datasets for analysis and machine learning (ML). For these, AWS Glue provides fast, scalable data transformation. Updates in Amazon Aurora MySQL are automatically and continuously propagated to Amazon Redshift.

Dashboards 75

Dashboards Data-driven Data Integration Data Lake 75

CIO Business Intelligence

APRIL 30, 2024

This feature provides users the ability to explore metrics with natural language. Tableau Pulse will then send insights for that metric directly to the executive’s preferred communications platform: Slack, email, mobile device, etc. Metrics Bootstrapping. Metric Goals.

Analytics 87

Analytics Metrics Visualization Dashboards 87

AWS Big Data

SEPTEMBER 19, 2023

We also need to be able to add new fields and metrics to the risk profiles as our team identifies new attacks, without needing engineering intervention or complex deployments. The AWS Glue streaming jobs generate derived fields and risk profiles that get stored in Amazon DynamoDB.

Analytics 82

Analytics Risk Big Data Machine Learning 82

Alation

NOVEMBER 22, 2022

An active data governance framework includes: Assigning data stewards. Standardizing data formats. Identifying structured and unstructured data. Setting data management policies, like tagging data. Quantifying effectiveness with metrics. Improve data visibility.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 52

Jet Global

MAY 9, 2024

Data Extraction : The process of gathering data from disparate sources, each of which may have its own schema defining the structure and format of the data and making it available for processing. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. When treating a patient, a doctor may wish to study the patient’s vital metrics in comparison to those of their peer group. Visual Analytics Users are given data from which they can uncover new insights.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52