Precisely

NOVEMBER 20, 2023

Businesses must navigate many legal and regulatory requirements, including data privacy laws, industry standards, security protocols, and data sovereignty requirements. Therefore, every AI initiative must occur within a sound data governance framework. Clean data reduces the need for data prep.

AI 121

AI AI Clean Data Predictive Analytics 121

Smart Data Collective

DECEMBER 21, 2022

It is a strategic activity that demands an understanding of the data and its sources, including causes of errors and what can be done to minimize the transition of poor data into downstream applications. To Conclude – clean data makes for reliable analytics.

Analytics 130

Analytics Analytics Big Data Big Data 130

Tableau

OCTOBER 8, 2021

Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. This partnership makes data more accessible and trusted. Our customers also need a way to easily clean, organize and distribute this data. Operationalizing Tableau Prep flows to BigQuery.

Tableau 138

Tableau Analytics Analytics Machine Learning 138

IBM Journey to AI blog

AUGUST 15, 2023

A common phrase you’ll hear around AI is that artificial intelligence is only as good as the data foundation that shapes it. Therefore, a well-built AI for business program must also have a good data governance framework. Building and training foundation models Creating foundations models starts with clean data.

Artificial Intelligence 93

Artificial Intelligence Artificial Intelligence AI AI 93

Alation

MAY 26, 2022

They represent the three G’s of data governance. (My Here’s what I mean: Nearly two decades ago, a client asked me why you needed good data. When you launch a governance initiative , prepare to witness the 4G’s of data governance: Grumpiness. That’s right, data just got emotional. Grouchiness.

Data Governance 52

Data Governance Clean Data ML ML 52

Pickl AI

JANUARY 12, 2025

Moreover, regulatory requirements concerning data utilisation, like the EU’s General Data Protection Regulation GDPR, further complicate the situation. Such challenges can be mitigated by durable data governance, continuous training, and high commitment toward ethical standards.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Tableau

OCTOBER 8, 2021

Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. This partnership makes data more accessible and trusted. Our customers also need a way to easily clean, organize and distribute this data. Operationalizing Tableau Prep flows to BigQuery.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

Pickl AI

JUNE 6, 2024

Data scrubbing is the knight in shining armour for BI. Ensuring clean data empowers BI tools to generate accurate reports and insights that drive strategic decision-making. Imagine the difference between a blurry picture and a high-resolution image – that’s the power of clean data in BI.

Clean Data 40

Clean Data Machine Learning Machine Learning Algorithm 40

DagsHub

AUGUST 23, 2024

Data quality is crucial across various domains within an organization. For example, software engineers focus on operational accuracy and efficiency, while data scientists require clean data for training machine learning models. Without high-quality data, even the most advanced models can't deliver value.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

OCTOBER 18, 2023

Data Enrichment Services Enrichment tools augment existing data with additional information, such as demographics, geolocation, or social media profiles. This enhances the depth and usefulness of the data. It defines roles, responsibilities, and processes for data management.

Data Quality 52

Data Quality ML ML Machine Learning 52

Pickl AI

JULY 24, 2024

Clear Formatting Remove any inconsistent formatting that may interfere with data processing, such as extra spaces or incomplete sentences. Validate Data Perform a final quality check to ensure the cleaned data meets the required standards and that the results from data processing appear logical and consistent.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

DagsHub

FEBRUARY 29, 2024

Data preparation involves multiple processes, such as setting up the overall data ecosystem, including a data lake and feature store, data acquisition and procurement as required, data annotation, data cleaning, data feature processing and data governance.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

Pickl AI

SEPTEMBER 12, 2024

Set Data Type Standards: Standardize data types across sources (e.g., This ensures consistency in how data is represented and helps prevent errors during data processing. Remove Duplicates: Identify and eliminate duplicate records to ensure data uniqueness. date formats, numeric formats, text encodings).

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Dataversity

JANUARY 3, 2022

Data cleaning (or data cleansing) is the process of checking your data for correctness, validity, and consistency and fixing it when necessary. No matter what type of data you are handling, its quality is crucial. What are the specifics of data […].

Clean Data 90

Clean Data Data Quality Data Governance Cloud Data 90

Dataversity

MARCH 31, 2021

Click to learn more about author Jett Oristaglio. As AI becomes ubiquitous across dozens of industries, the initial hype of new technology is beginning to be replaced by the challenge of building trustworthy AI systems.

AI 59

AI AI Clean Data Data Quality 59

Alation

JULY 7, 2022

Demand for data stewards and data catalogers is increasing steadily, particularly in entry to mid-level roles, as companies build out robust data governance programs to support data analytics initiatives. College programs with enterprise software prepare graduates to hit the ground running.

Data Scientist 52

Data Scientist Data Analyst Analytics Analytics 52

Heartbeat

MAY 29, 2023

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. Some of the steps that can be taken include: Data Governance: Implementing rigorous data governance policies that ensure fairness, transparency, and accountability in the data used to train LLMs.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

To borrow another example from Andrew Ng, improving the quality of data can have a tremendous impact on model performance. This is to say that clean data can better teach our models. Another benefit of clean, informative data is that we may also be able to achieve equivalent model performance with much less data.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

OCTOBER 23, 2024

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. Implement a Data Provenance System Tracking the origin and transformations of unstructured data helps maintain trust and transparency.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Women in Big Data

JANUARY 25, 2025

Key Points: Data Acquisition: Automated data collection from APIs, IoT devices, and databases. Live quality checks ensure clean data processing. Data enrichment with geodata and external market data. Data Management: AI cleans duplicates and errors while optimizing data integration (ETL processes).

Data Governance 52

Data Governance Big Data Big Data Data Quality 52