Data Governance Data Pipeline High Quality Data Machine Learning 
DataKitchen
FEBRUARY 18, 2022
It closely follows the best practices of DevOps although the implementation of DataOps to data is nothing like DevOps to code. This paper will focus on providing a prescriptive approach in implementing a data pipeline using a DataOps discipline for data practitioners.
Monte Carlo
JANUARY 10, 2024
On the other hand, “Can the marketing team easily segment the customer data for targeted communications?” usability) would be about extrinsic data quality. In this article, we present six intrinsic data quality techniques that serve as both compass and map in the quest to refine the inner beauty of your data.
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Monte Carlo
JULY 27, 2023
These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders. Strong analytical and technical skills to address sophisticated issues.
Databand.ai
JULY 5, 2023
What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Ensuring Data Quality Data accuracy is a critical factor in the success of any analytics or machine learning initiative.
Precisely
MARCH 14, 2023
And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. Simply design data pipelines, point them to the cloud environment, and execute. What does all this mean for your business? Bigger, better results.
Monte Carlo
JUNE 16, 2023
Data quality has broad applications across industries, but its importance and degree of quality required is also contextual to the use case. For example, in marketing, high-quality data can help businesses better understand their customers, allowing them to create more targeted and effective campaigns.
Monte Carlo
AUGUST 31, 2023
As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.
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