Data Quality
Data Quality
Data Quality
Data Brilliance, Delivered: Quality in Every Query.
Data Brilliance, Delivered: Quality in Every Query.
Data Brilliance, Delivered: Quality in Every Query.
In business and data engineering, data quality is key for effective decision-making.
In business and data engineering, data quality is key for effective decision-making.
In business and data engineering, data quality is key for effective decision-making.
Quality data is vital for strategy, customer interactions, and insights from analytics. Accurate data is crucial, as advanced strategies and algorithms need reliable data to work well. For businesses, good data quality is essential for success.
Quality data is vital for strategy, customer interactions, and insights from analytics. Accurate data is crucial, as advanced strategies and algorithms need reliable data to work well. For businesses, good data quality is essential for success.
Quality data is vital for strategy, customer interactions, and insights from analytics. Accurate data is crucial, as advanced strategies and algorithms need reliable data to work well. For businesses, good data quality is essential for success.
Experian Data Quality's survey found that 94% of organizations report
inaccuracies in their customer data, leading to negative consequences.
Experian Data Quality's survey found
that 94% of organizations report
inaccuracies in their customer data,
leading to negative consequences.
Experian Data Quality's survey found
that 94% of organizations report
inaccuracies in their customer data,
leading to negative consequences.
IBM's study found that poor data quality costs the U.S. economy
about $3.1 trillion yearly.
IBM's study found that poor data quality
costs the U.S. economy about $3.1
trillion yearly.
IBM's study found that poor data quality
costs the U.S. economy about $3.1
trillion yearly.
The Data Warehouse Institute reports poor data quality costing businesses
$600 billion annually.
The Data Warehouse Institute reports poor
data quality costing businesses $600
billion annually.
The Data Warehouse Institute reports poor
data quality costing businesses $600
billion annually.
?
?
?
Facets of Data Quality
Facets of Data Quality
Facets of Data Quality
Accuracy
Accuracy
Accuracy
Accurately represent the real world values.
Completeness
Completeness
Completeness
All necessary data is available for decision making.
Consistency
Consistency
Consistency
Data consistency between datasets/systems.
Timeliness
Timeliness
Timeliness
Is the data up to date?
Validity
Validity
Validity
Does data follow all the underlying rules, formats, data types, etc?
Uniqueness
Uniqueness
Uniqueness
Duplicated records will misrepresent the real data events.
Reliability
Reliability
Reliability
Is the data trustworthy over time? How can we check for this?
Visibility
Visibility
Visibility
Everyone should be able to view the current status of the data.
Solutions
Solutions
Solutions
Data Contracts
Data Contracts
Data Contracts
Data Contracts between data producers and data consumers. The need for an agreement between two parties exchanging data is fundamental and data contracts are the answer to a lot of data quality issues.
Data Contracts between data producers and data consumers. The need for an agreement between two parties exchanging data is fundamental and data contracts are the answer to a lot of data quality issues.
Data Contracts between data producers and data consumers. The need for an agreement between two parties exchanging data is fundamental and data contracts are the answer to a lot of data quality issues.
By Design
By Design
By Design
A holistic approach to data quality and different tools and design per data quality categories. Data integrity from sources requires different tools than data quality unit checks within the transformation layer.
A holistic approach to data quality and different tools and design per data quality categories. Data integrity from sources requires different tools than data quality unit checks within the transformation layer.
A holistic approach to data quality and different tools and design per data quality categories. Data integrity from sources requires different tools than data quality unit checks within the transformation layer.
Integrated
Integrated
Integrated
Validation of data through quality checks after each code change (through a CI pipeline) and data change (at each iteration of your data pipeline)
Validation of data through quality checks after each code change (through a CI pipeline) and data change (at each iteration of your data pipeline)
Validation of data through quality checks after each code change (through a CI pipeline) and data change (at each iteration of your data pipeline)
Constant Monitoring
Constant Monitoring
Constant Monitoring
Data Quality issues can go undetected in your CI pipelines and it is important to check for statistical deviations from one push to another.
Data Quality issues can go undetected in your CI pipelines and it is important to check for statistical deviations from one push to another.
Data Quality issues can go undetected in your CI pipelines and it is important to check for statistical deviations from one push to another.
Ownership
Ownership
Ownership
It should be your responsibility to ensure that data is up to standard, one error might compromise the trustworthiness of the client/consumer even after fixing it.
It should be your responsibility to ensure that data is up to standard, one error might compromise the trustworthiness of the client/consumer even after fixing it.
It should be your responsibility to ensure that data is up to standard, one error might compromise the trustworthiness of the client/consumer even after fixing it.
Technologies
Technologies
Technologies
We use the best combination of open-source and enterprise tools to tackle data quality under all its forms.
We use the best combination of open-source and enterprise tools to tackle data quality under all its forms.
We use the best combination of open-source and enterprise tools to tackle data quality under all its forms.
Why us
Why us
Why us
We prioritize data quality, employing strong systems and partnerships with leading tools to ensure your business has reliable data for informed decisions.
We prioritize data quality, employing strong systems and partnerships with leading tools to ensure your business has reliable data for informed decisions.
We prioritize data quality, employing strong systems and partnerships with leading tools to ensure your business has reliable data for informed decisions.
Robust
systems
Robust systems
Robust
systems
Partnerships with leading tools
Partnerships with leading tools
Partnerships with leading tools
Managed data quality
Managed data quality
Managed data quality
Resources
Resources
Resources
