Data Analysis / Validation Flashcards
● How do you validate your data to ensure it meets the desired outcomes? Give examples.
● What corrective action do you take?
● Why is it important to ensure you’re working the most up to date data?
Describes the methods of validating data.
I use the Filter tool in Alteryx to sort and check data for any inconsistencies or errors.
I use the Data cleansing tool to remove unwanted characters/whitespace, fix spelling mistakes, and standardise data formats. This makes the data clean and ready to analyse.
I use the Unique tool to identify and remove any duplicates. This is to avoid skewed results.
In a sales report 3CX, I used the Filter tool to remove duplicate phone calls so the number of calls per person was accurate.
For a customer survey, I used the Data cleansing tool to correct any spelling mistakes in the responses. This made the data reliable.
By validating and cleaning data with these Alteryx tools, I can ensure the data that I provide is reliable and accurate.
● How could data validation processes be improved in your organisation?
● Explain the impact that the changes you’ve outlined would have on your organisation or team?
● Are there any risks of changing the validation processes in your organisation?
Describes the methods of validating data.
Standardisation – We would need to create a set of rules everyone follows for checking data, as this would make sure everyone checks data the same way, reducing mistakes and saving space.
Automation – Use software to automatically check data for errors, this saves time and lets us focus on more important tasks.
Efficiency – Standardisation makes data checking faster and more reliable and Automation frees up time for more complex work.
Cost Savings – Less storage is needed and fewer mistakes to fix. Fewer staff needed for manual checks, saving money.
Standardisation Risks - Training everyone takes time and money and people might resist changing how they work.
Automation Risks: Software errors can be hard to spot and fix. Staff might rely too much on the system and lose critical skills.
● How could processes in your team be improved to prevent data quality issues arising?
● Can you give an example where you have improved processes in your team to prevent data quality issues arising?
Describes the methods of validating data.
Automate Processes: Automating repetitive tasks can significantly reduce human error. For example, using scripts to clean and validate data before it is entered into the system.
Use Better Software: Implementing advanced data collection software can help in obtaining more accurate raw data. These tools often have built-in validation checks that catch errors early.
Our team had problems with data quality because of mistakes in data entry. I needed to fix this issue.
I created a standard form for everyone to use and set up a simple check to catch errors before the data was saved.
This reduced mistakes and improved our data quality.
● What’s your process for identifying data quality issues and what corrective action do you take? Give examples.
Describes how to identify common data quality issues and the importance of corrective action.
Data Quality Definition: Data quality is a measure of how good the data is based on factors like accuracy, consistency, reliability, and whether it’s up to date.
2 processes for Identifying Issues:
Data Validation: I use filters, spell check, and conditional formatting to spot errors.
Regular Checks: I regularly review data for any inconsistencies or missing information.
One time our call data for sales was all over the place. I needed to improve the quality of this data.
I used a filter tool in Alteryx to filter out for only sales related data, I used a unique tool to ensure there were no duplicates, I standardised al the phone numbers to have the same length with a formula tool.
- How could you use automation to improve the use of data in your role?
Describes how to identify common data quality issues and the importance of corrective action.
Automated Validation and Cleaning with Alteryx: Using Alteryx automated workflows, I can set up processes that automatically validate and clean data. This saves time and ensures the data is accurate and consistent without manual intervention.
Automated Dashboards in Power BI: We can integrate Alteryx with Power BI to create automated dashboards. Alteryx workflows can pull data, clean it, and then feed it into Power BI dashboards, which update in real-time. This ensures our insights are always current and reliable.
