AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Workflow B corresponds to model quality drift checks.

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Precisely

JANUARY 17, 2023

As they do so, access to traditional and modern data sources is required. Poor data quality and information silos tend to emerge as early challenges. Customer data quality, for example, tends to erode very quickly as consumers experience various life changes.

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IBM Journey to AI blog

JANUARY 23, 2023

Simply put, data governance is the process of establishing policies, procedures, and standards for managing data within an organization. It involves defining roles and responsibilities, setting standards for data quality, and ensuring that data is being used in a way that is consistent with the organization’s goals and values.

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Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

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Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Implementing a data catalog first will make MDM more successful.

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Precisely

SEPTEMBER 8, 2023

For data teams, that often leads to a burgeoning inbox of new projects, as business users throughout the organization strive to discover new insights and find new ways of creating value for the business. In the meantime, data quality and overall data integrity suffer from neglect.

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Precisely

SEPTEMBER 5, 2023

An MDM consolidates important domain data into unique or linked instances (e.g. a “golden” record) and then uses that unique record as a reference point for aggregating associated data, purging duplicates, standardizing data across various applications, and creating rules to continuously resolve, merge, or disassociate records.

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Precisely

JUNE 26, 2023

Successful organizations also developed intentional strategies for improving and maintaining data quality at scale using automated tools. Only 46% of respondents rate their data quality as “high” or “very high.” Only 46% of respondents rate their data quality as “high” or “very high.” The biggest surprise?

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Smart Data Collective

APRIL 5, 2022

Drones Surveyors Are Pioneers in the Data Analytics Field. While this is definitely true, there are a few best practices that users need to learn to use it properly. Drone surveyors must also know how to gather and use data properly. They will need to be aware of the potential that data can bring to entities using drones.

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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.

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Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Data quality and quantity:  Machine learning algorithms require high-quality, labeled data to be effective, and their accuracy may be limited by the amount of data available.

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MAY 18, 2023

It strengthens accountability for data assets and enables organizations to unlock analytical insights needed to improve business outcomes and fuel growth. Each organization can tweak this description of data governance based on its unique culture and business drivers, but the general definition cited above applies across the board.

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FEBRUARY 6, 2023

If digital transformation initiatives are to deliver on their promises, they need accurate, consistent, contextualized, and rich data. What Is Data Integrity? Until recently, the business community has lacked a clear and consistent definition of data integrity.

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Precisely

NOVEMBER 10, 2023

To measure data quality – and track the effectiveness of data quality improvement efforts – you need, well, data. Keep reading for a look at the types of data and metrics that organizations can use to measure data Businesses today are increasingly dependent on an ever-growing flood of information.

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Alation

AUGUST 30, 2022

With Alation Anywhere launching in beta, we will meet people where they are, helping to deliver context and trustworthiness of data, from and across the modern data stack, starting with Tableau. With this integration, Alation descriptions and data quality flags of warnings and deprecations will propagate to Tableau.

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Smart Data Collective

SEPTEMBER 28, 2022

Big data management increases the reliability of your data. Big data management has many benefits. One of the most important is that it helps to increase the reliability of your data. Data quality issues can arise from a variety of sources, including: Duplicate records Missing records Incorrect data.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

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Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Data quality and quantity:  Machine learning algorithms require high-quality, labeled data to be effective, and their accuracy may be limited by the amount of data available.

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Alation

FEBRUARY 14, 2023

At first, “governance progress” metrics could be utilised, such as: What % of tables have a data steward assigned? What % of tables have a definition? What % of data is or isn’t assigned to a domain? What % of columns have data quality rules implemented? How much time spent locating data is being saved?

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DataRobot Blog

MAY 10, 2022

With this definition of model risk, how do we ensure the models we build are technically correct? 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. To reference SR 11-7: .

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JANUARY 5, 2023

It answers the question of how the company will oversee the data so it can serve its purposes more effectively. By defining data governance in this way, you run the risk of narrowing its definition to the point where it seems like little more than a mechanical exercise, but nothing could be further from the truth.

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Alation

OCTOBER 5, 2021

For example, it may be helpful to track specific daily activities or benchmarks for all data-related processes. Numerous committees spend hours deliberating over every word in a Glossary definition, then 6 months down the line leaders complain there hasn’t been enough value shown. Roadblock #2: Data problems and inconsistencies.

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Dataversity

SEPTEMBER 10, 2021

A business glossary is a list of data-related terms and definitions, displayed clearly and logically so everyone in an organization can access them. A business glossary is an essential Data Literacy tool and crucial for effective Data Governance. Click to learn more about author Sharad Varshney.

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Towards AI

FEBRUARY 20, 2024

Definition says, machine learning is the ability of computers to learn without explicit programming. Instead of being told how to perform a task, they learn from data and improve their performance over time. It isn't easy to collect a good amount of quality data.

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Pickl AI

DECEMBER 4, 2023

Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence. Think of data governance as the rules and regulations governing the kingdom of information. It ensures data quality , integrity, and compliance.

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Depends on the Definition

JANUARY 20, 2020

In every machine learning project, the training data is the most valuable part of your system. In many real-world machine learning projects the largest gains in performance come from improving training data quality. Training data is often hard to aquire and since the data can be large, quality can be hard to check.

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IBM Data Science in Practice

MARCH 8, 2023

Source: IBM Cloud Pak for Data Feature Catalog Users can manage feature definitions and enrich them with metadata, such as tags, transformation logic, or value descriptions. Source: IBM Cloud Pak for Data MLOps teams often struggle when it comes to integrating into CI/CD pipelines.

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Alation

MARCH 11, 2022

Leaders must also build policies about data into the catalog, from both an internal perspective (“How do we define data quality ?”) to an external, compliance-focused perspective (“How do we ensure analysts use private data legally?”). Again, a data catalog can automate the tedium of compliance at scale.

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MARCH 13, 2023

A Typical Data Governance Story Very often, initial data programs start out with a series of inquiries. The project team reaches out to business users, asking about data definitions, mapping out workflows and approval processes, and determining who is responsible for various datasets throughout the organization.

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IBM Journey to AI blog

JANUARY 8, 2024

 It includes a built-in schema registry to validate event data from applications as expected, improving data quality and reducing errors. This means they can be understood by people, are supported by code generation tools and are consistent with API definitions.

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phData

JUNE 27, 2023

Now that your data is loaded in using dbt, one can see the data displayed in Sigma itself, allowing the user to verify how up-to-date their data is. Data Quality View dbt quality tests on columns and models, providing precision and transparency into your data quality questions and concerns – What a relief.

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Alation

JULY 15, 2022

Data as an Insight: Operational Improvement. The mandate: Financial services businesses must meet extensive regulatory requirements, which demand full governance including: Data ownership, definitions, agreed-upon data quality rules and results, and lineage (BCBS239, CCAR, etc.).

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IBM Journey to AI blog

JANUARY 25, 2023

The right data architecture to link and gain insight across silos requires the communication and coordination of a strategic data governance program. Inconsistent or lacking business terminology, master data, hierarchies Raw data without clear business definitions and rules is ripe for misinterpretation and confusion.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

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Heartbeat

DECEMBER 7, 2023

Data should be designed to be easily accessed, discovered, and consumed by other teams or users without requiring significant support or intervention from the team that created it. Data should be created using standardized data models, definitions, and quality requirements. What is Data Mesh?

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Alation

AUGUST 17, 2022

“This failure is often the difference between successful implementations and data breaches. If your definitions are bad, so is your governance/risk/security. At the same time, data governance leaders must deliver tangible business value. It comes down to the question: What is the value of your data?

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