Top Trending LinkedIn Posts about Data Quality

Data quality reporting requires combining data observability results, pipeline logs, and data contract validation results.

Here is a proposal for a complete

Who owns data quality? When? Why?

In my latest Data Downtime Newsletter, I dive into the mire of data quality ownership to discuss

A data quality platform is a perfect data product for each data team that needs to monitor data quality.

However, centralized environments

We faced a buy-vs-build dilemma for a new data quality platform; the decision-making process turned out to be far more exciting than I

This is why data quality is crucial! So you can avoid "ghosts" messing with your data. 👻😂

Having a reliable data testing tool is

Tracking data quality requires three layers of data quality dashboards: KPIs, actions, and issue details.

If you are considering tracking data quality

There are three types of metadata: business, technical, and operational.

If you maintain all these types of metadata about your tables or

Data Quality Standards define your approach to ensure the same practices across the organization.

If you want to make the most of

Data quality is one of the most essential investments you can make when developing your data infrastructure. If you're data is "real-time" but

Data quality is no joke.

But this video is. Here are 4 tips to handle this situation:

1. Set boundaries early
↳ Tell them

Data quality begins with profiling and cleansing data and must end with long-term data observability.

The process of maintaining good data quality

Data quality is a team sport, not a solo act.

Don't let messy data cripple your organization.

𝘈𝘴 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘨𝘳𝘰𝘸, 𝘴𝘰 𝘥𝘰…

• Data complexities
• Pipeline

Data quality issues can occur at many stages. Let's examine an IoT environment to understand some of the reasons.

IoT devices are

Data quality is not about ownership. It is all about doing your job correctly to avoid data quality issues.

I have seen

Data quality should be promoted using simple words. The terms are complex, so they need a simple description.

Here is my proposal

Data quality activities differ for each stage of building a data pipeline.

We can define five stages: discover, ingest, transform, store, and

Data quality must be automated!

Nobody wants to spend time configuring and tweaking hundreds of thousands of data quality checks.

Here are six areas that

Data quality must be automated!

Nobody wants to spend time configuring and tweaking hundreds of thousands of data quality checks.

Here are six areas that

Data quality is mission-critical.

It’s not just about accurate reports anymore.

It’s about:

→ Empowering AI initiatives
→ Delivering reliable

Data quality guidance and expectations come from the top of the pyramid.

Activities related to data quality are primarily present at the

Data quality guidance and expectations come from the top of the pyramid.

Activities related to data quality are primarily present at the

Data quality is essential for any analysis or business intelligence. Employing best practices lets organizations address issues that become even more critical and

Data Quality should be monitored and validated at many stages.

There are three major areas where we can apply data quality within

Data quality is everyone's responsibility.

Imagine your organization as a massive puzzle.
Each piece represents a bit of data.
Each department is

Data quality is a collaborative activity - everybody has a role to play.

The Chief Data Officers and their leadership teams foresee the data

Data quality issues are not just software bugs. They collect all issues on their path and turn them into a snowball that users

Data quality must be validated at all data lifecycle stages.

The data gets generated somehow. It can be a business application, a SaaS platform,

Data Quality Leaders are often battling to make lasting change. What if you could stop being seen as the ‘data nag’ & instead

Data quality of structured, unstructured, and semi-structured data should detect different data quality issues.

The purpose of data quality is to ensure that data

Data quality testing and data observability shine at different stages. You should test dirty data and observe healthy data.

Data quality testing is a