Mastodawn

@jschwa1 Timeliness is generally covered as well, including staleness and freshness detection, etc. It is also possible to branch rules based on data age. With SRP (Single Row Processing), correctness is ensured as close as possible to the point of data creation. By not only validating but also cleansing data, and through our trigger architecture, we can significantly improve quality across all connected systems and ensure a high standard of quality.

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HEDDA.IO 3d ago

@jschwa1 For accuracy, we use the Member/Synonym Search and also Data Links, which allow data to be reconciled against other data sources and even corrected or enriched; with the addition of RDS (Reference Data Services), this can also be done against entirely different services outside the system.

@jschwa1 Generally speaking, you are correct; In HEDDA, a DQ dimension can be assigned to every rule, which can then be used for subsequent analyses. You are also correct that the first four dimensions can be mapped programmatically, but we also have approaches in place for the last two that have the potential to maintain high quality in these areas.

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HEDDA.IO 3d ago

@jschwa1 However, as we do not interact with the data sources themselves, we do not provide data sampling. In that case, for example, in Spark (Databricks, Fabric), when passing the dataframe to HEDDA.IO, you would use `df.sample(0.1)` to load, say, 10% of the data.

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HEDDA.IO 3d ago

@jschwa1 With our Run and Execution concept, we have the ability to perform separate evaluations for specific scenarios, which then place the individual executions within these runs side by side. This means that the “Assess” run can contain only the executions against the sample data and examine how they have changed over time, complete with all the insights we provide for each execution, and with the data snapshots even featuring the data that was current at the time.

HEDDA.IO 4d ago

🧵 Poor data quality rarely announces itself loudly.

Are you safe, or can you spot some warning signs in our guide? 👇

https://hedda.io/when-data-turns-against-you-spotting-and-handling-early-signs-of-data-quality-issues/

#DataQuality #DigitalTransformation #DataStrategy #

HEDDA.IO Mar 6

Another #SQLKonferenz comes to an end and we once agan had a wonderful time speaking to new and old #datamonsters.

Special thanks goes to the organizers and a special shoutout goes to oh22 Group 💞

HEDDA.IO Feb 26

⚔️ The Knight's Quest at #SQLKonferenz2026⚔️
"𝐅𝐫𝐨𝐦 𝐁𝐫𝐨𝐤𝐞𝐧 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐓𝐫𝐮𝐬𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬"

Join @teitelberg and Rafael Dabrowski for a session on transforming fragmented data quality approaches into reliable data products:

Tuesday, March 3rd | 12:35 PM | Room 2, Congress Park Hanau

HEDDA.IO Feb 19

Quick poll for #SQL Konferenz attendees:
What's your #1 data headache right now?
🔸 Bad data
🔸 Integration nightmares
🔸 Compliance stress
🔸 Manual processes
🔸 Other?

We're bringing solutions to discuss at our booth in March – help us focus on what matters most to you!