Six steps to data observability

Here we go. Implementing a robust data observability program with the following six steps is essential to ensuring your data is accurate, consistent and trustworthy:

1. Profile your data

Discover and classify all data sources
Identify sensitive data types and understand data structures

2. Define data policies and quality rules

Establish clear data policies and quality rules
Implement continuous testing and validation to maintain data integrity

3. Detect anomalies

Use machine learning to establish baselines and detect deviations
Identify potential data quality issues early

4. Monitor for impact

Correlate anomalies with business events to assess their impact
Prioritize remediation efforts based on severity

5. Notify key experts

Alert relevant stakeholders about data issues
Initiate and manage remediation processes effectively

6. Optimize continuously

Evolve data policies and practices based on insights from monitoring
Implement continuous improvement loops for ongoing optimization

These steps will guide you in building a reliable data infrastructure, ensuring high data quality
and supporting informed decision-making.

1. Profile your data

Profiling your data is the first crucial step in ensuring reliable data. By thoroughly understanding and classifying all your data sources, you can gain a comprehensive view of your data landscape. This foundational step helps identify potential data quality issues and sets the stage for effective data governance and management practices.

Objective

Understand and classify all data sources to gain a comprehensive view of your data landscape.

Activities

Data discovery and classification
Identifying data structure, types, and sensitivity

Checklist

Where is our data stored, and how is it structured?
What types of data do we have, and which are sensitive or regulated?
How complete and accurate is our data?
What tools and processes do we need to efficiently profile our data?

Practical exercise:
Data Profiling

Instructions

Select a data source (for example,
a customer database)
Use a data profiling tool to
analyze the data
Document the types of data, data
formats, and any sensitive data elements
Identify any data quality issues such
as missing values or inconsistencies

2. Define data policies and quality rules

Defining data policies and quality rules is critical to maintaining data integrity and ensuring reliable data across the organization. Establishing clear policies and rules helps prevent data quality issues and ensures that data handling aligns with regulatory requirements and organizational standards.

Objective

Establish and enforce data policies and business rules to maintain data integrity.

Activities

Continuous testing and validation mechanisms
Creating quality rules for data measurement and validation

Checklist

What are the critical data policies and quality rules needed for our data types?
How can we ensure continuous testing and validation of data quality?
What are the potential risks if these rules are violated?
Who is responsible for enforcing these policies, and how do we ensure accountability?

Practical exercise:
Defining Data Policies

Instructions

Choose a specific data set (for example, sales transactions)
Define policies for data accuracy, completeness, and consistency
Develop quality rules for data measurement and validation
Implement continuous monitoring
and testing mechanisms to ensure reliable data

3. Detect anomalies

Detecting anomalies is essential for identifying deviations from normal data behavior that could indicate data quality issues. Using machine learning to establish baselines and detect anomalies helps ensure that data remains accurate and reliable, thereby preventing potential problems before they escalate.

Objective

Use machine learning to identify deviations from normal behavior and detect anomalies.

Activities

Establishing baselines for normal data behavior
Using AI to automate anomaly detection

Checklist

What constitutes normal behavior for our data?
How do we automate our monitoring and detection processes?
How do we balance sensitivity and specificity to minimize false positives?
How often should we review and update our baseline metrics and monitoring processes?

Practical exercise:
Detect anomalies

Instructions

Select a data set with historical records (for example, financial transactions)
Use a data observability tool to establish a baseline for normal behavior
Run the anomaly detection process to identify outliers
Document and analyze any anomalies detected

4. Assess impact

Monitoring for impact involves correlating detected anomalies with business events to assess their potential effects. This step is crucial for prioritizing remediation efforts based on the severity of the impact and ensuring that data quality issues are addressed promptly to prevent business disruptions.

Objective

Correlate anomalies with changes and assess their impacts on business operations.

Activities

Root cause and impact analysis
Prioritization of remediation efforts

Checklist

How do we identify and document the root causes of anomalies?
How do we determine the downstream AI systems and analytics that are impacted?
How do we prioritize issues based on their potential impact?
What metrics should we use to assess the impact of data quality issues?

Practical exercise:
Impact assessment

Instructions

Take the anomalies detected in the previous exercise
Correlate these anomalies with business events or outcomes
Assess the potential impact of these anomalies on business operations
Prioritize remediation efforts based on the severity of impact

5. Notify key stakeholders

Effective communication is key to managing data quality issues. Notifying relevant stakeholders about data anomalies ensures that the right people are informed and can take immediate action. Establishing a clear notification and escalation process helps maintain data integrity and operational efficiency.

Objective

Alert relevant stakeholders and initiate remediation processes for data quality issues.

Activities

Creating and managing notifications
Setting up an escalation process for unresolved issues

Checklist

Who needs to be notified when a data quality issue is detected?
What information should be included in the alerts to make them actionable?
How can we ensure timely responses to critical alerts?
What is our process for escalating unresolved issues?

Practical exercise:
Notification and escalation

Instructions

Identify key stakeholders (for example, data stewards, data engineers and business analysts)
Develop a notification process for data quality issues
Create sample notification messages with necessary context
Set up an escalation process for unresolved issues

6. Optimize continuously

Continuous optimization is vital for maintaining high data quality standards. By regularly reviewing and refining data policies and practices, organizations can adapt to new challenges and opportunities. This ongoing improvement process ensures that data observability remains effective and aligned with evolving AI, analytics, and business needs.

Objective

Evolve data observability practices based on insights gained from monitoring and anomaly detection.

Activities

Continuous improvement
and feedback loops
Updating data observability practices based on new insights

Checklist

How do we gather and analyze feedback to improve our data policies and rules?
What process do we have in place for updating our data observability practices?
How do we stay informed about regulatory changes and ensure compliance?
How can we measure the effectiveness of our optimizations over time?

Practical exercise:
Data Profiling

Instructions

Review feedback from previous steps
Identify areas for improvement in data policies and rules
Update data observability practices based on new insights
Implement a continuous improvement loop to keep practices up-to-date