Splunk® Success Framework

Splunk Success Framework Handbook

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Data best practices overview

The data lifecycle is the center point of a Splunk implementation. Before you even engage Splunk software, best practices can help you manage and structure your data efficiently to optimize its searchability and value.

Best practices in the data functional area also help you design effective use cases that are tightly aligned to data, so Splunk can answer questions you know about and reveal answers to questions you didn't know you had.

Follow these best practices according to the standard, intermediate, or advanced goals you have set.

Activities Standard Intermediate Advanced

REQUEST DATA

Processes to bring requests for new use cases or data sources to your team's attention and to track and prioritize them among other requests.

Accept ad-hoc requests (for example, email, chat, voice).

Utilize the Request Workflow for Splunk app (see Ticketing/workflow system on Splunkbase)

Establish cost chargeback estimates for budget owner (see Showback best practices for Splunk implementations

DEFINE THE DATA

Guidelines to determine where to place line breaks and timestamps on incoming data and to identify the intended use case or value of the data.

Establish a process for defining baseline data that uses learned source types and little or no source data optimization

Establish a process for defining technical data that includes the following:

  • Defined source types (discover an existing add-on or create a new one)
  • Target index(es)
  • Data sensitivity searches, including personally identifiable information
  • End-user needs and outcomes
  • Field and value extractions
  • Generated knowledge objects, dashboards, and alerts

Everything outlined in intermediate

Establish a process for defining value-oriented data

  • Normalize fields with a common information model
  • Define tags
  • Develop a corporate information model
  • Consider license and storage impact
  • Identify business priority
  • Reuse data and knowledge objects for other use cases

IMPLEMENT THE USE CASE

Processes to carry out the Splunk configuration and to engage the requestor in implementation.

Deploy technical add-ons that support getting data in

Create a deployment server class that includes search, index, and forwarding and data collection tiers

Everything outlined in standard

Establish initial use case requirements

Apply naming conventions to knowledge objects (see Naming convention best practices)

Utilize the app builder for custom components

Everything outlined in intermediate

Create a lab environment for developing system and test automation

VALIDATE THE USE CASE

Processes to verify that the use case meets the requester's expectations and needs, and to enable the requester to communicate feedback.

Validate reactively: requester validates the work after it is completed

Validate proactively: requester validates the work at regular intervals during development

Validate demonstratively: requester validates the work in real-time from a hands-on demonstration of the use case (knowledge objects, data, and so on)

STEP 5: COMMUNICATE USE CASE CHANGES

Structures to inform the requester that the related work is completed and to enable others to learn about it.

Communicate with individuals that the work is completed

Everything outlined in standard

Announce to the community that the work is completed

Everything outlined in intermediate

Share final showback calculations with the requester

Track and communicate the business value of use cases to executive stakeholders

MAINTAIN AND RETIRE USE CASES

Processes to maintain Splunk knowledge objects and to remove them and their data sources when a use case is no longer needed.

Monitor the ongoing need of use cases individually

Stop indexing, generating, or forwarding the data when it is no longer needed

Everything outlined in standard

Establish a process for regularly evaluating the need for use cases

Establish a process for users to request that a use case be retired

Establish a process for retiring use cases that includes disabling knowledge objects, purging unnecessary data, and disabling server class(es) on the search, index, and forwarder and data collection tiers

Everything outlined in intermediate

Remove the use case from showback system

For more about data onboarding best practices, see Data onboarding best practices.

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This documentation applies to the following versions of Splunk® Success Framework: ssf


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