O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

Cloudera

MARCH 3, 2022

In 2017, Anthem reported a data breach that exposed thousands of its Medicare members. The medical insurance company wasn’t hacked, but its customers’ data was compromised through a third-party vendor’s employee. 86% of Experian survey respondents’, for instance, are prioritizing moving their data to the cloud in 2022.

Data Governance 96

Data Governance Enterprise Data Quality Metadata 96

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

CIO Business Intelligence

NOVEMBER 22, 2022

Data fabric is an architecture that enables the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. The fabric, especially at the active metadata level, is important, Saibene notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

AWS Big Data

MARCH 26, 2024

In 2022, AWS commissioned a study conducted by the American Productivity and Quality Center (APQC) to quantify the Business Value of Customer 360. The following figure shows some of the metrics derived from the study. Then, you transform this data into a concise format. Organizations using C360 achieved 43.9%

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71

Cloudera

MARCH 22, 2023

Cloudera Data Platform (CDP) is no different: it’s a hybrid data platform that meets organizations’ needs to get to grips with complex data anywhere, turning it into actionable insight quickly and easily. There are many logs and metrics, and they are all over the place.

Metrics 85

Metrics Data Governance Cost-Benefit Dashboards 85

Ontotext

MAY 2, 2024

But, although, this helps somewhat in terms of architecture, soon these data lakes become unwieldy. Every new dataset and new user adds a little more friction that hits the core metric of the velocity of data and brings it down to zero. Here we talk about metadata management, catalog of catalogs, and so on.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

CIO Business Intelligence

MARCH 4, 2024

As data volumes continue to increase alongside a correlating number of business requests, modern insurance data leaders face a nuanced set of challenges. Accelerated demand in AI-enabled innovations has recently compounded these issues, prioritizing the need for new capabilities that require even more robust data foundations.

Insurance 92

Insurance Strategy Data Governance Data Integration 92

Andrew White

JULY 5, 2023

One of the points of confusion is with catalogs – or data catalogs – or analytics catalogs or metrics stores. Here I repeat what I wrote in the original blog: Use cases for a data catalog Analytics use cases are quite different to governance use cases. The other 96% of data in the catalog has little meaning to them.

Metrics 52

Metrics Metadata Modeling Data Quality 52

AWS Big Data

MARCH 7, 2023

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

Analytics 74

Analytics Data Warehouse Data Lake Metadata 74

AWS Big Data

NOVEMBER 6, 2023

The following graph describes a simple data quality check pipeline using setup and teardown tasks. Airflow will cache variables and connections locally so that they can be accessed faster during DAG parsing, without having to fetch them from the secrets backend, environments variables, or metadata database.

Metrics 108

Metrics Metadata Snapshot Management 108

Alation

OCTOBER 5, 2021

These divergences of focus can lead to consumers feeling bogged down by overly complicated processes or leadership teams being unable to see initiative investments reap the desired rewards of their predictive business success metrics. (1). Incomplete data. Data governance and AI. Lack of commitment.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

AWS Big Data

FEBRUARY 1, 2023

We also used AWS Lambda for data processing. To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. Clients access this data store with an API’s.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

IBM Big Data Hub

JUNE 8, 2022

However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key. Here are a few common data management challenges: Regulatory compliance on data use.

Data-driven 58

Data-driven Enterprise Data Governance Data Lake 58

erwin

MARCH 8, 2019

A number of best practices and technology solutions were used to establish the data required for managing the registration and classification of data feeds: The underlying metadata is harvested followed by an initial quality check. Then the metadata is classified against a semantic model held in a business glossary.

Data Governance 65

Data Governance Metadata Data Warehouse Data Quality 65

O'Reilly on Data

MARCH 31, 2020

You might have millions of short videos , with user ratings and limited metadata about the creators or content. Job postings have a much shorter relevant lifetime than movies, so content-based features and metadata about the company, skills, and education requirements will be more important in this case.

Management 332

Management Machine Learning Experimentation Metrics 332

Alation

MAY 17, 2022

Those algorithms draw on metadata, or data about the data, that the catalog scrapes from source systems, along with behavioral metadata, which the catalog gathers based on human data usage. Beyond sampling, analysts must take care to validate data in other ways.

Metadata 52

Metadata Data Quality Machine Learning Reporting 52

CIO Business Intelligence

APRIL 29, 2022

Despite soundings on this from leading thinkers such as Andrew Ng , the AI community remains largely oblivious to the important data management capabilities, practices, and – importantly – the tools that ensure the success of AI development and deployment. Further, data management activities don’t end once the AI model has been developed.

Data Governance 145

Data Governance IT Risk Data Lake 145

Andrew White

JANUARY 12, 2021

Predicts 2021: Data and Analytics Strategies to Govern, Scale and Transform Digital Business : By 2024, 30% of organizations will invest in data and analytics governance platforms, thus increasing the business impact of trusted insights and new efficiencies.

Analytics 144

Analytics Metadata Enterprise Data-driven 144

Ontotext

JUNE 22, 2023

On top of that, data is sometimes unreliable , and inaccurate or missing metadata makes it hard to decide which information to trust. The confidence score and provenance metrics enable researchers to trace back any decisions and have more confidence to make evidence-driven decisions. Our customer is a U.S.-based

Metrics 52

Metrics Statistics Visualization Metadata 52

CDW Research Hub

MAY 29, 2019

Identify where your company currently stands in data governance, while keeping in mind your master data management plan, data quality goals, data stewardship, data lineage, and metadata. Start to develop a data governance framework. Then, prioritize where you want to be.

Data Governance 40

Data Governance Data Quality ROI Metadata 40

DataRobot Blog

MAY 10, 2022

The first step would be to make sure that the data used at the beginning of the model development process is thoroughly vetted, so that it is appropriate for the use case at hand. This requirement makes sure that no faulty data variables are being used to design a model, so erroneous results are not outputted. To reference SR 11-7: .

Risk 64

Risk Modeling Machine Learning Data Quality 64

Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

AWS Big Data

AUGUST 14, 2023

Goals of DPPM The goals of DPPM can be summarized as follows: Protect value – DPPM protects the value of the organizational data strategy by developing, implementing, and enforcing frameworks to measure the contribution of data products to organizational goals in objective terms. Monitoring and Event Management X X.

Management 81

Management Risk Measurement Optimization 81

AWS Big Data

NOVEMBER 16, 2023

Redshift provisioned clusters also support query monitoring rules to define metrics-based performance boundaries for workload management queues and the action that should be taken when a query goes beyond those boundaries. A predicate consists of a metric, a comparison condition (=, ), and a value.

Enterprise 88

Enterprise Data Warehouse Snapshot Cost-Benefit 88

Alation

OCTOBER 18, 2022

Alation is pleased to be named a dbt Metrics Partner and to announce the start of a partnership with dbt, which will bring dbt data into the Alation data catalog. In the modern data stack, dbt is a key tool to make data ready for analysis. Data Transformation in the Modern Data Stack.

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

DataKitchen

SEPTEMBER 21, 2023

These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Jet Global

JULY 2, 2019

Ungoverned or uncatalogued data can leave businesses vulnerable both in terms of data quality (and organizational trust in that data), as well as in terms of security, regulatory, and compliance risks.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

DataKitchen

OCTOBER 11, 2021

Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. While implementing a DataOps solution, we make sure that the pipeline has enough automated tests to ensure data quality and reduce the fear of failure. Monitoring Job Metadata.

Testing 152

Testing Metadata Dashboards Statistics 152

Alation

JULY 19, 2022

A data catalog can assist directly with every step, but model development. And even then, information from the data catalog can be transferred to a model connector , allowing data scientists to benefit from curated metadata within those platforms. How Data Catalogs Help Data Scientists Ask Better Questions.

Metadata 59

Metadata Data Quality Statistics Data Science 59

CIO Business Intelligence

DECEMBER 21, 2022

Defined as an enabler of frictionless access of data sharing in a distributed data environment, data fabric aims to help companies access, integrate, and manage their data no matter where that data is stored using semantic knowledge graphs, active metadata management, and embedded machine learning.

IT 131

IT Business Intelligence Sales Key Performance Indicator 131

O'Reilly on Data

JUNE 19, 2019

In a previous post , we noted some key attributes that distinguish a machine learning project: Unlike traditional software where the goal is to meet a functional specification, in ML the goal is to optimize a metric. Quality depends not just on code, but also on data, tuning, regular updates, and retraining.

Modeling 194

Modeling Machine Learning Testing Metrics 194

Alation

JANUARY 25, 2022

As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. In this blog, we will focus on the “integrated layer” part of this definition by examining each of the key layers of a comprehensive data fabric in more detail.

Metadata 52

Metadata IT Metrics Data-driven 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance Insurance Metadata Data-driven 52

Cloudera

JUNE 29, 2023

Use predictive analytics and ML to formalize key intraday liquidity metrics and monitor liquidity positions in real time. Deliver real-time analytic dashboards, suitable for different stakeholders, that integrate data from payment systems, nostro accounts , internal transactions, and other sources. Enhance counterparty risk assessment.

Data Architecture 82

Data Architecture Risk Management Risk Management 82

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

Cost-Benefit 83

Cost-Benefit Metadata Data Governance Modeling 83