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Data quality

1. Data quality

The purpose of a dashboard is to answer questions about performance.

2. Top dashboard benefits

Well-designed dashboards offer several benefits. Immediate access to at-a-glance information means businesses can improve decision-making.

3. Top dashboard benefits

They also allow for the tracking of multiple data sources simultaneously, saving time and increasing efficiency.

4. Top dashboard benefits

By monitoring business operations, dashboards can help to identify inefficiencies and areas for improvement.

5. Top dashboard benefits

The real-time nature of dashboards can encourage communication and collaboration among team members.

6. Top dashboard benefits

Dashboards can also increase accountability by making data easily accessible to all stakeholders.

7. Common challenges

To effectively communicate data, it is important to provide relevant context. Misleading information can occur when viewers lack the necessary background information to interpret data.

8. Common challenges

Discrepancies may occur when stakeholders are unclear about what each value represents, especially when comparing projected versus actual values.

9. Common challenges

And to prevent viewers from jumping to conclusions, it is essential to present data in a clear and concise manner.

10. Data quality dimensions

Data quality is a critical factor for accurate analysis and trusted business decisions. There are 6 dimensions of data quality.

11. Completeness and uniqueness

Completeness ensures that we have enough data to make meaningful decisions. Incomplete or missing data is a common issue, especially when you work with customer surveys. This can affect data on both product and user levels. Uniqueness is critical to avoid duplication or overlaps in the data. Data cleaning is necessary to ensure that each record is unique and accurate.

12. Integrity, validity, accuracy, consistency

Integrity ensures that all data can be traced and connected. Validity ensures consistency and accuracy of data and checks that all values conform to the correct format. Accuracy examines whether data represents the real-world scenario. High data accuracy enables correct reporting leading to trusted business outcomes. Consistency ensures that data remains the same over time and across multiple sources.

13. Data quality check

You can establish a data quality routine check by asking the following questions: How complete is my data?

14. Data quality check

Do I have an accurate representation of the associated real-world elements?

15. Data quality check

Is my data in sync with the organization?

16. Data quality check

Does my data fit the specified format or range?

17. Data quality check

Do I have any duplicated records?

18. Data quality check

Can the data be traced and connected across all systems in my organization? Answering these questions will help you ensure good data quality for your dashboard.

19. What is metadata?

Now, let's turn our attention to metadata, an important aspect of data quality improvement. Metadata is simply data about the data and describes how, when, and by whom the data was collected. The main purpose of metadata is to ensure that data is FAIR.

20. What is metadata?

Users should be able to easily find

21. What is metadata?

and have access to data.

22. What is metadata?

Data should be easily integrated with other applications.

23. What is metadata?

and be reusable with well-described documentation.

24. Metadata and dashboard

Metadata can improve dashboard quality by showing data reliability: whether data is coming from trusted sources, data selection: what was the data selection process, data reproducibility: whether the same results can be reproduced, and data imputation: how data anomalies are remedied, for example, what type of data preprocessing is performed.

25. Let's practice!

Enough of theory, let's see how well you learned it.

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