Attention all data professionals!
Are you tired of spending hours sifting through endless reports and data sets to find the insights you need? Look no further, because we have the perfect solution for you – BigQuery Reporting and Google BigQuery Knowledge Base.
Our comprehensive dataset consists of 1510 prioritized requirements, solutions, benefits, results, and real-world use cases for BigQuery Reporting and Google BigQuery.
This means that you no longer have to waste time figuring out what questions to ask or how to prioritize your data analysis.
We′ve done all the hard work for you!
But that′s not all – our dataset also includes a detailed overview of the product specifications and usage guidelines.
You′ll know exactly how to use it and get the most out of it.
Plus, our DIY approach makes it an affordable alternative to other reporting and analytics tools on the market.
Don′t just take our word for it – our BigQuery Reporting and Google BigQuery dataset has been researched and tested to ensure top-quality results for businesses of any size.
Our customers have seen a significant increase in efficiency and productivity, leaving them with more time to focus on strategic decision-making.
Compared to other alternatives on the market, BigQuery Reporting and Google BigQuery stands head and shoulders above the rest.
It′s specifically designed for professionals like you, making it the perfect fit for your business needs.
But what about the cost? We believe that every business, big or small, should have access to top-of-the-line reporting and analytics.
That′s why our dataset is available at an unbeatable price.
No hidden fees, just a one-time investment for countless benefits.
We understand that every business has its unique needs, which is why we offer both pros and cons of our product.
Our transparency ensures that you can make an informed decision and see for yourself why BigQuery Reporting and Google BigQuery is the right choice for your business.
So, don′t wait any longer – take your data analysis to the next level with BigQuery Reporting and Google BigQuery Knowledge Base.
Trust us, you won′t regret it.
How does one approach data quality and data validation in a BigQuery pipeline, including techniques such as data profiling, data cleansing, and data normalization, and what are the implications of poor data quality on downstream analytics and reporting?
BigQuery Reporting
In BigQuery, ensure data quality through data profiling, cleansing, and normalization to prevent poor quality from impacting analytics and reporting.
Here are the answers in the context of Google BigQuery:
**Approach to data quality and data validation:**
* Data profiling: Use BigQuery′s `INFORMATION_SCHEMA` to analyze data distribution and identify anomalies.
* Data cleansing: Use `CASE` statements and `REGEXP_REPLACE` to clean and transform data.
* Data normalization: Use `STRUCT` and `ARRAY` functions to standardize data formats.
**Techniques for ensuring data quality:**
* Data validation: Use `CHECK` constraints and `VALIDATE` functions to enforce data rules.
* Data governance: Establish data ownership and lineage using BigQuery′s data catalog.
**Implications of poor data quality:**
* Inaccurate analytics: Poor data quality leads to incorrect insights and bad business decisions.
* Resource waste: Poor data quality can lead to unnecessary computational resources and costs.
CONTROL QUESTION: How does one approach data quality and data validation in a BigQuery pipeline, including techniques such as data profiling, data cleansing, and data normalization, and what are the implications of poor data quality on downstream analytics and reporting?
Big Hairy Audacious Goal (BHAG) for 10 years from now: Here′s a Big Hairy Audacious Goal (BHAG) for 10 years from now for BigQuery Reporting:
**BHAG:** By 2033, BigQuery Reporting will democratize access to high-quality, trusted data insights across the globe, making it the de facto standard for data-driven decision-making. By then, our platform will have enabled 1 million organizations to achieve data quality excellence, resulting in a 50% reduction in reporting errors, a 30% increase in data-driven decisions, and a 25% boost in business performance.
To achieve this ambitious goal, BigQuery Reporting needs to tackle the critical issue of data quality and data validation in its pipeline. Here′s a high-level approach to tackling this challenge:
**Short-term goals (2023-2025):**
1. **Data Profiling**: Integrate data profiling capabilities within BigQuery Reporting to provide users with a deeper understanding of their data distribution, anomalies, and patterns. This will involve developing algorithms to detect outliers, data skewness, and other statistical anomalies.
2. **Data Quality Metrics**: Establish a set of data quality metrics, such as data completeness, accuracy, and freshness, to help users measure the health of their data. These metrics will serve as a foundation for data validation and cleansing.
3. **Data Cleansing**: Develop an extensible data cleansing framework that allows users to write custom data cleaning rules, leverage machine learning-based anomaly detection, and integrate with external data validation services.
**Mid-term goals (2025-2028):**
1. **Data Validation Workflows**: Create a visual workflow editor for data validation, enabling users to design, execute, and monitor data validation processes. This will include integration with data profiling and cleansing capabilities.
2. **Data Quality Dashboards**: Develop interactive dashboards to provide real-time visibility into data quality metrics, enabling users to track data quality issues, identify root causes, and measure the impact of data quality on downstream analytics and reporting.
3. **Machine Learning-based Data Quality**: Integrate machine learning algorithms to detect complex data quality issues, such as data drift, concept drift, and anomalies, and provide recommendations for data cleansing and validation.
**Long-term goals (2028-2033):**
1. **Automation of Data Quality**: Achieve automation of data quality processes through the integration of machine learning, artificial intelligence, and natural language processing. This will enable BigQuery Reporting to proactively detect and resolve data quality issues in real-time.
2. **Data Quality Governance**: Establish a data quality governance framework that provides role-based access control, data quality policies, and auditing capabilities to ensure data quality standards are met across organizations.
3. **Ecosystem of Data Quality Partners**: Foster an ecosystem of partners offering specialized data quality services, such as data validation, data enrichment, and data analytics, to provide users with a comprehensive data quality solution.
**Implications of poor data quality:**
1. ** Reporting Errors**: Poor data quality can lead to inaccurate reports, which can result in misinformed business decisions, revenue loss, and damage to reputation.
2. **Analytics Inefficiencies**: Analytics models trained on poor-quality data may produce inaccurate predictions, leading to inefficient resource allocation and poor customer experiences.
3. **Loss of Trust**: Repeated exposure to poor data quality can erode trust in analytics and reporting, leading to reduced adoption and usage of data-driven decision-making tools.
By achieving this BHAG, BigQuery Reporting will become the go-to platform for organizations seeking to achieve data quality excellence, driving business success and shaping the future of data-driven decision-making.
"I can`t thank the creators of this dataset enough. The prioritized recommendations have streamlined my workflow, and the overall quality of the data is exceptional. A must-have resource for any analyst."
**Client Situation:**
Our client, a leading e-commerce company, leverages BigQuery as its central data warehouse to power business intelligence and analytics. With millions of customer interactions every day, the company relies on its BigQuery pipeline to inform strategic decisions, optimize operations, and drive growth. However, the data quality issues in their BigQuery pipeline were hindering the accuracy and reliability of their reporting, leading to poor decision-making and lost revenue opportunities.
**Consulting Methodology:**
Our team of data quality experts employed a structured approach to address data quality issues in the BigQuery pipeline. The methodology consisted of:
1. **Data Profiling**: We analyzed the data distribution, frequency, and correlation to identify anomalies, outliers, and inconsistencies. This involved using BigQuery′s built-in data profiling features, such as `INFO_SCHEMA` tables and `DATA_TABLE` metadata, to understand the data structure and content (Google Cloud, 2022).
2. **Data Cleansing**: We applied data cleansing techniques to detect and correct errors, such as handling missing values, removing duplicates, and performing data type conversions. This step involved writing custom SQL scripts to correct data inconsistencies and data quality issues (Talend, 2020).
3. **Data Normalization**: We normalized the data to ensure consistency in formatting and representation, enabling easier analysis and reporting. This involved applying data transformation techniques, such as aggregating data and performing data aggregations (Kimball et al., 2013).
4. **Data Validation**: We implemented data validation rules to ensure data accuracy and completeness. This involved creating custom data quality checks using BigQuery′s `ASSERT` statement and `TRY`-`CATCH` blocks to handle errors and exceptions (BigQuery, 2022).
**Deliverables:**
Our deliverables included:
1. A comprehensive data quality assessment report highlighting data quality issues and recommendations for improvement.
2. A set of custom SQL scripts for data profiling, data cleansing, and data normalization.
3. A data validation framework using BigQuery′s built-in features and custom data quality checks.
4. A set of KPIs (Key Performance Indicators) to monitor data quality and its impact on downstream analytics and reporting.
**Implementation Challenges:**
During the implementation, we faced several challenges, including:
1. ** Complexity of data processing pipelines**: The client′s BigQuery pipeline was complex, with multiple data sources and processing stages, making it difficult to identify and address data quality issues.
2. **Lack of data governance**: The client lacked a data governance framework, making it challenging to establish data quality standards and policies.
3. **Insufficient data documentation**: The client′s data documentation was incomplete, making it difficult to understand the data structure and content.
**KPIs and Management Considerations:**
To measure the success of the data quality initiative, we established the following KPIs:
1. **Data accuracy**: Measured by the percentage of accurate records in the data pipeline.
2. **Data completeness**: Measured by the percentage of complete records in the data pipeline.
3. **Data freshness**: Measured by the time lag between data ingestion and reporting.
To sustain the data quality initiative, we recommended the following management considerations:
1. **Establish a data governance framework**: Develop a data governance framework to establish data quality standards, policies, and procedures.
2. **Assign data quality ownership**: Designate a data quality owner to oversee data quality initiatives and ensure accountability.
3. **Monitor data quality metrics**: Regularly monitor data quality metrics to identify areas for improvement and track the impact of data quality initiatives on downstream analytics and reporting.
**Implications of Poor Data Quality:**
Poor data quality can have significant implications on downstream analytics and reporting, including:
1. **Inaccurate insights**: Poor data quality can lead to inaccurate insights, which can result in poor decision-making and lost revenue opportunities (Harvard Business Review, 2016).
2. **Reduced confidence**: Poor data quality can reduce confidence in the accuracy of analytics and reporting, leading to decreased adoption and utilization (Dresner Advisory Services, 2020).
3. **Increased costs**: Poor data quality can result in increased costs associated with data rework, reprocessing, and remediation (Gartner, 2020).
**Conclusion:**
Ensuring data quality in a BigQuery pipeline is critical for accurate reporting and informed decision-making. Our approach, which included data profiling, data cleansing, data normalization, and data validation, helped our client address data quality issues and improve the accuracy and reliability of their reporting. By establishing a data governance framework, assigning data quality ownership, and monitoring data quality metrics, our client can sustain the data quality initiative and reap the benefits of high-quality data.
**References:**
BigQuery. (2022). Data Profiling. Retrieved from u003chttps://cloud.google.com/bigquery/docs/schema-datasets#data_profilingu003e
Dresner Advisory Services. (2020). 2020 Wisdom of Crowds Business Intelligence Market Study. Retrieved from u003chttps://www.dresneradvisory.com/2020-wisdom-of-crowds-business-intelligence-market-study/u003e
Gartner. (2020). How to Improve Data Quality. Retrieved from u003chttps://www.gartner.com/smarter-with-gartner/how-to-improve-data-quality/u003e
Google Cloud. (2022). BigQuery Data Profiling. Retrieved from u003chttps://cloud.google.com/bigquery/docs/data-profilingu003e
Harvard Business Review. (2016). The Cost of Poor Data Quality. Retrieved from u003chttps://hbr.org/2016/09/the-cost-of-poor-data-qualityu003e
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., u0026 Becker, B. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley u0026 Sons.
Talend. (2020). Data Profiling: The Foundation of Data Governance. Retrieved from u003chttps://www.talend.com/resources/data-profiling-foundation-data-governance/u003e
Are you tired of spending hours sifting through endless reports and data sets to find the insights you need? Look no further, because we have the perfect solution for you – BigQuery Reporting and Google BigQuery Knowledge Base.
Our comprehensive dataset consists of 1510 prioritized requirements, solutions, benefits, results, and real-world use cases for BigQuery Reporting and Google BigQuery.
This means that you no longer have to waste time figuring out what questions to ask or how to prioritize your data analysis.
We′ve done all the hard work for you!
But that′s not all – our dataset also includes a detailed overview of the product specifications and usage guidelines.
You′ll know exactly how to use it and get the most out of it.
Plus, our DIY approach makes it an affordable alternative to other reporting and analytics tools on the market.
Don′t just take our word for it – our BigQuery Reporting and Google BigQuery dataset has been researched and tested to ensure top-quality results for businesses of any size.
Our customers have seen a significant increase in efficiency and productivity, leaving them with more time to focus on strategic decision-making.
Compared to other alternatives on the market, BigQuery Reporting and Google BigQuery stands head and shoulders above the rest.
It′s specifically designed for professionals like you, making it the perfect fit for your business needs.
But what about the cost? We believe that every business, big or small, should have access to top-of-the-line reporting and analytics.
That′s why our dataset is available at an unbeatable price.
No hidden fees, just a one-time investment for countless benefits.
We understand that every business has its unique needs, which is why we offer both pros and cons of our product.
Our transparency ensures that you can make an informed decision and see for yourself why BigQuery Reporting and Google BigQuery is the right choice for your business.
So, don′t wait any longer – take your data analysis to the next level with BigQuery Reporting and Google BigQuery Knowledge Base.
Trust us, you won′t regret it.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1510 prioritized BigQuery Reporting requirements. - Extensive coverage of 86 BigQuery Reporting topic scopes.
- In-depth analysis of 86 BigQuery Reporting step-by-step solutions, benefits, BHAGs.
- Detailed examination of 86 BigQuery Reporting case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Data Pipelines, Data Governance, Data Warehousing, Cloud Based, Cost Estimation, Data Masking, Data API, Data Refining, BigQuery Insights, BigQuery Projects, BigQuery Services, Data Federation, Data Quality, Real Time Data, Disaster Recovery, Data Science, Cloud Storage, Big Data Analytics, BigQuery View, BigQuery Dataset, Machine Learning, Data Mining, BigQuery API, BigQuery Dashboard, BigQuery Cost, Data Processing, Data Grouping, Data Preprocessing, BigQuery Visualization, Scalable Solutions, Fast Data, High Availability, Data Aggregation, On Demand Pricing, Data Retention, BigQuery Design, Predictive Modeling, Data Visualization, Data Querying, Google BigQuery, Security Config, Data Backup, BigQuery Limitations, Performance Tuning, Data Transformation, Data Import, Data Validation, Data CLI, Data Lake, Usage Report, Data Compression, Business Intelligence, Access Control, Data Analytics, Query Optimization, Row Level Security, BigQuery Notification, Data Restore, BigQuery Analytics, Data Cleansing, BigQuery Functions, BigQuery Best Practice, Data Retrieval, BigQuery Solutions, Data Integration, BigQuery Table, BigQuery Explorer, Data Export, BigQuery SQL, Data Storytelling, BigQuery CLI, Data Storage, Real Time Analytics, Backup Recovery, Data Filtering, BigQuery Integration, Data Encryption, BigQuery Pattern, Data Sorting, Advanced Analytics, Data Ingest, BigQuery Reporting, BigQuery Architecture, Data Standardization, BigQuery Challenges, BigQuery UDF
BigQuery Reporting Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
BigQuery Reporting
In BigQuery, ensure data quality through data profiling, cleansing, and normalization to prevent poor quality from impacting analytics and reporting.
Here are the answers in the context of Google BigQuery:
**Approach to data quality and data validation:**
* Data profiling: Use BigQuery′s `INFORMATION_SCHEMA` to analyze data distribution and identify anomalies.
* Data cleansing: Use `CASE` statements and `REGEXP_REPLACE` to clean and transform data.
* Data normalization: Use `STRUCT` and `ARRAY` functions to standardize data formats.
**Techniques for ensuring data quality:**
* Data validation: Use `CHECK` constraints and `VALIDATE` functions to enforce data rules.
* Data governance: Establish data ownership and lineage using BigQuery′s data catalog.
**Implications of poor data quality:**
* Inaccurate analytics: Poor data quality leads to incorrect insights and bad business decisions.
* Resource waste: Poor data quality can lead to unnecessary computational resources and costs.
CONTROL QUESTION: How does one approach data quality and data validation in a BigQuery pipeline, including techniques such as data profiling, data cleansing, and data normalization, and what are the implications of poor data quality on downstream analytics and reporting?
Big Hairy Audacious Goal (BHAG) for 10 years from now: Here′s a Big Hairy Audacious Goal (BHAG) for 10 years from now for BigQuery Reporting:
**BHAG:** By 2033, BigQuery Reporting will democratize access to high-quality, trusted data insights across the globe, making it the de facto standard for data-driven decision-making. By then, our platform will have enabled 1 million organizations to achieve data quality excellence, resulting in a 50% reduction in reporting errors, a 30% increase in data-driven decisions, and a 25% boost in business performance.
To achieve this ambitious goal, BigQuery Reporting needs to tackle the critical issue of data quality and data validation in its pipeline. Here′s a high-level approach to tackling this challenge:
**Short-term goals (2023-2025):**
1. **Data Profiling**: Integrate data profiling capabilities within BigQuery Reporting to provide users with a deeper understanding of their data distribution, anomalies, and patterns. This will involve developing algorithms to detect outliers, data skewness, and other statistical anomalies.
2. **Data Quality Metrics**: Establish a set of data quality metrics, such as data completeness, accuracy, and freshness, to help users measure the health of their data. These metrics will serve as a foundation for data validation and cleansing.
3. **Data Cleansing**: Develop an extensible data cleansing framework that allows users to write custom data cleaning rules, leverage machine learning-based anomaly detection, and integrate with external data validation services.
**Mid-term goals (2025-2028):**
1. **Data Validation Workflows**: Create a visual workflow editor for data validation, enabling users to design, execute, and monitor data validation processes. This will include integration with data profiling and cleansing capabilities.
2. **Data Quality Dashboards**: Develop interactive dashboards to provide real-time visibility into data quality metrics, enabling users to track data quality issues, identify root causes, and measure the impact of data quality on downstream analytics and reporting.
3. **Machine Learning-based Data Quality**: Integrate machine learning algorithms to detect complex data quality issues, such as data drift, concept drift, and anomalies, and provide recommendations for data cleansing and validation.
**Long-term goals (2028-2033):**
1. **Automation of Data Quality**: Achieve automation of data quality processes through the integration of machine learning, artificial intelligence, and natural language processing. This will enable BigQuery Reporting to proactively detect and resolve data quality issues in real-time.
2. **Data Quality Governance**: Establish a data quality governance framework that provides role-based access control, data quality policies, and auditing capabilities to ensure data quality standards are met across organizations.
3. **Ecosystem of Data Quality Partners**: Foster an ecosystem of partners offering specialized data quality services, such as data validation, data enrichment, and data analytics, to provide users with a comprehensive data quality solution.
**Implications of poor data quality:**
1. ** Reporting Errors**: Poor data quality can lead to inaccurate reports, which can result in misinformed business decisions, revenue loss, and damage to reputation.
2. **Analytics Inefficiencies**: Analytics models trained on poor-quality data may produce inaccurate predictions, leading to inefficient resource allocation and poor customer experiences.
3. **Loss of Trust**: Repeated exposure to poor data quality can erode trust in analytics and reporting, leading to reduced adoption and usage of data-driven decision-making tools.
By achieving this BHAG, BigQuery Reporting will become the go-to platform for organizations seeking to achieve data quality excellence, driving business success and shaping the future of data-driven decision-making.
Customer Testimonials:
"I can`t thank the creators of this dataset enough. The prioritized recommendations have streamlined my workflow, and the overall quality of the data is exceptional. A must-have resource for any analyst."
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"It`s refreshing to find a dataset that actually delivers on its promises. This one truly surpassed my expectations."
BigQuery Reporting Case Study/Use Case example - How to use:
**Case Study: Ensuring Data Quality in a BigQuery Pipeline for Accurate Reporting****Client Situation:**
Our client, a leading e-commerce company, leverages BigQuery as its central data warehouse to power business intelligence and analytics. With millions of customer interactions every day, the company relies on its BigQuery pipeline to inform strategic decisions, optimize operations, and drive growth. However, the data quality issues in their BigQuery pipeline were hindering the accuracy and reliability of their reporting, leading to poor decision-making and lost revenue opportunities.
**Consulting Methodology:**
Our team of data quality experts employed a structured approach to address data quality issues in the BigQuery pipeline. The methodology consisted of:
1. **Data Profiling**: We analyzed the data distribution, frequency, and correlation to identify anomalies, outliers, and inconsistencies. This involved using BigQuery′s built-in data profiling features, such as `INFO_SCHEMA` tables and `DATA_TABLE` metadata, to understand the data structure and content (Google Cloud, 2022).
2. **Data Cleansing**: We applied data cleansing techniques to detect and correct errors, such as handling missing values, removing duplicates, and performing data type conversions. This step involved writing custom SQL scripts to correct data inconsistencies and data quality issues (Talend, 2020).
3. **Data Normalization**: We normalized the data to ensure consistency in formatting and representation, enabling easier analysis and reporting. This involved applying data transformation techniques, such as aggregating data and performing data aggregations (Kimball et al., 2013).
4. **Data Validation**: We implemented data validation rules to ensure data accuracy and completeness. This involved creating custom data quality checks using BigQuery′s `ASSERT` statement and `TRY`-`CATCH` blocks to handle errors and exceptions (BigQuery, 2022).
**Deliverables:**
Our deliverables included:
1. A comprehensive data quality assessment report highlighting data quality issues and recommendations for improvement.
2. A set of custom SQL scripts for data profiling, data cleansing, and data normalization.
3. A data validation framework using BigQuery′s built-in features and custom data quality checks.
4. A set of KPIs (Key Performance Indicators) to monitor data quality and its impact on downstream analytics and reporting.
**Implementation Challenges:**
During the implementation, we faced several challenges, including:
1. ** Complexity of data processing pipelines**: The client′s BigQuery pipeline was complex, with multiple data sources and processing stages, making it difficult to identify and address data quality issues.
2. **Lack of data governance**: The client lacked a data governance framework, making it challenging to establish data quality standards and policies.
3. **Insufficient data documentation**: The client′s data documentation was incomplete, making it difficult to understand the data structure and content.
**KPIs and Management Considerations:**
To measure the success of the data quality initiative, we established the following KPIs:
1. **Data accuracy**: Measured by the percentage of accurate records in the data pipeline.
2. **Data completeness**: Measured by the percentage of complete records in the data pipeline.
3. **Data freshness**: Measured by the time lag between data ingestion and reporting.
To sustain the data quality initiative, we recommended the following management considerations:
1. **Establish a data governance framework**: Develop a data governance framework to establish data quality standards, policies, and procedures.
2. **Assign data quality ownership**: Designate a data quality owner to oversee data quality initiatives and ensure accountability.
3. **Monitor data quality metrics**: Regularly monitor data quality metrics to identify areas for improvement and track the impact of data quality initiatives on downstream analytics and reporting.
**Implications of Poor Data Quality:**
Poor data quality can have significant implications on downstream analytics and reporting, including:
1. **Inaccurate insights**: Poor data quality can lead to inaccurate insights, which can result in poor decision-making and lost revenue opportunities (Harvard Business Review, 2016).
2. **Reduced confidence**: Poor data quality can reduce confidence in the accuracy of analytics and reporting, leading to decreased adoption and utilization (Dresner Advisory Services, 2020).
3. **Increased costs**: Poor data quality can result in increased costs associated with data rework, reprocessing, and remediation (Gartner, 2020).
**Conclusion:**
Ensuring data quality in a BigQuery pipeline is critical for accurate reporting and informed decision-making. Our approach, which included data profiling, data cleansing, data normalization, and data validation, helped our client address data quality issues and improve the accuracy and reliability of their reporting. By establishing a data governance framework, assigning data quality ownership, and monitoring data quality metrics, our client can sustain the data quality initiative and reap the benefits of high-quality data.
**References:**
BigQuery. (2022). Data Profiling. Retrieved from u003chttps://cloud.google.com/bigquery/docs/schema-datasets#data_profilingu003e
Dresner Advisory Services. (2020). 2020 Wisdom of Crowds Business Intelligence Market Study. Retrieved from u003chttps://www.dresneradvisory.com/2020-wisdom-of-crowds-business-intelligence-market-study/u003e
Gartner. (2020). How to Improve Data Quality. Retrieved from u003chttps://www.gartner.com/smarter-with-gartner/how-to-improve-data-quality/u003e
Google Cloud. (2022). BigQuery Data Profiling. Retrieved from u003chttps://cloud.google.com/bigquery/docs/data-profilingu003e
Harvard Business Review. (2016). The Cost of Poor Data Quality. Retrieved from u003chttps://hbr.org/2016/09/the-cost-of-poor-data-qualityu003e
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., u0026 Becker, B. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley u0026 Sons.
Talend. (2020). Data Profiling: The Foundation of Data Governance. Retrieved from u003chttps://www.talend.com/resources/data-profiling-foundation-data-governance/u003e
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