Data Quality Quiz

Which of these is a factor that contributes to poor data quality?

Systems operating in silos
Lack of standards
Poor or incomplete documentation
Lack of governance
All of the above

Which of these is NOT a goal of Data Quality management?

Define and implement processes to measure, monitor, and report on data quality levels
Develop a governed approach to make data fit for purpose based on data consumers’ requirements
To ensure integration capabilities between structured and unstructured Content
Identify and advocate for opportunities to improve the quality of data, through process and system improvements
Define standards, requirements, and specifications for data quality controls as part of the data lifecycle

One of the main principles of a good Data Quality programme focuses not just on correcting errors but identifying why the error happened and addressing it at its core. This is also known as:

Prevention
Lifecycle Management
Criticality
Root cause remediation
Data deep dive

What are Data Quality dimensions?

A lexicon of terms used to define Data Management broadly
A measurable feature or characteristic of data that provides a way to assess whether the data is fit for purpose
A framework for assessing Data Quality
The size and breadth of the Data Quality Program
A set of standards used to ensure data quality

When profiling our customer address dataset, we found erroneous zip/postal code records that were 5 digits instead of 6. Which Data Quality Dimension does this error represent?

Uniqueness
Timeliness
Validity
Consistency
Completeness

ISO 8000 is the international standard for Data Quality that is developed with the aim to:

Ensure every organisation can duplicate the standards so there is a common standard across all industries
Help organisations represent metadata with data standards, naming conventions and other best practices
Help addresses the semantics of data, the representation of data, and the registration of the descriptions of that data
Help organisations to define what is and what is not quality data, properly request for it and validate if what they have received is indeed quality data
None of the above

What is NOT a common barrier to effective data quality management?

Using inappropriate tools or measurements
Leadership is not aware of the importance of data management
Lack of proper business governance
Lack of superior tools or software to assist in data management activities
Difficulty justifying any type of improvements made by proper data management

When dealing with issues caused by data entry processes, what constitutes a training issue?

Basic data entry features like a drop down list being in the incorrect order
Reusing fields instead of updating the data model
Lack of awareness of knowledge of the importance of proper data entry or misaligned incentives for data entry
Poor or unintuitive interface for data entry
All of the above

Which of these data quality issues is NOT prevented by enforcing referential integrity rules?

Orphan rows
Duplicate data
Inaccurate data
Multiple values for the same calculation
Improved response times

Who should be the one to assess the results from data profiling?

Data governance manager
Data owner
Senior management
Data Modeller
Data analyst

Inspecting the quality of data using statistical techniques is called:

Data Profiling
Data Enhancement
Data Transformation
Data Munging
Data Extraction

What is NOT true about data cleansing?

To continuously remediate data through cleansing incurs costs and increases risk
Data cleansing includes detecting and correcting data errors to bring the quality of data to an acceptable level
Data cleansing requires that you transform the data so that it adheres to data standards and domain rules
Correcting data errors on an ongoing basis is always more cost effective than other alternatives
Correcting data errors on an ongoing basis is sometimes necessary

When we are enhancing customer data with information such as their gender, age, housing, we are looking at information that is:

Contextual
Demographic
Psychographic
Valuable
Personal

Which best describes data parsing?

Taking a list of phone numbers and breaking it into area code, exchange, and line number before transforming into a standard format
Adding information that is useful to a phone number dataset to help better describe it
Manually collecting phone number data from consumers
Analysing phone number data for counts of null values
Correcting and deleting erroneous data in a list of phone numbers

Which of these is TRUE about defining high-quality data?

Many people can define what high-quality data means
The goal of defining high-quality data is to ensure data is 100% error free
High quality data is defined as data that is fit for the purposes of data consumers
High quality data is defined by the data characteristics, not by business needs
The data has to be "right" is sufficient to determine what high-quality data means

Which of these is NOT a good question to ask when assessing an organisation’s readiness to embark on data quality improvement

What are the main drivers for data quality improvement?
What are the technologies required to improve data quality?
What is the impact of poor data quality on the business and its operations?
What is the level of tolerance for poor data quality?
What are the governance structures required to improve data quality?

When adopting or developing a framework or methodology to guide your data quality strategy, which method should be considered?

How to manage metadata produced through data quality processes
How to identify critical data that can help the business meet their needs
How to prioritise and manage issues
How to define business rules and data quality standards
All of the above

Which of these is NOT true of Critical Data?

Business rules are not required for Critical Data improvement
Master Data is among the most important data
Critical Data lists allow DQ teams to focus their efforts
Improving Critical Data will provide greater value to the organisation
Data can be prioritised by financial, regulatory or customer impact factors

Data profiling assists in many aspects of improving data quality except:

Identifying duplicate columns and exposing embedded value dependencies
Drilling down into the analysed data
Determining business process impacts
Discovering the structure, content, and quality of your critical data
None of the above

Which of these is an example of a good goal for data quality improvement?

Remove all null values from customer data
Improve accuracy of customer credit score by 10% by referencing data from credit reporting agencies
Remove duplicates and match/merge all customer data that are of the same entity
Increase completeness of customer "Job Title" field by 10% to improve customer segmentation and increase ROI from email marketing by 20%
All of the above

What is NOT an advantage of defining data quality rules upfront?

Providing functional requirements for procuring a better data quality tool
Setting clear expectation for data quality characteristics
Creating the foundation for ongoing data quality measurement and reporting
Providing the requirements for system edits and controls that prevent data issues
Providing data quality requirements to external parties

What should a data quality rule ultimately be connected to?

The data quality dimension
How easily the IT team can implement it
The KPI of the data governance team
The business impact it has on the success of the organisation
All of the above

If a metric is calculated by taking the percentage of duplicated records to total records, we are looking at:

A business rule that stipulates there cannot be any blank field in this column
The data quality dimension of uniqueness
A business rule that governs the timeframe in which records must arrive
The data quality dimension of completeness
The data quality dimension of validity

When diagnosing issues, the operational procedure should guide the Data Quality Operations team to:

Evaluate if the are process issues that contributed to the data quality incident
Review the data issues with the information processing flow and isolate the part where the flaw is introduced
Evaluate if there have been any environmental changes causing errors
Determine if the issues with external data affected the data quality
All of the above

Which of these should NOT be a focus of data quality reporting?

Whether or not the quality of data is trending up or down
The status of any open data quality issues
A high-level view of data quality scores based on various metrics within certain thresholds
Conformance of the IT and business users against the rules set
The cost of implementing the data quality project

Which of these statements is TRUE of data quality tools?

Data Profiling tools are only necessary for Data Science projects
Data quality tools can be purchased off the shelf with the required logic to solve DQ issues
Data Quality tools are only needed in the Operation phase of a DQ project
Data Modelling tools have no impact on the quality of data
Organisations must input their own context specific rules and actions into any tool

Which of the following is the best example of the data quality dimension of ‘consistency’?

The product file has 70% duplicated entries
The invoice data in the dataset is $90 out this month
The source data for the weekly report arrived 1 day late
The postal/zip codes in the customer file are missing
The number of records in the Billing System does not match the number of records in the data warehouse

Which of these is not a stage in the Shewhart Cycle?

Plan
Do
Initiate
Check
Act

What is the main reason why you’d investigate data quality from the top-down and bottom-up at the same time?

It involves the whole team
It balances the impact to the business and the actual state of the data
To maximise the value from your DQ tool investment
To keep data architects busy while the profilers get on with the work
It allows the profiler to show the business the true state of the data

Why would you use Manual Directed Data Quality Correction?

To automate all data cleanse and correction routines
To ensure automated cleanse and correction tool results are manually checked before committing outputs
To leverage spreadsheets to manually inspect and correct data
To use a data quality improvement manual for guidance on data cleansing activities
To ensure data quality teams have been properly managed during their work

When measuring data quality, which three levels of granularity should you measure?

Data element value, data instance or record, and data set
Big data, small data, detailed data
Contact data, location data, and product data
Historical data, current data and future dated data
Departmental data, regional data, and enterprise data

Which of these would you expect in a Data Quality Service Level Agreement (SLA)?

Details of the Data transfer API
A conceptual data model
Server uptime guarantee
Respective roles & responsibilities for data quality
Marketing strategy

Which of these is NOT an expected role of a Data Quality Oversight Board?

Producing certification & compliance policies
Developing & maintaining data quality
Data profiling & analysis
Establishing communications & feedback mechanisms
Setting data quality improvement priorities

Data Quality Quiz

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