Splunk Build-a-thon!

Category

Developing

(1 point)

Meets Expectations 

(3 points)

Exceeds Expectations

(5 points)

Fit

The add-on has minimal alignment with the hackathon goals.

The add-on has moderate alignment, addressing a relevant integration challenge.

The add-on strongly aligns with the hackathon’s goals, providing a well-integrated solution that enhances Splunk’s capabilities.

Functionality / Usability

The add-on demonstrates minimal functionality (20-40%). Key components are incomplete, unreliable, or difficult to use.

The add-on is functional (40-80%) and achieves most intended goals with some usability gaps. Configuration and interaction are mostly intuitive.

The add-on is fully functional (80-100%) and provides a seamless, intuitive user experience, making integration easy for users.

Innovation

The add-on provides a basic integration without new or creative solutions.

The add-on solves a relevant problem with a thoughtful integration approach.

The add-on introduces an innovative way to connect external data sources or optimize Splunk workflows, setting a new standard for integrations.

Business Value

The add-on has minimal practical business relevance or feasibility.

The add-on demonstrates clear and direct value, solving a specific operational or security need.

The add-on delivers significant business impact, improving operational efficiency, security monitoring, or analytics workflows at scale.

Category

Developing

(1 point)

Meets Expectations 

(3 points)

Exceeds Expectations

(5 points)

Fit

The SPL2 pipeline has minimal alignment with the hackathon goals.

The SPL2 pipeline has moderate alignment, addressing a relevant data management challenge.

The SPL2 pipeline strongly aligns with the hackathon’s goals, providing a well-structured and impactful solution for data ingestion, transformation, or optimization.

Functionality / Usability

The SPL2 pipeline demonstrates minimal functionality (20-40%), with incomplete or inefficient processing logic.

The SPL2 pipeline is functional (40-80%) and achieves most intended goals with some processing inefficiencies or minor usability gaps.

The SPL2 pipeline is fully functional (80-100%) and efficiently processes, transforms, or optimizes data with seamless usability and scalability.

Innovation

The SPL2 pipeline provides a basic transformation or processing method without unique or creative improvements.

The SPL2 pipeline solves a relevant data challenge with a thoughtful approach to ingestion, transformation, or storage optimization.

The SPL2 pipeline introduces an innovative way to process data, improving performance, scalability, or efficiency beyond existing solutions.

Business Value

The SPL2 pipeline has minimal practical business relevance or impact.

The SPL2 pipeline demonstrates clear and direct value, improving data processing or storage efficiency for Splunk users.

The SPL2 pipeline delivers significant business impact, improving data ingestion, transformation accuracy, or query performance at scale, leading to operational efficiency or cost savings.

Category

Developing

(1 point)

Meets Expectations 

(3 points)

Exceeds Expectations

(5 points)

Fit

The ML model has minimal alignment with the hackathon goals.

The ML model has moderate alignment, addressing a relevant security anomaly detection challenge.

The ML model strongly aligns with the hackathon’s goals, providing a well-structured and impactful threat detection or predictive analytics solution.

Functionality / Usability

The app demonstrates minimal functionality (20-40%), with incomplete or ineffective ML implementation.

The app is functional (40-80%) and achieves most intended goals, with some usability gaps or performance limitations.

The app is fully functional (80-100%) and delivers a seamless experience, efficiently processing security/log data and detecting anomalies in Splunk.

Innovation

The app applies standard ML techniques without unique or creative improvements.

The app solves a relevant security or operational challenge with a well-implemented ML approach.

The app introduces an innovative way to detect threats, optimize ML workflows, or enhance model accuracy within Splunk.

Business Value

The ML model has minimal practical business relevance or impact.

The ML model demonstrates clear and direct value, improving threat detection, operational visibility, or security monitoring.

The ML model delivers significant business impact, providing actionable insights that enhance cybersecurity posture, reduce risk, or optimize Splunk operations.