Informatica CDQ | What is Data Quality & Its Key Dimensions

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Day4-Informatica Cloud Data Quality(CDQ)
Agenda

• What is Data Quality?
• Dimensions of Data Quality

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What is Data Quality?

• Data Quality refers to the condition or fitness
  of data for its intended use, ensuring that the
  data is accurate, consistent, reliable, and
  relevant to meet the needs of business processes,
  decision-making, and analytics. High-quality data
  is essential for organizations to make informed
  decisions, maintain regulatory compliance, and
  enhance operational efficiency. Poor data quality
  can lead to incorrect analysis, lost
  opportunities, increased costs, and operational
  inefficiencies.
• Example of Data Quality in Practice
• Imagine a retail company that collects customer
  data to send personalized marketing emails. If
  the customer records contain outdated or
  incorrect information (such as incorrect email
  addresses or missing names), the company might
  send marketing emails to the wrong recipients or
  miss potential customers. By ensuring high data
  qualitythrough cleaning up the data, ensuring
  proper formats, and filling in missing valuesthe
  company can improve the effectiveness of its
  marketing campaigns and customer relationships.

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Dimensions of Data Quality

• The Dimensions of Data Quality are the
  characteristics or attributes used to assess and
  measure the quality of data. These dimensions
  help organizations evaluate how well their data
  meets the required standards for business
  decision-making, operational processes, and
  compliance. Understanding and managing these
  dimensions is crucial to maintaining
  high-quality, reliable data.
• Here are the key dimensions of data quality
• 1. Accuracy
• Definition Data is considered accurate if it
  correctly reflects the real-world entity or event
  it represents.
• Example If a customer's address is recorded, it
  should match their actual physical address, not
  contain errors like misspellings or incorrect
  information.
• Importance Inaccurate data can lead to faulty
  business decisions and misunderstandings.

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Dimensions of Data Quality

• 2. Consistency
• Definition Data is consistent when it does not
  conflict with other data sources. The same data
  element should have the same value across
  multiple datasets or systems.
• Example A customers email address in the CRM
  system should match the one in the billing
  system.
• Importance Consistency is vital for accurate
  reporting and integration between different data
  sources or systems.
• 3. Completeness
• Definition Data is complete when all necessary
  information is present, without missing values or
  incomplete fields.
• Example A customer record should have a full
  name, address, email, and phone number. Missing
  these details would be considered incomplete.
• Importance Incomplete data can lead to missed
  opportunities, especially in analytics and
  decision-making.

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Dimensions of Data Quality

• 4. Timeliness
• Definition Data is timely when it is up to date
  and available when needed. Outdated data may lead
  to poor decision-making or missed opportunities.
• Example A product inventory database should
  reflect the real-time availability of products,
  not outdated stock levels.
• Importance Timeliness is crucial for systems
  that rely on real-time or near-real-time data,
  such as financial transactions or inventory
  management.
• 5. Validity
• Definition Valid data conforms to defined
  formats, rules, and constraints. It is data that
  adheres to business rules and expected values.
• Example A date field should contain a valid
  date, and a phone number should follow a standard
  format (e.g., country code, area code, etc.).
• Importance Invalid data can cause errors in data
  processing or analysis and may require extra
  effort to clean and standardize.

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Dimensions of Data Quality

• 6. Uniqueness
• Definition Data is unique when there are no
  duplicates in the dataset, ensuring that each
  data point appears only once.
• Example A customer should have only one record
  in the database, not multiple records with
  different variations of their name or contact
  details.
• Importance Duplicate records can lead to
  inefficiencies, reporting errors, and operational
  confusion.
• 7. Relevance
• Definition Data is relevant when it is
  appropriate for the context in which it is used.
  Irrelevant data can clutter systems and distract
  from meaningful analysis.
• Example Collecting demographic data (like age
  and gender) may be relevant for a marketing
  campaign but not for inventory management.
• Importance Relevant data ensures that business
  operations, decision-making, and analytics are
  based on useful and purposeful information.

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Dimensions of Data Quality

• 8. Integrity
• Definition Data integrity refers to the accuracy
  and consistency of data over its lifecycle. It
  also includes the concept of maintaining
  relationships between different data points and
  ensuring data integrity across systems.
• Example In a database, foreign key relationships
  between tables should be maintained, and data
  updates should not break these relationships.
• Importance Data integrity is crucial for
  ensuring that data remains trustworthy,
  consistent, and usable across different systems
  and throughout its lifecycle.
• 9. Auditability
• Definition Data is auditable when its history is
  traceable, and changes to the data can be
  tracked, including who made changes and when they
  were made.
• Example If a customer's contact information is
  updated, the system should log the change with
  details about who made the update and when.
• Importance Auditability is important for
  ensuring transparency and accountability in the
  data management process, especially for
  regulatory compliance.

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Dimensions of Data Quality

• 10. Accessibility
• Definition Data is accessible when it can be
  easily retrieved, processed, and used by
  authorized users without unnecessary barriers.
• Example Data stored in a centralized data
  warehouse should be easily accessible to users
  with the appropriate permissions and roles.
• Importance Data accessibility ensures that teams
  can leverage data effectively for decision-making
  and operational tasks.
• 11. Conformity
• Definition Conformity ensures that data adheres
  to established standards or formats across the
  organization.
• Example If the address format is standardized
  across the organization (e.g., street, city,
  state, zip code), all data should follow this
  format.
• Importance Conforming to standards ensures
  consistency and makes it easier to integrate data
  from different sources.

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Dimensions of Data Quality

• Why are These Dimensions Important?
• These dimensions are essential because they
  represent the criteria that determine whether the
  data will support accurate analysis, operational
  tasks, and strategic decision-making. Ensuring
  high data quality across these dimensions will
• Increase Trust Organizations can trust data for
  decision-making and compliance if it meets
  high-quality standards.
• Reduce Costs Managing poor-quality data (e.g.,
  errors, duplicates) is expensive. High-quality
  data reduces the need for rework and minimizes
  the costs of resolving data issues.
• Enhance Business Efficiency Good-quality data
  supports efficient business processes by ensuring
  accurate insights, reducing errors, and
  streamlining workflows.

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Thank You !
References https//informatica.com
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