CIO Business Intelligence

MARCH 21, 2022

Finance: Data on accounts, credit and debit transactions, and similar financial data are vital to a functioning business. But for data scientists in the finance industry, security and compliance, including fraud detection, are also major concerns. Data scientist skills. What does a data scientist do?

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 100

Advertising Recreation/Entertainment Experimentation Enterprise 100

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

IBM Big Data Hub

MARCH 4, 2024

This culture encourages experimentation and expertise growth. For example, by using compliance control scanning of terraform templates to fail provisioning if controls are not met. An AI+ enterprise also recognizes that alongside the necessary tools, fostering a culture that embraces AI and trains talent is crucial.

Enterprise 101

Enterprise ROI Risk Strategy 101

AWS Big Data

JANUARY 9, 2024

It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space to find matches. This functionality was initially released as experimental in OpenSearch Service version 2.4, This is also called embedding the text into the vector space.

Cost-Benefit 96

Cost-Benefit Visualization Modeling Machine Learning 96

AWS Big Data

MAY 2, 2023

If your updates to a dataset triggers multiple subsequent DAGs, then you can use the Airflow metric max_active_tasks_per_dag to control the parallelism of the consumer DAG and reduce the chance of overloading the system. Removal of experimental Smart Sensors. Let’s demonstrate this with a code example. Apache Airflow v2.4.3

Testing 105

Testing Experimentation Management Metadata 105

AWS Big Data

FEBRUARY 12, 2024

Overall architecture and implementation details with Redshift Materialized views Gupshup uses a CDC mechanism to extract data from their source systems and persist it in S3 in order to meet these needs. A series of materialized view refreshes are used to calculate metrics, after which the incremental data from S3 is loaded into Redshift.

Data Warehouse 104

Data Warehouse Analytics Snapshot Cost-Benefit 104

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Media-Mix Modeling/Experimentation. Mobile content consumption, behavior along key metrics (time, bounces etc.) And again, a custom set of metrics.

Metrics 141

Metrics Reporting Analytics Marketing 141

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 300

Testing Machine Learning Consulting Data Quality 300

datapine

FEBRUARY 4, 2020

To get started, you might want to equip yourself with a marketing BI software to analyze all your data and easily build professional reports. Structure your metrics. As with any report you might need to create, structuring and implementing metrics that will tell an interesting and educational data-story is crucial in our digital age.

Reporting 280

Reporting Marketing Advertising Metrics 280

AWS Big Data

JULY 20, 2023

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. Why did Orca choose Apache Iceberg?

Data Lake 89

Data Lake Analytics Snapshot Optimization 89

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. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

JULY 31, 2016

To figure this out, let's consider an appropriate experimental design. In other words, the teacher is our second kind of unit, the unit of experimentation. This type of experimental design is known as a group-randomized or cluster-randomized trial. When analyzing the outcome measure (e.g.,

Experimentation 42

Experimentation Testing Measurement Metrics 42

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. And the goodness doesn’t stop there.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Occam's Razor

FEBRUARY 28, 2017

To ensure customer delight was delivered in a timely manner, it was also decided that Average Call Time (ACT) would now be The success metric. The success metric, ACT, did go down. That ACT was an activity metric was terrible – if you have a The success metric, it should always be an outcome metric. Another issue.

Key Performance Indicator 106

Key Performance Indicator Metrics Measurement Advertising 106

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant. The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. In statistics, such segments are often called “blocks” or “strata”.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. To create the vector index, you must define the vector field name, dimensions, and the distance metric.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Occam's Razor

AUGUST 12, 2013

There are few things more complicated in analytics (all analytics, big data and huge data!) From all my experimentation I've found that taking out the last channel (whichever one it is) causes a material impact on the conversion process, so it gets a "good amount of credit." Then Experimentation.

Modeling 161

Modeling Optimization Marketing Interactive 161

AWS Big Data

NOVEMBER 14, 2023

With a combination of low-latency data streaming and analytics, they are able to understand and personalize the user experience via a seamlessly integrated, self-reliant system for experimentation and automated feedback. Real-time streaming data technologies are essential for digital transformation.

IoT 56

IoT Data-driven Data Lake Data Strategy 56

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. Taking a pulse.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

AWS Big Data

JANUARY 24, 2023

This post explains how to create a design that automatically backs up Amazon Simple Storage Service (Amazon S3), the AWS Glue Data Catalog, and Lake Formation permissions in different Regions and provides backup and restore options for disaster recovery. These mechanisms can be customized for your organization’s processes.

Data Architecture 68

Data Architecture Metadata Data Lake Snapshot 68

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. Lakshmi Randall is Director of Product Marketing at Cloudera, the enterprise data cloud company. Cloudera Manager 6.2 Conclusion and future work.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

DECEMBER 12, 2016

What one critical metric will help you clearly measure performance for each strategy above? How will you know if the performance was a success or failure, what's the target for each critical metric? If you don't have this, ideally signed in blood by your leadership team and you, then you are just messing around with data.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

AUGUST 13, 2012

That means: All of these metrics are off. The central team is responsible for analytics frameworks, centralized contracts (tools, consultants), for aggregated company level analysis, complex project execution (experimentation, media mix models etc) and for setting standards. "Was the data correct?" Hopefully soon!

Modeling 124

Modeling Metrics Data Quality Data-driven 124

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. As you can see from the tiny confidence intervals on the graphs, big data ensured that measurements, even in the finest slices, were precise.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

MAY 8, 2019

If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

MAY 27, 2015

(even if you've never visited the site) has access to tons of intent signals from you right now, tons of third-party cookies that litter your browser right now, and immense Big Data and algorithms. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. Does Yahoo!

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. Second… well there is no second, it is all about the big action and getting a big impact on your bottom-line from your big investment in analytics processes, consulting, people and tools.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

There is no longer always intentionality behind the act of data collection — data are not collected in response to a hypothesis about the world, but for the same reason George Mallory climbed Everest: because it’s there. Make experimentation cheap and understand the cost of bad decisions. And for good reason!

Experimentation 61

Experimentation Testing Statistics Metrics 61

DataRobot

APRIL 29, 2021

Rob O’Neill is Head of Analytics for the University Hospitals of Morecambe Bay, NHS Foundation Trust , where he leads teams focused on business intelligence, data science, and information management. Eric Weber is Head of Experimentation And Metrics for Yelp.

Machine Learning 59

Machine Learning Experimentation Data-driven Data Science 59

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. Marketer, is not spent with data you''ll fail to achieve professional success.]. Many used some data, but they unfortunately used silly data strategies/metrics. It is a really good metric.

Marketing 166

Marketing Metrics Advertising Measurement 166

IBM Big Data Hub

SEPTEMBER 25, 2023

Data Exploration and Innovation: The flexibility of Presto has encouraged data exploration and experimentation at Uber. Data professionals can easily test hypotheses and gain insights from large and diverse datasets, leading to continuous innovation and service improvement.

OLAP 90

OLAP Data Lake Data-driven Snapshot 90