Informatica CDQ | Learn Data Quality Management Process Cycle

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Day4-Informatica Cloud Data Quality(CDQ)
Agenda

• Data Quality Management Process Cycle

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Data Quality Management Process Cycle

• The Data Quality Management (DQM) Process Cycle
  refers to a set of systematic steps used to
  ensure that data is accurate, consistent,
  complete, and usable. This process cycle is
  critical for organizations to maintain
  high-quality data and to leverage it for business
  decisions, analytics, and operational
  effectiveness. The DQM cycle typically involves a
  continuous, iterative process that spans the
  entire data lifecycle, ensuring that data remains
  high quality from collection through to analysis.
• Key Steps in the Data Quality Management Process
  Cycle
• 1. Data Profiling
• Definition Data profiling involves assessing the
  current state of data to identify anomalies,
  patterns, inconsistencies, and gaps in the data.
  This step helps establish a baseline for data
  quality.
• Activities
• Examine the structure, completeness, and format
  of data.
• Identify missing, duplicated, or invalid data.
• Assess the distribution of values (e.g., range,
  frequency).
• Goal To understand the quality of the data and
  identify areas that need improvement.

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Data Quality Management Process Cycle

• 2. Data Cleansing (or Data Cleaning)
• Definition Data cleansing is the process of
  identifying and correcting errors or
  inconsistencies in the data. It involves removing
  duplicate records, correcting inaccurate values,
  and filling in missing data.
• Activities
• Removing duplicates Identifying and removing
  identical records.
• Correcting inaccuracies Fixing incorrect data,
  such as misspelled names, invalid addresses, or
  incorrect phone numbers.
• Filling missing values Replacing missing data
  with valid values, such as using default values
  or imputation methods.
• Goal To improve the accuracy and consistency of
  the data.

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Data Quality Management Process Cycle

• 3. Data Standardization
• Definition Data standardization involves
  converting data into a consistent format to
  ensure uniformity across different data sources
  and systems.
• Activities
• Standardizing date formats (e.g., converting all
  dates to YYYY-MM-DD).
• Normalizing address formats (e.g., making sure
  that addresses include street, city, state, and
  postal code in the same order and format).
• Standardizing units of measurement (e.g.,
  ensuring that all monetary values are in the same
  currency).
• Goal To ensure that data follows standardized
  formats and rules across the organization, making
  it easier to process, compare, and integrate.

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Data Quality Management Process Cycle

• 4. Data Enrichment
• Definition Data enrichment is the process of
  adding valuable, external data to the existing
  dataset to enhance its quality and value.
• Activities
• Adding demographic, geographic, or firm graphic
  information to customer records.
• Integrating third-party data sources (e.g.,
  social media data, credit scores).
• Goal To improve the completeness and relevance
  of the data by incorporating additional external
  data that can provide context or insights.

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Data Quality Management Process Cycle

• 5. Data Validation
• Definition Data validation is the process of
  ensuring that data complies with predefined rules
  and business logic before being used for analysis
  or integration.
• Activities
• Ensuring that values match expected data types
  (e.g., numerical values in a price field, email
  format for email addresses).
• Validating data against predefined rules or
  business constraints (e.g., ensuring that a
  customer's age is within a valid range).
• Running consistency checks across different
  systems to ensure uniformity.
• Goal To prevent invalid or non-conforming data
  from being used, which could lead to errors in
  processing or analysis.

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Data Quality Management Process Cycle

• 6. Data Monitoring
• Definition Data monitoring involves continuously
  tracking data quality metrics to ensure that data
  quality remains high over time. This step
  includes real-time or periodic checks on data
  quality to spot issues before they escalate.
• Activities
• Continuously monitoring data for errors, missing
  values, or inconsistencies.
• Setting up automated data quality reports and
  alerts.
• Tracking data quality trends over time (e.g.,
  improvements or declines in data accuracy,
  completeness, etc.).
• Goal To maintain data quality on an ongoing
  basis and quickly identify and address issues.

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Data Quality Management Process Cycle

• 7. Data Governance
• Definition Data governance refers to the overall
  management of data, including policies,
  procedures, and standards that ensure data is
  properly handled, protected, and used
  responsibly.
• Activities
• Defining and enforcing data quality standards and
  policies.
• Assigning data stewards or owners responsible for
  specific data sets.
• Implementing security measures to protect data
  integrity and privacy.
• Goal To ensure that data quality management
  aligns with organizational goals and regulatory
  requirements, and that there are clear
  responsibilities for maintaining data quality.

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Data Quality Management Process Cycle

• 8. Data Integration
• Definition Data integration is the process of
  combining data from different sources into a
  unified view while ensuring that the integrated
  data maintains its quality.
• Activities
• Integrating data from various departments,
  databases, or external sources.
• Ensuring that the integrated data adheres to
  consistency, accuracy, and format rules.
• Merging datasets without creating duplicates or
  inconsistencies.
• Goal To create a consolidated dataset that
  combines data from multiple sources while
  maintaining high data quality.

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Data Quality Management Process Cycle

• 9. Data Quality Reporting and Analytics
• Definition Reporting and analytics involve
  generating reports on data quality, measuring key
  metrics, and analyzing trends in data quality
  over time.
• Activities
• Generating dashboards or reports that show key
  data quality metrics (e.g., accuracy,
  completeness, consistency).
• Analyzing the causes of data quality issues and
  trends.
• Communicating data quality insights to
  stakeholders for further action.
• Goal To track the effectiveness of the data
  quality program and identify areas for
  improvement.

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Data Quality Management Process Cycle

• Summary of the Data Quality Management Process
  Cycle
• Data Profiling Assess current data to identify
  issues.
• Data Cleansing Correct errors and remove
  duplicates.
• Data Standardization Convert data to a uniform
  format.
• Data Enrichment Add external data to enhance
  value.
• Data Validation Ensure data meets business
  rules.
• Data Monitoring Continuously track data quality
  over time.
• Data Governance Implement policies and
  procedures for managing data quality.
• Data Integration Combine data from multiple
  sources while maintaining quality.
• Data Quality Reporting and Analytics Track data
  quality metrics and improve processes.
• This cycle helps ensure that data is of high
  quality throughout its lifecycle, supporting
  better business decisions, operational processes,
  and customer experiences.

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Thank You !
References https//informatica.com
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