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HRTech

Digitally Transforming

Resource Allocation

for a Data Annotation Leader

Keywords

Data Annotations, Machine Learning, Algorithm Development, Agile Methodologies, CI/CD Pipelines

Services Rendered

Product Design

Product Engineering

Continuing Design

Tech stack

React, Node.js, PostgreSQL, Kafka

$19M Series B

A global leader in data annotation, partnered with Zemoso to co-create a talent management system that automates, unifies, and streamlines talent allocation, and scales as the organization evolves.

iNDUSTRY CHALLENGE

Client’s resource allocation process relied on manually identifying available resources among a pool of 5,000 professionals, matching skills to assess suitability, and allocating them to projects.

The data annotation company decided to mitigate operational inefficiencies by partnering up with Zemoso to build a machine learning (ML) powered Talent Management System (TMS).

Zemoso built the TMS from ground-up and enabled Solution Architects with the following:

  1. Resource Allocation Efficiency: Managing a vast workforce with diverse skill sets and availability across numerous projects without delaying project onboarding and delivery.
  2. Talent Shortages and Skill Gaps: Enabling visibility into talent and skill gaps causing delay in critical hiring decisions and project launches, directly impacting business outcomes.
  3. Human Resource Operations: Overcoming information silos, and accelerating decision-making by integrating disparate people systems and processes.

Zemoso’s Partnership Challenge

Zemoso pods opted for a strategic engineering approach for deploying the TMS and resolved the following challenges.

  1. Integration Complexity: Integrating the TMS with the client’s people management and upskilling systems with seamless connectivity and data synchronization across systems without disrupting ongoing operations.
  2. Real-time Data Processing: Facilitating real-time data processing and communication among diverse modules to provide real-time updates on resource availability and project demands.
  3. Adapting to Client Workflows: Aligning the TMS with the client’s internal workflows, operational processes, and organizational culture to fit the client’s specific use case.

Impact created

Zemoso pod successfully delivered the required functionalities within a tight deadline of 4 months.

4

Months

What are our clients saying?

Our clients love what we do:

How do we do this?

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Zemoso’s discovery pod collaborated with the stakeholders for a week of GV-inspired design sprint, nailed the golden user path, and created a high-fidelity, clickable prototype in a two-weeks sprint.

Architecture Sprint

We opted for the Domain-Driven Design (DDD) approach to identify and prioritize essential entities, services, and Non-Functional Requirements (NFRs). We crafted a hybrid architecture — microservices + event-driven architecture to better meet client’s needs

Microservices architecture: For management of RESTful web service.Event-driven architecture: For async capturing and processing of events occurring in different systems.

User Story Mapping

We created user story maps to understand user needs and requirements, and used the t-shirt sizing approach to estimate the effort, define the scope, and prioritized features accordingly.

Algorithm Development

Zemoso built an ML algorithm to ensure a systematic and structured process for resource allocation. The algorithm maximized workforce efficiency through:

  1. Skill Information Management: We used PostgreSQL as the database management system to regularly store and update employees' skill information, leveraging the structured nature of PostgreSQL for ensuring data integrity and reliability.
  2. Employee Availability Management: Managed and tracked employees' availability status in real-time with Node.js’s asynchronous event-driven architecture to fast-track resource allocation.
  3. Project Requirements and Matching: The TMS captured incoming project requirements with a user-friendly React interface and processed all project opportunities with a Node.js back-end, leveraging Kafka event streaming to communicate with all other people management systems.
  4. Skills Matching and Calculation: The ML algorithm utilized the stored skill information and employed computational logic to compare project requirements and skill sets of available employees to provide a clear compatibility assessment.
  5. Data-driven Decision-making: The TMS displayed the calculated match percentage on the front-end to the Project Managers and enabled data-driven decision making. API Development
  6. We developed APIs using Node.js to connect the TMS with the client’s other people solutions. These APIs facilitated seamless communication among various platforms establishing interconnectivity and data flow.

Robust CI/CD

Throughout the partnership, we implemented CI/CD pipelines to ensure shorter feedback loops and to steadily make progress on product development milestones. Our agile methodologies, and continuous feedback loops allowed us to iterate, streamline development, and refine the product.

In Conclusion

Zemoso design and engineering pods built the TMS using a blend of modern tech stack, architectural strategies, and iterative development methodologies. The client decided to test-drive the Talent Management System and assess effectiveness internally.

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