Databand.ai

AUGUST 30, 2023

Accurate data ensures that these decisions and strategies are based on a solid foundation, minimizing the risk of negative consequences resulting from poor data quality. There are various ways to ensure data accuracy.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. It fosters trust in the data product and safeguards sensitive information.

Data Governance 69

Data Governance High Quality Data Government Data 69

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. It fosters trust in the data product and safeguards sensitive information.

Data Governance 52

Data Governance High Quality Data Government Data 52

The Modern Data Company

JULY 18, 2023

Data Quality and Reliability Ensuring data quality is crucial for any data product. High-quality data, free from errors, inconsistencies, or biases, forms the foundation for accurate analysis and reliable insights. It fosters trust in the data product and safeguards sensitive information.

Data Governance 52

Data Governance High Quality Data Government Data 52

Databand.ai

JULY 11, 2023

There are several reasons why organizations need a data quality platform to ensure the accuracy and reliability of their data. With a data quality platform in place, decision-makers can trust the data they use, reducing the risk of costly mistakes and missed opportunities.

Data Cleanse 52

Data Cleanse Telecommunication High Quality Data BI 52

Databand.ai

JUNE 14, 2023

It enables: Enhanced decision-making: Accurate and reliable data allows businesses to make well-informed decisions, leading to increased revenue and improved operational efficiency. Risk mitigation: Data errors can result in expensive mistakes or even legal issues.

Amazon Web Services 52

Amazon Web Services Datasets High Quality Data ETL Tools 52

Monte Carlo

JULY 11, 2023

Examples of inaccurate data Inaccurate data can arise in a few different ways, all primarily due to manual errors, system hiccups, code changes, or data pipeline breakages. Below, we list a few examples: Data entry errors: Mistakes made during the process of entering data into a system can lead to inaccuracies.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Databand.ai

AUGUST 30, 2023

Data cleansing: Implement corrective measures to address identified issues and improve dataset accuracy levels. Data validation: Ensure new database entries adhere to predefined rules or standards to maintain dataset consistency. Additionally, high-quality data reduces costly errors stemming from inaccurate information.

Building 52

Building Data Cleanse Data Governance Datasets 52

Precisely

MARCH 14, 2023

But early adopters realized that the expertise and hardware needed to manage these systems properly were complex and expensive. Enjoy a much more sophisticated computing environment than what’s available in a standard relational database – with data architectures based on tools like Databricks and Snowflake. Bigger, better results.

Data Integration 52

Data Integration High Quality Data BI Data 52

DataKitchen

JUNE 19, 2023

Finding problems before your customers know they exist helps your team’s happiness, productivity, customer trust, and customer data success. Given the complicated distributed systems we use to get value from data and the diversity of data, we need a simplifying framework. That idea is the Data Journey.

Data 52

Data Data Validation Utilities High Quality Data 52

Monte Carlo

JANUARY 25, 2023

It can be challenging when a team is expected to take full responsibility for a key data product when there are no guarantees around the upstream data quality. Without clear management of each transformation step stretching back to source systems, teams may be unwilling to bear the responsibility of contracts.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Databand.ai

AUGUST 30, 2023

It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making. Monitoring can ensure that data quality issues are detected early, before they can impact an organization’s business operations and customers.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Picnic Engineering

DECEMBER 12, 2023

The rich context provided by our Snowflake-powered Data Warehouse enhances their performance, allowing us to create a robust feature set for training. Training these models on our historical demand and assessing their performance is manageable as a one-time project since concepts like data drift are still not a big concern.

Machine Learning 52

Machine Learning Deep Learning Food High Quality Data 52

DataKitchen

SEPTEMBER 21, 2023

This proactive approach to data quality guarantees that downstream analytics and business decisions are based on reliable, high-quality data, thereby mitigating the risks associated with poor data quality. There are multiple locations where problems can happen in a data and analytic system.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Airbnb Tech

NOVEMBER 28, 2023

Enable full visibility into the quality of our offline data warehouse and individual data assets. Composing the Score Before diving into the nuances of measuring data quality, we drove alignment on the vision by defining our DQ Score guiding principles. whereas others were more like proxies for quality (e.g.,

Data Warehouse 98

Data Warehouse Metadata Data Certification 98

U-Next

SEPTEMBER 7, 2022

Access to employee data and information is essential for efficient staff management. Employee database software used is also recognized to encourage openness, maintain organization, centralize employee records, and maintain an employee payroll management system. . Regulating how people and apps access and utilize data .

Data Management 52

Data Management Management Electronics Unstructured Data 52

Ascend.io

JUNE 8, 2023

Data ingestion spans all these sources regardless of origin, and constitutes the initial step of every data pipeline as a critical and integral part of the data product lifecycle. Without clever ingestion functionality, the risk of introducing bad data into the pipelines is high, compromising the entire system.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Airbnb Tech

NOVEMBER 24, 2020

The comprehensive Midas quality guarantee, coupled with clear identification of certified data across Airbnb’s internal tools, became our big bet to guarantee access to high quality data across the company. Figure 4: An overview of the nine steps in the Midas Certification process.

Data Warehouse 52

Data Warehouse Certification Data Pipeline Data 52