The six steps to
data reliability

Step 1: Profile the data

Start by discovering all your data sources and classifying them, with a particular focus on identifying sensitive data types. Profiling your data helps in understanding the nature, structure, and quality of your data assets. This foundational step is crucial for setting the stage for effective data governance and observability, especially as you increase your AI investments and the regulatory landscape evolves.

Common obstacles

Data silos: Data spread across different departments or systems can be challenging to consolidate
Data volume: Large volumes of data can make profiling and classification time-consuming and resource-intensive
Data complexity: Different data formats and structures add to the complexity of profiling

Four questions to ask

Where is our data stored, and how can we access it?
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?

Step 2: Define data policies and business rules

Based on the types of data you have, define policies and business rules that guide how data should be handled. Implement continuous testing and validation mechanisms to ensure data pipelines do not contain data that violates these policies. This proactive approach helps your organization maintain data integrity, while AI becomes more integral to decision-making.

Common obstacles

Lack of standardization: Inconsistent data policies across the organization
Regulatory complexity: Navigating the myriad of evolving data regulations
Resistance to change: Difficulty in getting buy-in from all stakeholders

Four questions to ask

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

Step 3: Detect anomalies

Establish a baseline for normal behavior within your data. Use machine learning algorithms to monitor data continuously and detect deviations from this baseline. Anomaly detection is vital for identifying potential data quality issues before they escalate into bigger problems, which is increasingly important as AI applications depend on high-quality data to function correctly and comply with regulations.

Common obstacles

Defining normal behavior: Establishing accurate baselines can be complex
Algorithm selection: Choosing the right ML algorithms for effective anomaly detection
False positives: High rates of false positives can overwhelm teams and reduce trust in the system

Four questions to ask

What constitutes normal behavior for our data?
Which ML algorithms are best suited for our anomaly detection needs?
How do we balance sensitivity and specificity to minimize false positives?
How often should we review and update our baseline metrics?

Step 4: Monitor for impact

Once anomalies are detected, correlate these deviations with unintended changes and other events to identify the root cause and assess the potential impact. Understanding the impact of data quality issues on your operations helps in prioritizing remediation efforts effectively, ensuring your AI models and analytics applications continue to perform accurately and reliably.

Common obstacles

Correlation complexity: Difficulties in accurately correlating anomalies with their impacts
Resource allocation: Limited resources to investigate and resolve detected issues
Impact assessment: Challenges in quantifying the business impact of data quality issues

Four questions to ask

How do we identify and document the root causes of anomalies?
What tools can help us correlate anomalies with their impacts efficiently?
How do we prioritize issues based on their potential impact?
What metrics should we use to assess the impact of data quality issues?

Workbook

Stop bad data before it stops you: How to ensure reliable data for AI and analytics success

Download workbook

Step 5: Notify key experts

Provide contextual alerts to relevant stakeholders—including data engineers, analysts, business leaders and compliance officers—to initiate remediation processes. Timely and informed notifications ensure that the right people can take swift action to resolve data quality issues, maintaining both operational efficiency and regulatory compliance.

Common obstacles

Alert fatigue: Too many alerts can lead to important notifications being ignored
Contextual information: Ensuring alerts contain enough context for quick action
Stakeholder engagement: Ensuring all relevant stakeholders are engaged and responsive

Four questions to ask

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?

Step 6: Optimize continuously

Evolve your data policies, rules, and reports based on insights gained from monitoring and anomaly detection. Continuous optimization ensures that your data governance practices remain effective and aligned with evolving data quality goals, supporting the scalability of AI initiatives and adherence to changing regulatory standards.

Common obstacles

Continuous improvement: Keeping up with the need for ongoing adjustments and optimizations
Feedback loop: Establishing effective feedback mechanisms to inform improvements
Regulatory changes: Adapting to new and changing regulations in a timely manner

Four questions to ask

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 governance practices?
How do we stay informed about regulatory changes and ensure compliance?
How can we measure the effectiveness of our optimizations over time?

The path to data reliability is here

By following these steps, your organization can leverage machine learning to enhance data observability, ensuring high data quality and compliance. This structured approach leads to better decision-making, reduced costs associated with poor data quality, and improved overall data management.