Today’s organizations work with a lot of data, making it more important than ever for data to be of good quality. Unfortunately, many data problems are not always easy to spot because they remain hidden within large datasets and do not always cause an obvious error. For example, a data cleaning workflow can run with success and the final output can even appear correct, regardless if it has duplicate records, missing values, or some kind of unexpected data.
When errors like these are not caught early, they can spread into reports, dashboards, and affect major business decisions. With that in mind, we’re hosting our next webinar on July 29th at 10 AM EST / 2 PM UTC. During this session, you will learn seven common data quality problems, why they happen, and how to identify, correct, and prevent them using EasyMorph.
What You’ll Learn in 7 Data Quality Scenarios
- Duplicate Records: Catch complete or partial duplicates so they don’t skew reports or inflate totals.
- Incorrect Data Types: Uncover numbers and dates stored incorrectly as text.
- Empty Values and Nulls: Detect empty values that can cause errors in your data and distort calculations.
- Extra Spaces and Characters: Remove extra spaces, line breaks, and special characters.
- Outliers and Anomalous Values: Discover outliers, invalid dates, and unusually long or short text before they compromise your analysis.
- Incorrect Relationships: Identify unexpected relationships between records and understand better how your data fits together.
- Incorrect Formats: Validate email addresses, product codes, URLs, and other formats to make sure your data is in the right format.
Live Q&A – 10 Minutes
We’ll wrap up with live questions from attendees.
Who This Is For
- Data analysts who regularly clean, validate, and transform data.
- Data managers who want to prevent inaccurate information from reaching reports or systems.
- Anyone working with spreadsheets, CSV files, databases, or business applications and wants to catch data quality before it goes downstream
- Automatically correct errors or stop workflows when issues are detected
Why come to this webinar?
Data quality problems can go undetected because validation errors don’t exist for every possible problem and even when the data gets through, workflows don’t fail. For example, a duplicate record can remain hidden until it’s affecting a report downstream.
That’s why you need a way to discover data quality issues before they hit a business platform, dashboard or report. EasyMorph comes built in with visual profiling so you can see the full output of your data before you move forward with your data workflow and you can build validation rules to catch any errors before they travel somewhere else in the organization.
