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Improving data management and governance


Data management and governance requires a systematic approach for success. To start, the approach should include policies for data onboarding, validation, normalization, classification, and enrichment. It must also define policies for data retention, lifecycle management, and stewardship. When you pair these policies with clearly-defined roles, responsibilities, processes and tools, your framework increases the chances that you'll achieve robust data reliability, compliance, and optimized utilization. The strategies provided in this pathway will help you accomplish these goals. You can work through them sequentially or in any order that suits your current level of progress.

This article is part of the Improve Performance outcome. For additional pathways to help you succeed with this outcome, click here to see the Improve Performance overview.

Following data onboarding best practices

Implementing standardized data onboarding procedures in the Splunk platform ensures that data is ingested and managed consistently.

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In this article, you will learn about the following data onboarding best practices:

  1. Data validation
  2. Great 8 configurations
  3. Data normalization
  4. Data enrichment
  5. Data transformation
  6. Versioning and auditing
  7. Data quality monitoring
  8. Documentation

Data validation

Data validation ensures that the data being ingested into the Splunk platform is accurate, reliable, and adheres to predefined standards. These qualities set the foundation for accurate data analysis, reporting, and decision-making. Here's a more detailed explanation of the key aspects of data validation:

  • Data Format Correctness: Validate that the data is in the expected format. This includes verifying that date and time formats, numerical values, and string formats are correct. For example, if your data requires a specific date format (such as YYYY-MM-DD), a validation script should flag any data entries that don't adhere to this format.
  • Completeness: Check for missing or incomplete data. Ensure that all required fields are present and populated with valid values. For instance, if certain fields are mandatory for analysis, the validation process should identify records where these fields are missing.
  • Data Integrity: Verify the integrity of the data by checking for inconsistencies or errors within the dataset. This could involve cross-referencing related fields to ensure they align logically. For example, if you have a dataset with customer orders, the validation process could verify that the total order amount matches the sum of individual line items.
  • Adherence to Standards: Ensure that the data conforms to predefined standards, both internal and industry-specific. This might involve checking against defined naming conventions, units of measurement, or any other guidelines that are relevant to your organization's data practices. For more guidance, see the Open Worldwide Application Security Project (OWASP) Logging Cheat Sheet.
  • Custom Validation Rules: Depending on the nature of your data, you might need to implement custom validation rules. These rules could involve complex business logic that checks for specific conditions or patterns within the data.
  • Data Enrichment and Transformation Validation: If you perform any data enrichment or transformation during the onboarding process, validate that these processes are working as intended. Ensure that the enriched or transformed data still aligns with your validation criteria.

To implement data validation effectively, you can use validation scripts or tools. These scripts can be programmed to automatically run checks on the incoming data and flag any issues that are identified. The flagged data can then be reviewed and corrected before being ingested into the Splunk platform. Automation of this process not only saves time but also reduces the risk of human errors in the validation process.

Great 8 configurations

The props.conf configuration file is a power configuration option for controlling how data is ingested, parsed, and transformed during the onboarding process. Among other things, props.conf is used for defining field extractions, identifying and capturing specific pieces of information from your raw data.

The Great 8 configurations below provide a standard for transforming raw data into well formatted, searchable events within the Splunk platform. They ensure that events are accurately separated, timestamps are correctly captured, so that fields can be properly extracted for analysis. By adhering to these configurations, you enhance data consistency, accessibility, and reliability, setting the stage for accurate insights and efficient analysis.

The following list only provides a brief explanation of each of these configurations. For complete, hands-on configuration guidance, see Configuring new source types.

  1. SHOULD_LINEMERGE = false (always false): This configuration tells the Splunk platform not to merge multiple lines of data into a single event. This is particularly useful for log files where each line represents a separate event, preventing accidental merging of unrelated lines. For additional context, reference the props.conf spec around line breaking.
  2. LINE_BREAKER = regular expression for event breaks: The LINE_BREAKER configuration specifies a regular expression pattern that indicates where one event ends and another begins. This is essential for parsing multi-line logs into individual events for proper indexing and analysis. For additional context, reference the props.conf spec around line breaking.
  3. TIME_PREFIX = regex of the text that leads up to the timestamp: When data contains timestamps, TIME_PREFIX helps the Splunk platform identify the portion of the data that precedes the actual timestamp. This helps the Splunk platform correctly locate and extract the timestamp for indexing and time-based analysis.
  4. MAX_TIMESTAMP_LOOKAHEAD = how many characters for the timestamp: This configuration sets the maximum number of characters that the Splunk platform will look ahead from the TIME_PREFIX to find the timestamp. It ensures that the Splunk platform doesn't search too far ahead, optimizing performance while accurately capturing timestamps.
  5. TIME_FORMAT = strptime format of the timestamp: TIME_FORMAT specifies the format of the timestamp within the data. The Splunk platform uses this information to correctly interpret and index the timestamp, making it usable for time-based searches and analyses.
  6. TRUNCATE = 999999 (always a high number): TRUNCATE configuration helps prevent overly long events from causing performance issues. It limits the maximum length of an event, ensuring that extremely long lines don't negatively impact the performance of the Splunk platform.
  7. EVENT_BREAKER_ENABLE = true: This configuration indicates whether event breaking should be enabled. Setting it to true ensures that event breaking based on LINE_BREAKER is activated.
  8. EVENT_BREAKER = regular expression for event breaks: EVENT_BREAKER allows you to define an additional regular expression pattern for event breaking. This can be useful for scenarios where more complex event breaking is required.

Data normalization

Data normalization is a process of transforming data from various sources into a common and standardized format or structure. This is particularly important in the Splunk platform, as normalized data allows for consistent analysis, reporting, and integration across different data sources.

Data normalization process

  • Consistent Format: Data can be received from diverse sources, each with its own format. During normalization, the data is transformed into a uniform format. For example, if different data sources use different date formats (MM/DD/YYYY and DD-MM-YYYY), normalization would involve converting them all to a standardized format (YYYY-MM-DD).
  • Standardized Units: Normalize units of measurement to ensure consistency. This is particularly important when dealing with numerical data, such as converting measurements from metric to imperial units or vice versa.
  • Field Naming Conventions: Ensure consistent field naming across different data sources. For example, if one source uses "IP_Address" and another uses "Source_IP," normalization involves mapping these variations to a single, standardized field name.
  • Data Enrichment: As part of normalization, you might enrich data by adding contextual information.

Importance of data normalization in compliance with Splunk's Common Information Model (CIM)

In the context of CIM compliance, data normalization becomes even more crucial to ensure interoperability and consistency across different security-related data sources. the CIM is a standardized framework for organizing data into a common format. It enables interoperability between different security solutions by providing a consistent model for data. When normalizing data for CIM compliance, you're aligning your data with the CIM's predefined data structures, which allows for seamless integration and correlation of events across various sources.

For example, if you're collecting logs from different security devices like firewalls, intrusion detection systems, and antivirus solutions, each might have its own unique data structure. By normalizing the data to CIM's standard, you're ensuring that these different sources can be easily correlated and analyzed together.

In the context of the Splunk platform, data normalization for CIM compliance involves mapping your data fields to the CIM's standardized fields. This mapping ensures that your data fits into the CIM data model and can be effectively used with CIM-compliant apps and searches. CIM compliance enhances your ability to perform security analytics, threat detection, and incident response by providing a unified view of security-related data.

Data enrichment

Data enrichment is the process of enhancing existing data with additional context, information, or attributes to make it more valuable and meaningful for analysis and decision-making. In the context of the Splunk platform and data management, data enrichment plays a large role in improving the quality, relevance, and usability of the data you collect and analyze.

Context-based enrichment

  • Geolocation Data: Adding geographical context to data can provide insights into the geographic origin of events. For example, enriching IP addresses with geolocation information can help you understand where certain activities are occurring.
  • External Data Sources: Enriching data with information from external sources can provide a broader context. For instance, you might enrich user data with social media profiles or industry-related data to gain a better understanding of user behavior.
  • Threat Intelligence Feeds: Enriching security-related data with threat intelligence feeds can help identify known malicious IPs, domains, or URLs, aiding in the early detection of potential security threats.

Business-specific logic enrichment

  • Derived Fields: Enrichment can involve creating new fields or attributes based on existing data. For example, you might create a "Customer Segment" field based on customer purchase history and demographics.
  • Calculated Fields: Enrichment can also include performing calculations on existing data to generate new insights. For instance, calculating the average transaction value from historical sales data can provide valuable business insights.

Benefits of enrichment

  • Improved Analysis: Enriched data provides more context and depth, enabling more accurate and insightful analysis. This leads to better decision-making and actionable insights.
  • Enhanced Correlation: Enrichment helps correlate data from different sources by adding common attributes. This is especially important in security and operational contexts where identifying relationships between events is crucial.
  • Better Visualization: Enriched data can lead to more meaningful visualizations. For example, visualizing sales data enriched with customer demographics can reveal patterns and trends.
  • Advanced Analytics: Enriched data supports advanced analytics, machine learning, and predictive modeling by providing a more comprehensive view of the data.

Methods of enrichment

  • Lookup Tables: You can use lookup tables to enrich IP addresses with geolocation data.
  • Scripted Inputs: You can use scripted inputs to fetch external data from API.
  • Custom Search Commands: You can develop search commands to perform specific enrichments on data during analysis.

Data transformation

Data transformation can be a crucial step in the data management process, especially when dealing with data collected from diverse sources with varying structures and formats. In the context of the Splunk platform and data management, data transformation involves reshaping and reorganizing data to make it more suitable for analysis, reporting, and other purposes.

Key aspects of data transformation

  • Aggregation: Aggregating data involves combining multiple data records into a summary or aggregated view. This can be done to calculate totals, averages, counts, or other aggregated metrics. For example, transforming daily sales data into monthly or quarterly aggregates can provide a higher-level overview.
  • Field Merging: Sometimes, data from different sources might have related information stored in separate fields. Data transformation might involve merging these fields to consolidate related data. For instance, merging "First Name" and "Last Name" fields into a single "Full Name" field.
  • Splitting Data: In some cases, data might need to be split into different dimensions for analysis. For example, transforming a date field into separate fields for year, month, and day can allow for time-based analysis.
  • Normalization: Data normalization involves standardizing data values. This is especially important when dealing with data from multiple sources that use different units of measurement. For example, one system might use "usr_name" while another uses "username" to indicate a user's name; normalization would involve mapping these differing fields to a common field name, such as "user_name," to facilitate unified searches and analytics.

Benefits of data transformation

  • Improved Analysis: Transformed data is more structured and aligned with analysis requirements, enabling more accurate and insightful results.
  • Enhanced Compatibility: Transformation ensures that data from diverse sources can be integrated and analyzed together, even if they have different structures.
  • Efficient Storage: Aggregating and summarizing data can lead to reduced data volumes, making storage more efficient.
  • Simplified Reporting: Transformed data is often more suitable for creating reports and visualizations that highlight key insights.

Versioning and auditing

Implementing version control for your data onboarding scripts and configurations is a crucial practice in data management and governance. Here's why versioning and auditing are important and how they can benefit your data onboarding processes:

Version control

Version control, often managed through tools like Git, is a systematic way of tracking changes to your scripts, configurations, and any other code-related assets. In the context of data onboarding, version control is important for several reasons.

  • Change Tracking: Version control allows you to keep a historical record of every change made to your data onboarding scripts. This includes modifications, additions, and deletions.
  • Collaboration: If multiple team members are involved in managing data onboarding, version control enables collaborative work. Team members can work on separate branches, making changes without directly impacting the main codebase until they are ready.
  • Error Tracking: In case an issue arises after a change is implemented, version control helps you identify the exact change that might have caused the problem. This speeds up the process of debugging and resolving issues.
  • Reversion: If a change leads to unexpected results or issues, version control allows you to revert to a previous working version of the scripts. This is particularly helpful in quickly rolling back changes to maintain data integrity.


Here's why auditing matters:

  • Compliance: In regulated industries, auditing helps ensure that your data onboarding processes adhere to regulatory requirements. Having a record of changes and who made them is crucial for demonstrating compliance.
  • Accountability: Auditing adds a layer of accountability. Knowing that changes are being tracked and reviewed can encourage responsible practices among team members.
  • Root Cause Analysis: When an issue arises, auditing can help pinpoint the root cause. It allows you to trace back when and by whom a specific change was made, aiding in troubleshooting.
  • Process Improvement: By analyzing the history of changes and their impact, you can identify areas for process improvement and optimize your data onboarding procedures over time.

Data quality monitoring

Data quality monitoring involves consistently assessing the quality of the data you have onboarded into your system to ensure that it meets the desired standards.

Importance of data quality monitoring

Maintaining high-quality data is essential for making informed decisions, ensuring accurate analysis, and deriving meaningful insights. Poor-quality data can lead to erroneous conclusions, misguided strategies, and operational inefficiencies. Data quality monitoring helps to:

  • Detect Inaccuracies: Data quality issues can range from missing values to inconsistencies and errors. Monitoring allows you to catch these issues before they affect downstream processes or negatively impact your analyses and decisions.
  • Operational Efficiency: Addressing data quality issues promptly reduces the time and effort required to correct larger-scale problems later.
  • Maintain Trust: Accurate and consistent data builds trust among users, stakeholders, and decision-makers who rely on the information provided by the data.
  • Enhance Decision-Making: Reliable data leads to more accurate insights, enabling better decision-making and informed strategies.
  • Compliance: In regulated industries, data quality is often a compliance requirement. Monitoring ensures that your data meets these standards.

Data quality monitoring process

  1. Define Metrics: Establish clear metrics and criteria that define what constitutes high-quality data for your organization. This could include accuracy, completeness, consistency, and timeliness.
  2. Set Up Monitoring: Implement tools, scripts, or solutions that regularly assess the data against the defined metrics. This could involve automated checks or manual reviews.
  3. Real-Time Alerts: Configure real-time alerts that notify you when data quality issues are detected. These alerts could be sent via email, dashboards, or integration with incident management systems.
  4. Anomaly Detection: Use anomaly detection techniques to identify data points that deviate significantly from the expected patterns. This can help you catch subtle issues that might not be immediately obvious.
  5. Root Cause Analysis: When issues are flagged, conduct root cause analysis to understand the underlying reasons for the data quality problems.
  6. Immediate Remediation: After issues are identified and their root causes determined, take immediate action to rectify the data. This could involve data cleansing, normalization, or re-onboarding.
  7. Continuous Improvement: Regularly review the data quality monitoring process itself. Are the metrics still relevant? Are new issues arising that need to be addressed?

Data quality monitoring is a proactive practice that ensures the integrity of your data. By establishing metrics, setting up monitoring processes, and promptly addressing issues, you can maintain accurate, consistent, and reliable data for your organization's decision-making and operational needs.


Documenting the data onboarding process ensures transparency, consistency, and effective management of the entire data lifecycle. This documentation acts as a comprehensive guide that captures the various aspects of the data onboarding process, making it easier to understand, replicate, and improve over time.

Importance of documentation

  • Consistency: Documenting the data onboarding process ensures that the same steps are followed consistently each time new data is brought into the system. This minimizes errors and discrepancies that can arise from variations in execution.
  • Knowledge Transfer: When team members change or new members join, comprehensive documentation allows for a smooth transfer of knowledge, helping team members to quickly understand the process and follow best practices.
  • Future Reference: Documentation serves as a reference for the future. If issues arise or improvements are needed, the documented process provides insights into how the onboarding was originally set up.
  • Enhanced Collaboration: Documentation fosters collaboration among team members as they can easily share insights, suggest improvements, and work together more effectively.
  • Continuous Improvement: Documented processes can be reviewed periodically, leading to refinements and enhancements. These improvements contribute to the overall efficiency of data onboarding.
  • Reduced Dependency: Relying solely on individual expertise can create dependency on specific team members. Documentation reduces this dependency and empowers the entire team to execute the process effectively.
  • Risk Mitigation: In case of issues or discrepancies, documentation serves as a valuable reference point to identify the root cause and find effective solutions.

Key elements of documentation

  • Validation Rules: Clearly outline the rules and criteria used to validate the data during onboarding. This includes defining acceptable data formats, ranges, and any specific conditions.
  • Transformation Logic: Document how the data is transformed from its source format to the desired target format. Include details about calculations, aggregations, and any data modifications.
  • Enrichment Sources: Specify where and how additional context or information is added to the data. This could involve referencing external data sources, APIs, or business-specific logic.
  • Workflow Sequence: Detail the sequence of steps in the onboarding process. This includes the order in which validation, transformation, and enrichment occur.
  • Dependencies: If the onboarding process relies on external systems, tools, or scripts, document these dependencies to ensure that everyone is aware of the interconnected components.
  • Parameters and Configurations: Document the parameters, settings, and configurations used during data onboarding. This ensures that these settings can be accurately replicated or adjusted as needed.
  • Error Handling: Describe the strategies for handling errors or exceptions that might occur during the onboarding process. This could involve error logging, notifications, or automated retries.

By implementing these standardized data onboarding procedures, you establish a foundation of reliable and consistent data in your Splunk platform environment. This, in turn, supports accurate analysis, reporting, and decision-making, while adhering to data management and governance best practices.

Establishing data retention policies

Data retention, at its core, refers to the policies and practices that determine how long data is stored and retained within the Splunk platform. An integral part of data management is that only relevant data is stored, while outdated or unnecessary data is systematically purged. However, retention isn't just about storage. It's about striking the right balance between resource utilization, data availability for analysis, and compliance with internal and external regulations.

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This section covers the following data retention topics:

  1. Defining the lifecycle of data in the Splunk platform
  2. Identifying stakeholders for data retention discussions
  3. Defining data retention policies
  4. Implementing data retention in the Splunk platform
  5. Securely deleting data
  6. Monitoring and auditing data retention practices

Defining the lifecycle of data in the Splunk platform

  • Ingestion: The journey of data in the Splunk platform begins with ingestion. As data streams in, the Splunk platform indexes it, making the raw data searchable and analyzable. During this phase, you must ensure data is categorized correctly and the right meta-information is associated with it, as this impacts retention decisions later on.
  • Storage and Analysis: After it is ingested, data resides in the indexes and is available for analysis. In the indexes, data can be subjected to numerous operations: searches, reports, alerts, and more. Data retention policies during this phase revolve around optimizing storage, ensuring that frequently accessed data is readily available, while less critical data might be moved to slower, more cost-effective storage.
  • Aging and Rolling: As data ages and passes the threshold defined by the retention policies, it transitions between several buckets within the Splunk platform. From "hot" to "warm," then "cold," and possibly even to "frozen," each stage represents a different storage and accessibility status. This systematic aging and rolling process, driven by the Splunk platform's configurations, ensures efficient storage use.
  • Deletion or Archival: The final stage in the data lifecycle is its removal from active storage. Based on the retention policy, after data surpasses its defined lifespan, the Splunk platform either deletes it or moves it to a "frozen" state, which could mean archival for long-term storage or complete removal.

This lifecycle underscores the dynamic nature of data within the Splunk platform. By understanding each stage, from the point of ingestion to eventual deletion or archival, you can better tailor your data retention policies, ensuring they are both compliant with regulations and efficient in terms of storage resource management.

Identifying stakeholders for data retention discussions

Misalignment among stakeholders can result in inefficiencies, increased costs, or even legal repercussions. Here's why identifying stakeholders from multiple business units is essential:

  • Consistency: Ensuring that retention policies are uniform across different datasets and departments avoids confusion and potential errors.
  • Compliance: With the combined expertise of IT and legal teams, your organization can ensure its policies are both technically feasible and legally compliant.
  • Optimization: By understanding the specific data needs of various business units, IT can optimize storage, ensuring critical data is readily available while archiving or deleting outdated information.

Key stakeholders typically include:

  • IT Teams: Responsible for the technical implementation and maintenance of the Splunk platform, they possess firsthand knowledge of the platform's capabilities, limitations, and current configurations.
  • Legal and Compliance Units: They ensure that data retention practices adhere to regulatory requirements and organizational legal obligations. Their input is crucial in setting minimum and maximum retention periods.
  • Business Units: Different departments might have varying needs concerning data accessibility and retention. For instance, the marketing team's requirements might differ considerably from those of the finance department.

By involving the right stakeholders and fostering open communication, organizations can craft data retention policies in the Splunk platform that are both effective and reflective of their unique operational and compliance needs.

Defining data retention policies

Data retention policies provide a structured approach to managing the duration for which data is stored, ensuring it's available when needed but removed when no longer necessary. Here's a detailed look into the elements to consider when defining these policies.

  • Regulatory and Compliance Requirements: Different industries and regions have distinct laws and regulations dictating the minimum or maximum periods for retaining certain data types. For instance, financial transaction data might have a different retention period than consumer interaction data due to financial regulations. Regularly review industry-specific guidelines and regional data protection laws to ensure that your policies remain compliant.
  • Business Needs and Operational Requirements: Not all data has the same value or utility over time. While some datasets might be crucial for long-term trend analysis, others might lose their relevance quickly. Collaborate with various departments to learn how often they access older data and the implications of not having that data available.
  • Storage Constraints and Costs: While retaining data can be beneficial, be mindful of the associated costs, both in terms of physical storage and potential performance implications. Analyze the growth rate of your data, project future storage needs, and factor in costs to determine an economically feasible retention period.

Workshops and sessions to gather requirements

To capture the diverse needs and considerations for data retention, conduct the following requirements gathering sessions:

  • Initial Workshops: Begin with broad sessions, introducing the goals of the data retention initiative and gathering preliminary insights from different teams.
  • Focused Discussions: Conduct more in-depth discussions with individual departments or teams, diving into their specific data needs, access frequencies, and retention requirements.
  • Feedback Loops: After drafting an initial retention policy, share it with stakeholders for feedback. This iterative process helps refine the policy, ensuring it's both comprehensive and practical.

Documenting policies clearly and comprehensively

After you've settled on a data retention policy, document it in a clear and accessible manner. This documentation should:

  • Define the different data categories.
  • Specify the retention period for each category.
  • Detail the procedures for data deletion or archival.
  • Highlight any exceptions or special cases.

Regularly review and update this documentation, especially when there are changes in business operations, regulatory environments, or technological infrastructures.

Implementing data retention in the Splunk platform

Effective data management within the Splunk platform revolves around understanding the lifecycle of data and configuring the platform to respect your established retention policies. To accomplish this, you must understand the various data bucket types in the Splunk platform and the tools available to tailor retention settings.

Configuring index settings for retention

The Splunk platform offers granular controls to define the behavior of each bucket type:

Utilize the indexes.conf file to set parameters like maxHotSpanSecs for hot buckets or frozenTimePeriodInSecs to determine the age at which data transitions to the frozen state.

The maxHotSpanSecs is an advanced setting that should be set with care and understanding of the characteristics of your data.

Remember to consider both the size and age of data when defining retention settings. Depending on your policies, either parameter might trigger a transition between bucket states.

Example Scenario

An organization has identified a specific use-case that requires a custom Splunk platform index configuration. The index, named myindex, will be receiving a data volume of approximately 5GB/day. Their retention requirements for this index are specific:

  • Hot Buckets: Data should only stay in the hot bucket for a maximum duration of 24 hours before transitioning to the warm bucket.
  • Warm Buckets: After leaving the hot bucket, data should reside in the warm bucket for 30 days.
  • Cold Buckets: Post the warm bucket phase, the data should transition into cold storage, where it will remain for another 30 days.
  • Frozen Data: After its stint in cold storage, instead of deleting the data, we intend to archive it for possible future needs.

To achieve this, your organization needs to customize the the Splunk platform indexes.conf file for myindex.

homePath = $Splunk_DB/myindex/db
coldPath = $Splunk_DB/myindex/colddb
thawedPath = $Splunk_DB/myindex/thaweddb
maxDataSize = 5000
frozenTimePeriodInSecs = 5184000  # 60 days (30 days in warm + 30 days in cold)
maxHotSpanSecs = 86400  # 24 hours
maxWarmDBCount = 30  # Since we have 5GB/day and want to retain for 30 days in warm
# Additional settings might be required depending on where you want to archive the data, such as:
coldToFrozenDir = /path/to/my/archive/location
coldToFrozenScript = /path/to/my/archive/script

Securely deleting data

Deleting data securely isn't just about making space or managing storage; it's about ensuring that sensitive information doesn't fall into the wrong hands or get misused post-deletion.

The imperative of secure data deletion

Every piece of data that flows into the Splunk platform might contain information of varying sensitivity levels. Whether it's personally identifiable information (PII), business intellectual property, or operational insights, after the decision to delete it is made, its removal should be thorough and irreversible. Secure data deletion is:

  • Mandatory for Compliance: Many regulatory standards, like GDPR, HIPAA, and others, mandate secure data erasure as part of their compliance requirements.
  • Critical for Privacy: In the case of data breaches, securely deleted data offers no value to malicious actors, ensuring further data safety.
  • Essential for Trust: Customers, stakeholders, and partners are more likely to trust organizations that take every facet of data security, including its deletion, seriously.

Data deletion in the Splunk platform

The Splunk platform offers a set of tools and configurations that assist in data management, including its deletion.

  • Bucket Deletion: Data in the Splunk platform resides in buckets based on its age and status (hot, warm, cold, etc.). Deleting buckets is one method to remove data, but this doesn't ensure secure erasure.
  • Expire and Delete: Configurations in indexes.conf allow for automatic data aging out and deletion after a set period, defined by the frozenTimePeriodInSecs setting.
  • Manual Deletion: The Splunk platform provides SPL commands like delete to remove indexed data. But remember, using the delete command doesn't remove the data physically from disk but marks it non-searchable.

Ensuring secure erasure

While the Splunk platform provides tools for data deletion, ensuring the data is irrecoverable post-deletion requires additional steps:

  • Overwriting Data: Secure deletion tools work by overwriting the storage space occupied by the data multiple times with random patterns, ensuring that data recovery tools cannot retrieve the original data.
  • Physical Destruction: For highly sensitive data, after secure deletion, organizations sometimes opt for physical destruction of storage devices.
  • Encryption: Data encrypted at rest ensures that even if someone could retrieve deleted data, they wouldn't decipher its content without the encryption key.

By understanding the tools and techniques available within the Splunk platform, coupled with industry best practices, organizations can confidently manage and, when necessary, erase data securely.

Monitoring and auditing data retention practices

Properly setting up data retention practices in the Splunk platform is the first step in a broader journey. To ensure the ongoing efficacy and compliance of these practices, active monitoring and periodic auditing is a must.

Alerts and reports on data age

To actively manage and oversee data retention, you can:

  • Set Up Alerts: Create specific alerts in the Splunk platform when data in an index reaches its maximum age limit or is about to. This proactive measure allows administrators to take timely action, be it data backup, transfer, or deletion.
  • Generate Age Reports: Create periodic reports that offer insights into the age of data across various indexes. These reports can help identify anomalies or deviations from the retention policy.

Periodic review of retention settings

Data retention is not a static operation, and as organizational needs evolve, so too should retention practices. You should employ the following review processes:

  • Scheduled Reviews: Organize regular reviews of your Splunk platform's indexes.conf and other retention-related configurations to ensure they align with the current policy and operational needs.
  • Change Management: Any changes to retention settings should be documented, with reasons for the change, the individuals involved, and the date of the change. This aids in audit trails and provides clarity for future reviews.

The act of retaining data in the Splunk platform should be as deliberate and standardized as the act of deleting it. With the tools and strategies at hand, administrators can ensure that data retention practices are transparent, traceable, and in alignment with both policy directives and operational necessities.

Helpful resources

Splunk OnDemand Services: Use these credit-based services for direct access to Splunk technical consultants with a variety of technical services from a pre-defined catalog. Most customers have OnDemand Services per their license support plan. Engage the ODS team at if you require assistance.

Data governance framework

Data governance refers to the overall management of data's availability, usability, integrity, and security in enterprises. A governance framework provides clarity in data-related roles and responsibilities, ensures adherence to organizational and regulatory standards, and mitigates risks associated with data anomalies or breaches.

The Splunk platform isn't just about parsing and visualizing large datasets. It's also a tool that can assist you in fortifying your data governance structures. From its data collection and indexing mechanisms to its security and compliance features, the Splunk platform offers functionalities that can significantly streamline the establishment and operation of a data governance framework.

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This section outlines the following steps in creating a data governance framework:

  1. Components of data governance
  2. Roles and responsibilities in data governance
  3. Processes in data governance

Components of data governance

How data is managed can define an organization's efficiency, regulatory compliance, and even competitive advantage. Without proper governance, organizations risk data inconsistencies, breaches, and the resultant operational inefficiencies and potential legal liabilities. The following components must be a part of your governance framework:

Key components of data governance

  • Data Stewardship: At the heart of governance lies stewardship, the responsibility for data quality and the appropriate use of data. Data stewards usually bridge the gap between business and IT, ensuring data policies are enacted and adhered to.
  • Data Quality: Ensuring data is accurate, timely, and relevant involves processes like validation, cleansing, and reconciliation.
  • Data Security & Privacy: Protecting sensitive data and ensuring only authorized personnel can access specific data sets is important, especially in an era of heightened data breaches.
  • Data Lifecycle Management: This encompasses the stages of data from creation to deletion. Proper governance ensures that data is archived, retained, and purged in alignment with business needs and regulatory requirements.
  • Metadata Management: This involves documenting data, its interrelationships, source, and transformations. Metadata provides context, making data more useful and easier to manage.
  • Compliance & Auditing: Given the myriad of regulations globally, a robust governance framework ensures that data usage, storage, and processing are in line with laws such as GDPR, CCPA, etc. It also includes keeping track of who accessed what data and when.

Roles and responsibilities in data governance

Data owners

Data owners, often senior-level executives or managers, are accountable for the data within their respective domains. They have the final say on how data should be used, who has access to it, and its overall quality. Their key responsibilities often include defining the critical data elements, setting data policies, and ensuring data security within their purview.

The Splunk platform can offer data owners a comprehensive view of their data landscape. The extensive logging and visualization capabilities allow data owners to monitor data access, usage patterns, and potential security threats in real-time. Alerting mechanisms in the Splunk platform help data owners proactively address anomalies or unauthorized access, ensuring their data remains secure and compliant.

Data stewards

Data stewards are the guardians of data quality. Positioned at the intersection of business and IT, they work closely with data owners to enforce data policies, resolve data quality issues, and liaise with IT for technical fixes. Their responsibilities encompass data definition, quality checks, and ensuring that data processes align with the business's needs and goals.

The Splunk platform can be invaluable for data stewards. With its data indexing and searching capabilities, the Splunk platform helps data stewards quickly pinpoint data quality issues. Custom Splunk dashboards can offer visual insights into data health, while its reporting features allows stewards to generate regular data quality assessments, ensuring consistency and compliance.

Data consumers

Data consumers are the end-users of data, spanning a range from analysts and business users to external partners. They rely on data for their tasks, making it essential that they receive accurate, timely, and relevant information. Within the governance framework, their primary responsibility is to utilize data responsibly, adhering to the set guidelines and ensuring data confidentiality.

Data consumers stand at the receiving end of the data governance pipeline. Their feedback and user experience are crucial in refining data processes and ensuring the governance framework remains robust and effective. The Splunk platform can assist in ensuring that data consumers can easily retrieve and analyze the data they need, all while remaining within the boundaries set by data owners and stewards.

Processes in data governance

Data classification

Data classification is the process of organizing data into categories based on its type, sensitivity, and importance to your organization. This aids in determining security measures, access controls, and storage requirements.

The Splunk platform supports dynamic data tagging and field extractions, making it an ideal tool to assist in data classification. By using search and reporting capabilities, in addition to index time capabilities, organizations can automatically categorize data based on predefined criteria, ensuring consistency and scalability in the classification process.

Data quality management

Data quality management (DQM) involves the processes and technologies to ensure data's accuracy, completeness, reliability, and timeliness. Proper DQM processes prevent data errors, inconsistencies, and redundancies.

The Splunk platform provides functionalities to assist with data validation, anomaly detection, and deduplication. By setting up custom alerts and validation rules within the Splunk platform, organizations can proactively identify and rectify data quality issues, ensuring data integrity throughout its lifecycle.

Data access and security

Data access and security pertain to who can access data, how it's accessed, and the protective measures to prevent unauthorized or malicious access.

Role-Based Access Control (RBAC) in the Splunk platform allows administrators to define who can access specific datasets and the actions they can perform on them. Coupled with its encryption, logging, and real-time monitoring features, the Splunk platform offers a comprehensive suite to ensure data remains secure, both in transit and at rest.

Data lifecycle management

Data Lifecycle Management (DLM) represents the stages data goes through from creation to deletion. This includes data creation, processing, archiving, and eventual purging or deletion.

The Splunk platform supports DLM through its data retention policies, tiered storage, and archiving functionalities. Splunk administrators can set up policies that determine how long data resides in indexes before being archived or deleted. Organizations can optimize storage costs, ensure compliance with data retention laws, and guarantee data availability when required.

Best practices

Regular auditing

Regular auditing involves periodic checks to ensure data governance policies are adhered to and to detect any anomalies or unauthorized activities. The following capabilities of the Splunk platform help with this process:

  • Auditing capabilities: Allow organizations to maintain a watchful eye over their data.
  • Logging capabilities: Organizations can capture all data interactions, providing an immutable record of who accessed what data and when. These logs can be analyzed for patterns, helping to spot anomalies or potential breaches.
  • Reporting capabilities: Scheduled audits can further help in ensuring compliance and maintaining data integrity.

Training and awareness

Training and awareness initiatives ensure that all team members are familiar with the functionalities, tools, and best practices associated with the Splunk platform. Teams can use continuous training sessions, webinars, and workshops to keep on evolving features of the Splunk platform and how they can be leveraged for effective data governance. Ensuring that every team member understands and utilizes the capabilities available in the Splunk platform fully can significantly elevate the efficiency and effectiveness of data governance.

Helpful resources

Splunk OnDemand Services: Use these credit-based services for direct access to Splunk technical consultants with a variety of technical services from a pre-defined catalog. Most customers have OnDemand Services per their license support plan. Engage the ODS team at if you require assistance.