Rocket-Powered Data Science

FEBRUARY 15, 2023

Know thy data: understand what it is (formats, types, sampling, who, what, when, where, why), encourage the use of data across the enterprise, and enrich your datasets with searchable (semantic and content-based) metadata (labels, annotations, tags). So, if you have 1 trillion data points (g.,

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

DECEMBER 19, 2018

Anomaly detection is well-known in the financial industry, where it’s frequently used to detect fraudulent transactions, but it can also be used to catch and fix data quality issues automatically. If you suddenly see unexpected patterns in your social data, that may mean adversaries are attempting to poison your data sources.

Machine Learning 279

Machine Learning Software Metadata Testing 279

AWS Big Data

JANUARY 15, 2025

An extract, transform, and load (ETL) process using AWS Glue is triggered once a day to extract the required data and transform it into the required format and quality, following the data product principle of data mesh architectures. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 100

IoT Machine Learning Metadata Data-driven 100

erwin

DECEMBER 9, 2024

Data observability provides the ability to immediately recognize, and be alerted to, the emergence of hallucinations and accept or reject these changes iteratively, thereby training and validating the data. Maybe your AI model monitors sales data, and the data is spiking for one region of the country due to a world event.

Metadata 52

Metadata Data Quality Sales Modeling 52

AWS Big Data

DECEMBER 4, 2024

It’s the preferred choice when customers need more control and customization over the data integration process or require complex transformations. This flexibility makes Glue ETL suitable for scenarios where data must be transformed or enriched before analysis. Check CloudWatch log events for the SEED Load.

Data Integration 97

Data Integration Data Lake Statistics Data-driven 97

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

DataKitchen

OCTOBER 20, 2023

Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap. Such an instance answers the critical question of ‘Dude, Where is my data?’ ’ while maintaining operational efficiency and ensuring data quality—thus preserving customer satisfaction and the team’s credibility.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

erwin

APRIL 29, 2020

An understanding of the data’s origins and history helps answer questions about the origin of data in a Key Performance Indicator (KPI) reports, including: How the report tables and columns are defined in the metadata? Who are the data owners? Data lineage offers proof that the data provided is reflected accurately.

Metadata 111

Metadata Key Performance Indicator Data Governance Data Quality 111

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

Octopai

APRIL 19, 2021

Aptly named, metadata management is the process in which BI and Analytics teams manage metadata, which is the data that describes other data. In other words, data is the context and metadata is the content. Without metadata, BI teams are unable to understand the data’s full story.

Metadata 59

Metadata Management Business Intelligence Data Governance 59

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

DataKitchen

OCTOBER 11, 2021

The DataOps pipeline you have built has enough automated tests to catch errors, and error events are tied to some form of real-time alerts. Based on business rules, additional data quality tests check the dimensional model after the ETL job completes. Monitoring Job Metadata. Adding Tests to Reduce Stress.

Testing 152

Testing Metadata Dashboards Statistics 152

IBM Big Data Hub

NOVEMBER 21, 2023

Apache Kafka is a well-known open-source event store and stream processing platform and has grown to become the de facto standard for data streaming. Apache Kafka transfers data without validating the information in the messages. Kafka does not examine the metadata of your messages. What’s next?

Data Quality 96

Data Quality Metadata Data-driven Optimization 96

Smart Data Collective

OCTOBER 13, 2021

Data Virtualization can include web process automation tools and semantic tools that help easily and reliably extract information from the web, and combine it with corporate information, to produce immediate results. How does Data Virtualization manage data quality requirements? In forecasting future events.

Visualization 145

Visualization Cost-Benefit Big Data Prescriptive Analytics 145

erwin

SEPTEMBER 17, 2020

Here are six benefits of automating end-to-end data lineage: Reduced Errors and Operational Costs. Data quality is crucial to every organization. Automated data capture can significantly reduce errors when compared to manual entry. Automating data capture frees up resources to focus on more strategic and useful tasks.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

AWS Big Data

SEPTEMBER 11, 2024

This also includes building an industry standard integrated data repository as a single source of truth, operational reporting through real time metrics, data quality monitoring, 24/7 helpdesk, and revenue forecasting through financial projections and supply availability projections.

Data Architecture 96

Data Architecture Optimization Data Warehouse Metadata 96

AWS Big Data

MAY 7, 2024

Figure 1: Flow of actions for self-service analytics around data assets stored in relational databases First, the data producer needs to capture and catalog the technical metadata of the data asset. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 86

Metadata Data Lake Data Processing Data-driven 86

erwin

APRIL 11, 2019

The Regulatory Rationale for Integrating Data Management & Data Governance. This is also true for existing data regulations. Compliance is an on-going requirement, so efforts to become compliant should not be treated as static events. In fact, such an understanding is arguably better put to use proactively.

Insurance 100

Insurance Data-driven Data Governance Metadata 100

AWS Big Data

MARCH 7, 2023

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. Unlike ingestion processes, data can be transformed as per business rules before loading.

Analytics 78

Analytics Data Warehouse Data Lake Metadata 78

Ontotext

JULY 29, 2021

KGs bring the Semantic Web paradigm to the enterprises, by introducing semantic metadata to drive data management and content management to new levels of efficiency and breaking silos to let them synergize with various forms of knowledge management. The RDF data model and the other standards in W3C’s Semantic Web stack (e.g.,

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Cloudera

JULY 20, 2021

Invest in maturing and improving your enterprise business metrics and metadata repositories, a multitiered data architecture, continuously improving data quality, and managing data acquisitions. enhanced customer experiences by accelerating the use of data across the organization.

Manufacturing 106

Manufacturing Forecasting IoT Insurance 106

Ontotext

SEPTEMBER 15, 2020

Today’s organizations are dealing with data of unprecedented diversity in terms of type, location and use at equally unprecedented volumes and no one is proposing that it is ever going to simplify. This multiplicity of data leads to the growth silos, which in turns increases the cost of integration.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

AWS Big Data

NOVEMBER 6, 2023

The trigger runs in a parent process called a triggerer , a service that runs an asyncio event loop. The following graph describes a simple data quality check pipeline using setup and teardown tasks. The triggerer has the capability to run triggers in parallel at scale, and to signal tasks to resume when a condition is met.

Metrics 116

Metrics Metadata Snapshot Management 116

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”. Experience the power of Business Intelligence with our 14-days free trial!

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Octopai

JANUARY 31, 2021

The particular episode we recommend looks at how WeWork struggled with understanding their data lineage so they created a metadata repository to increase visibility. Agile Data. Another podcast we think is worth a listen is Agile Data. Currently, he is in charge of the Technical Operations team at MIT Open Learning.

Data Governance 83

Data Governance Data Processing Data Quality Metadata 83

Cloudera

APRIL 1, 2024

We scored the highest in hybrid, intercloud, and multi-cloud capabilities because we are the only vendor in the market with a true hybrid data platform that can run on any cloud including private cloud to deliver a seamless, unified experience for all data, wherever it lies.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. Then, you transform this data into a concise format.

Data Strategy 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

Ontotext

APRIL 10, 2024

Bad data tax is rampant in most organizations. Currently, every organization is blindly chasing the GenAI race, often forgetting that data quality and semantics is one of the fundamentals to achieving AI success. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Smart DwH Mover helps in accelerating data warehouse migration.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

Alation

AUGUST 26, 2021

What Is Data Intelligence? Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.” Yet finding data is just the beginning.

Metadata 64

Metadata Data Governance Dashboards Software 64

Cloudera

JUNE 29, 2023

Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. CDP also enables data and platform architects, data stewards, and other experts to manage and control data from a single location.

Data Architecture 85

Data Architecture Risk Management Risk Management 85

Alation

MAY 19, 2022

Alation attended last week’s Gartner Data and Analytics Summit in London from May 9 – 11, 2022. Coming off the heels of Data Innovation Summit in Stockholm, it’s clear that in-person events are back with a vengeance, and we’re thrilled about it. Establish what data you have. Leverage small data.

Metadata 52

Metadata Data Analytics Analytics Data Governance 52

Ontotext

DECEMBER 1, 2023

The event held the space for presentations, discussions, and one-on-one meetings, where more than 20 partners, 1064 Registrants from 41 countries, spanning across 25 industries came together. According to him, “failing to ensure data quality in capturing and structuring knowledge, turns any knowledge graph into a piece of abstract art”.

Metadata 78

Metadata Sales Machine Learning Consulting 78

Ontotext

JULY 15, 2024

The generation, transmission, distribution and sale of electrical power generates a lot of data needed across a variety of roles to address reporting requirements, changing regulations, advancing technology, rapid responses to extreme weather events and more.

Contextual Data 52

Contextual Data Metadata Data Quality Publishing 52

Alation

MAY 31, 2023

If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. This year’s DGIQ West will host tutorials, workshops, seminars, general conference sessions, and case studies for global data leaders.

Data Governance 52

Data Governance Insurance Metadata Data-driven 52

Timo Elliott

JANUARY 5, 2022

The first one is: companies should invest more in improving their data quality before doing anything else. You must master your metadata and make sure that everything is lined up. To make a big step forward with data science, you first need to do that painful work. That’s an awful waste of resources.

Digital Transformation 115

Digital Transformation Machine Learning Enterprise Data Science 115

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. The data is partitioned on InputDataSetName, Year, Month, and Date.

Optimization 52

Optimization Forecasting Data Lake Metadata 52