On the design of the product vision and the product backlog for IDP applications

Source: Product Backlog vs Sprint Backlog in Scrum | DoneTonic

Introduction

In today’s digital age, the management and processing of vast amounts of unstructured data have become paramount for organizations across industries. As businesses grapple with mountains of documents, extracting valuable insights and information becomes a time-consuming and error-prone task. However, the convergence of Computer Vision and Natural Language Processing (NLP) has opened up new horizons in document processing, paving the way for Intelligent Document Processing (IDP) applications.

Drawing inspiration from Geoffrey Moore’s product vision template, we crafted a compelling vision for IDP, envisioning it as an indispensable tool for organizations across industries. Our aim is to establish IDP applications as the market leader, enabling users to unlock the full potential of unstructured data, gain a competitive advantage, and drive innovation.

As we progress through this article, we will explore practical aspects of IDP implementation, including the generation of a product backlog using Fibonacci numbers to estimate effort and prioritize development tasks. We will discuss the significance of user stories, acceptance criteria, and backlog refinement in ensuring a well-structured and efficient development process.

Product vision

Here’s a product vision using Geoffrey Moore’s template for your Intelligent Document Processing application:

For [target customers], Our Intelligent Document Processing solution is a groundbreaking platform that combines cutting-edge Natural Language Processing (NLP) and Computer Vision technologies.

Unlike traditional document processing methods, our solution revolutionizes the way [target customers] handle vast amounts of unstructured data by leveraging advanced NLP algorithms and sophisticated computer vision techniques.

We enable [target customers] to:

1. Streamline document extraction and classification: Our solution automates the extraction of valuable information from various types of documents, reducing manual effort and eliminating errors.
2. Enhance data accuracy and quality: By leveraging powerful NLP models, we ensure accurate interpretation, understanding, and extraction of relevant information, resulting in improved data quality.
3. Improve operational efficiency: With our intelligent automation capabilities, [target customers] can process documents rapidly, freeing up valuable time for higher-value tasks and optimizing overall productivity.
4. Gain actionable insights: Our solution empowers [target customers] to unlock hidden patterns and valuable insights within their document repositories, facilitating data-driven decision-making and strategic planning.
5. Ensure compliance and security: We prioritize the highest standards of data security and compliance, ensuring that sensitive information remains protected throughout the document processing lifecycle.

By addressing these critical needs, Our Intelligent Document Processing application will become an indispensable tool for [target customers], transforming their document management workflows, boosting efficiency, and empowering them to make informed decisions based on accurate, timely data.

Ultimately, we aim to establish ourselves as the market leader in Intelligent Document Processing, empowering [target customers] across industries to unlock the full potential of their unstructured data, gain a competitive advantage, and drive innovation.

By executing our vision, we will revolutionize how [target customers] handle document processing, opening up new opportunities for growth, efficiency, and strategic insights.

Product Backlog

Here’s a detailed way to write a product backlog:

1. User Story Title: Provide a concise title for each user story that describes the desired functionality or requirement. For example, “Document Upload and Processing.”
2. User Story Description: Write a brief description of the user story, capturing the “who,” “what,” and “why” aspects. Focus on the user’s perspective and the value the feature or functionality will provide. For example, “As a user, I want to be able to upload documents to the system and have them processed automatically to extract relevant information, reducing manual effort and improving efficiency.”
3. Acceptance Criteria: Define specific acceptance criteria that outline the conditions that must be met for the user story to be considered complete. These criteria serve as a basis for testing and validation. For example, “The system should accept common document formats such as PDF, Word, and images. The uploaded documents should be processed using optical character recognition (OCR) to extract text and relevant data. The extracted data should be validated for accuracy with an acceptable margin of error.”
4. Effort Estimate: Assign a Fibonacci number to each user story, indicating the perceived effort, complexity, or relative size of the task. This helps the team understand the magnitude of each user story and assists with planning and prioritization.
5. Dependencies: Identify any dependencies between user stories or external factors that may impact their implementation. This information helps the team understand the order in which the user stories should be tackled.
6. Priority: Determine the priority of each user story based on business value, customer needs, strategic goals, and other relevant factors. This ensures that the most valuable and essential user stories are addressed first.
7. Story Points: Optionally, you can assign story points to each user story to represent the overall effort required for implementation. Story points are a relative measure that can help with capacity planning and team velocity tracking.
8. Notes and Attachments: Include any additional notes, references, or attachments that provide further details, examples, or context for the user story. This can include wireframes, mockups, design specifications, or any other relevant information.
9. Backlog Refinement: Regularly review and refine the product backlog in collaboration with the team. This involves adding new user stories, modifying existing ones, removing obsolete items, and ensuring the backlog remains up to date and aligned with the project goals.

Remember, the product backlog is a living document that evolves throughout the project. It’s important to maintain open communication with the team and stakeholders, regularly groom the backlog, and adapt it as new information emerges.

Effort estimation focuses on the actual time or resources required for a specific user story, while story point estimation provides a relative measure of effort to help with prioritization, capacity planning, and understanding the size and complexity of backlog items. Both estimation approaches have their benefits and can be used in conjunction to manage and prioritize the product backlog effectively.

Conclusion

Throughout our conversation, we have delved into the exciting realm of Intelligent Document Processing (IDP) by harnessing the power of Computer Vision and Natural Language Processing (NLP). We have explored the potential of IDP applications in revolutionizing document processing workflows, automating data extraction, and enabling organizations to unlock valuable insights from unstructured data.

By combining NLP algorithms and Computer Vision techniques, IDP offers a transformative solution to the challenges of manual document processing. Through our exploration of product vision and product backlog generation using Fibonacci numbers, we have emphasized the importance of efficient planning, prioritization, and collaboration within IDP development teams.

Moreover, our discussion has shed light on the iterative nature of backlog refinement, emphasizing the need for continuous updates and re-estimation to ensure accuracy and adaptability. Regular collaboration and communication within the team and with stakeholders remain crucial for maintaining a well-structured and dynamic product backlog.

By embracing the principles discussed in this conversation, organizations can position themselves at the forefront of IDP innovation, gaining a competitive advantage and empowering their teams to unlock the full potential of unstructured data.

In conclusion, the fusion of Computer Vision and NLP in IDP applications represents a significant leap forward in document processing. Through efficient product backlog management, accurate effort estimation, and continuous refinement, organizations can harness the power of IDP to streamline operations, drive insights, and achieve new levels of efficiency and effectiveness.

Thank you for engaging in this insightful discussion, and we wish you success in your endeavors within the fascinating realms of Computer Vision, NLP, and Intelligent Document Processing.

Acknowledgements

This article was generated with the help of OpenAI’s ChatGPT software by providing user prompts. The research was carried out with the support of IN-D Power of AI.

About the author

My name is Arvind (Yetirajan*) Narayanan Iyengar and I am working as an AI/ML R&D engineer at IN-D by Emulya Technologies PTE LTD. My interests lie in Computer vision, NLP, Deep learning, Machine learning and Software engineering. My LinkedIn profile can be found at: https://www.linkedin.com/in/arvind-yetirajan-narayanan-iyengar-2b0632167/

*meaning king of the ascetics