Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Did you just have a spectacular new idea for a data analytics product?

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 266

Machine Learning Data Quality Statistics Modeling 266

Ontotext

JULY 15, 2024

Current electricity data standards EU legislation, such as REMIT , charges the European Network of Transmission System Operators for Electricity (ENTSO-E) with collecting electricity market data from TSOs. TSOs must provide this data in a format from the IEC’s family of standards known as the Common Information Model (CIM).

Contextual Data 52

Contextual Data Metadata Data Quality Publishing 52

CIO Business Intelligence

SEPTEMBER 1, 2023

Sun has a PhD from MIT and continued to publish academic research papers during his time at Microsoft, in addition to teaching at Seattle and Washington universities. In a recent blog post, Sun described how Microsoft researchers conducted experiments to compare the performance of different AI models for use in Dynamics 365.

IT 104

IT Forecasting Reporting Data Quality 104

Smart Data Collective

APRIL 5, 2022

Here at Smart Data Collective, we never cease to be amazed about the advances in data analytics. We have been publishing content on data analytics since 2008, but surprising new discoveries in big data are still made every year. One of the biggest trends shaping the future of data analytics is drone surveying.

Data Collection 108

Data Collection Data Quality Big Data Data-driven 108

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

erwin

JULY 12, 2019

It also helps enterprises put these strategic capabilities into action by: Understanding their business, technology and data architectures and their inter-relationships, aligning them with their goals and defining the people, processes and technologies required to achieve compliance. How erwin Can Help.

Data Governance 110

Data Governance Management Metadata Risk Management 110

DataRobot

AUGUST 18, 2021

Using the right financial datasets and models , fraudulent account practices can be accurately detected and reasonably explained. The dataset used in the following example was published in the Journal of Accounting Research. Fortunately, regulators now have an additional tool to fight accounting fraud— artificial intelligence.

Machine Learning 52

Machine Learning Data Quality Modeling Sales 52

datapine

SEPTEMBER 27, 2022

Some of these ‘structures’ may include putting all the information; for instance, a structure could be about cars, placing them into tables that consist of makes, models, year of manufacture, and color. With a MySQL dashboard builder , for example, you can connect all the data with a few clicks.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Jen Stirrup

SEPTEMBER 30, 2021

They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way. Data Science – Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 341

Management Machine Learning Experimentation Metrics 341

CIO Business Intelligence

JUNE 3, 2024

Yet privacy activists remained opposed to the public services card, arguing that citizens who change their address should be able to decide which government bodies to tell, while the European model, which Ireland signed up to, is a once-only approach. “We It helped make the noise go away about the murky use of data by governments,” he says.

Digital Transformation 109

Digital Transformation Strategy Publishing Risk 109

Alation

MARCH 14, 2023

Data gathering and use pervades almost every business function these days — and it’s widely acknowledged that businesses with a clear strategy around data are best placed to succeed in competitive, challenging markets such as defence.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Ontotext

NOVEMBER 16, 2023

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs.

Metadata 52

Metadata Data-driven Data Quality Data Architecture 52

TDAN

AUGUST 7, 2024

My column today is a follow-up to my article “The Challenge of Data Consistency,” published in the May 2023 issue of this newsletter. In that article, I discussed how semantic encoding (also called concept encoding) is the go-to solution for consistently representing master data entities such as customers and products.

Publishing 52

Publishing Data Quality Data Governance Modeling 52

DataKitchen

MARCH 16, 2023

What are the metrics that matter? Gartner attempted to list every metric under the sun in their recent report , “T oolkit: Delivery Metrics for DataOps, Self-Service Analytics, ModelOps, and MLOps, ” published February 7, 2023. For example, Gartner’s DataOps metrics can be categorized into Velocity, Efficiency, and Quality.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

CIO Business Intelligence

FEBRUARY 14, 2023

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. It’s clear how these real-time data sources generate data streams that need new data and ML models for accurate decisions.

Data Strategy 113

Data Strategy Strategy Machine Learning Data-driven 113

Sisense

OCTOBER 15, 2020

Every data professional knows that ensuring data quality is vital to producing usable query results. Streaming data can be extra challenging in this regard, as it tends to be “dirty,” with new fields that are added without warning and frequent mistakes in the data collection process. Now, let’s connect to Redshift.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Ontotext

JUNE 10, 2021

And yeah, the real-world relationships among the entities represented in the data had to be fudged a bit to fit in the counterintuitive model of tabular data, but, in trade, you get reliability and speed. They create reliable, consistent and communicable models for representing data. Schemas are powerful.

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

Alation

AUGUST 17, 2022

Data governance policy should be owned by the top of the organization so data governance is given appropriate attention — including defining what’s a potential risk and what is poor data quality.” It comes down to the question: What is the value of your data?

Data Governance 92

Data Governance Risk Data-driven Measurement 92

Peter James Thomas

AUGUST 4, 2020

A revised and expanded version of the peterjamesthomas.com Data and Analytics Dictionary has been published. Conformed Data (Conformed Dimension). Data Capability. Data Capability Framework (Data Capability Model). Data Capability Review (Data Capability Assessment). Data Driven.

Data Analytics 98

Data Analytics Analytics Machine Learning Data Governance 98

Ontotext

JANUARY 26, 2023

published as a special topic article in AI magazine, Volume 43, Issue 1 , Spring 2022. The paper introduces KnowWhereGraph (KWG) as a solution to the ever-growing challenge of integrating heterogeneous data and building services on top of already existing open data. web service/API interfaces and communication protocols).

Interactive 52

Interactive Metadata Data Integration Data-driven 52

Octopai

APRIL 19, 2021

He is the President of Knowledge Integrity, Inc and an expert in master data management, data quality, and business intelligence. His articles on TDWI deal with advice for analysts, customer data profiling, master data management technology, and machine learning. . It is published by Robert S.

Metadata 59

Metadata Management Business Intelligence Data Governance 59

AWS Big Data

MARCH 7, 2023

ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.

Analytics 78

Analytics Data Warehouse Data Lake Metadata 78

TDAN

JUNE 21, 2022

Twenty-five years ago today, I published the first issue of The Data Administration Newsletter. It only took a few months to recognize that there was an audience for an “online” publication focused on data administration. […].

Publishing 78

Publishing Data Quality Data Architecture IT 78

DataKitchen

FEBRUARY 21, 2023

If he is to take Gartner’s advice to heart, Marcus will have to add a set of tasks to his team’s daily data engineering tasks. Data is obviously essential to data engineering teams. When Gartner advises your team to put “Customer Value First,” what does that mean? Find opportunities to automate and refactor.

Data-driven 130

Data-driven Testing Risk Cost-Benefit 130

AWS Big Data

JANUARY 31, 2023

Data governance is increasingly top-of-mind for customers as they recognize data as one of their most important assets. Effective data governance enables better decision-making by improving data quality, reducing data management costs, and ensuring secure access to data for stakeholders.

Data Lake 89

Data Lake Data Governance Data Architecture Machine Learning 89

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Data analysts in one organization might be called data scientists or statisticians in another.

Statistics 97

Statistics Metrics Visualization Finance 97

Andrew White

DECEMBER 4, 2019

If I am moved to write research about a vendor, I’ll write it and publish it behind our pay wall, in the assumption the advice is valuable. I heard some interesting stuff: Historically firms had an inside-out view of their market, and this led to organizational silos, data silos etc.

Digital Transformation 64

Digital Transformation Data Quality Data Integration Data Warehouse 64

Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships. This makes LPGs inflexible.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

CIO Business Intelligence

MARCH 29, 2022

Fortunately, learning-based projects typically use data collected for other purposes. . It’s also important to go back to raw data to ask new questions and train new models, particularly as the world is constantly changing. You’ve lost that opportunity if the data has been thrown away. . • You have data but don’t use it.

Cost-Benefit 52

Cost-Benefit Machine Learning Metadata Unstructured Data 52

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Sources of model risk. Model risk management. Image by Ben Lorica.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

AWS Big Data

OCTOBER 31, 2024

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. Data quality issues – Because the data was processed redundantly and shared multiple times, there was no guarantee of or control over the quality of the data.

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57

erwin

MAY 27, 2020

Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. A single source of data truth helps companies begin to leverage data as a strategic asset.

Modeling 98

Modeling Metadata Data Governance Visualization 98

AWS Big Data

MAY 7, 2024

Data governance is a key enabler for teams adopting a data-driven culture and operational model to drive innovation with data. This post explains how you can extend the governance capabilities of Amazon DataZone to data assets hosted in relational databases based on MySQL, PostgreSQL, Oracle or SQL Server engines.

Metadata 84

Metadata Data Lake Data Processing Data-driven 84

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). in next several years.

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109