Is Your Application Ready for AI?

With the growing importance of AI and its far-reaching impacts on lives and businesses, along with regular publications of new models in the market, everyone wants a piece of the AI journey and would like to be first movers in the space. In this process, teams often overlook the requirement to understand the environment and the applications that generate data and jump into solving the problem.

Role of Data in AI Readiness

Data is the key and focal point; as all activities revolve around the data. Data is like a diamond: it needs to be explored, mined, sorted, cut, polished, and undergo final inspection and recutting, which makes it ready for the jeweler's requirement.

In a typical environment:

• Multiple systems are involved in generating the business outcome.
• These systems can be internal or connected with external systems.
• Data can be in various forms, from different platforms and formats.
• Data may exist in various databases, tables, structures, derived, normalized, or not normalized.
• System applications have evolved over the years, with new fields added, changed, etc.

Do not try to solve the business use case by yourself without involving the SME experts in the domain, architects, business owners, application SMEs, and AI data roles. While assembling a team to address the business use case, we need to ask the question: "Is your application

AI-ready?" When you ask this question, consider the data that is being generated from various applications involved in use cases. Gathering data for AI algorithms and processing is a crucial first step in building any AI system.

Following examples encapsulates the key issues and reasons for failure in each of the AI examples we discussed. They highlight how problems in data sourcing, evaluation, and integration can lead to significant issues in AI system performance and fairness.

• Google Flu Trends: Over-reliance on search query data without integrating other sources led to inaccurate flu outbreak predictions.
• Microsoft's Tay Chatbot: Failure to filter toxic data from Twitter led to the chatbot learning and reproducing offensive language.
• Facebook's Image Recognition Failure: Poor quality and diversity in training data labels resulted in racially biased image classification
• Zillow's Home-Buying Algorithm: Inadequate data cleaning led to overvalued property purchases, causing significant financial losses.

Steps to Ensure AI Data Readiness

Define your problem and objectives. What problem are you trying to solve with AI? This will determine the type of data you need. For example, if you are building a chatbot, you will need conversational data. If you are creating an image recognition system, you will need images. What are your goals for the AI system? Do you want it to predict, classify, generate, or do something else? This will influence the data you gather and how you process it.

As part of the exploration and mining process of data, we need to:

• Identify and evaluate potential internal and external data sources, such as customer records, sensor data, public datasets, and third-party data providers. Assess the quality, relevance, and accessibility of the data sources.
• Develop processes to extract data from various sources, often involving data engineering techniques like web scraping, API integration, and database querying. Ensure seamless data integration by standardizing formats, managing missing values, and resolving data inconsistencies.

After mining the data, we need to cut, polish, and label the data:

• Clean and preprocess the data to address issues like outliers, duplicates, formatting errors, and inconsistencies. Transform data that can improve the AI model's performance.
• Engage in the labeling process if it does not exist. Ensure the accuracy and consistency of the labeling process, as the quality of the labels directly impacts the model's performance.
• Split the data into training, validation, and test sets to assess the model's performance during the development and deployment stages. Implement a robust data versioning system to track changes, manage data lineage, and ensure reproducibility of the AI models.

With data identified, we need to design a data storage and management system:

• Determine the appropriate data storage solutions, such as data lakes, data warehouses, or cloud-based storage services, based on the volume, velocity, and variety of the data.
• Establish data governance policies and procedures to ensure the security, privacy, and compliance of the data.

Since the above process is not a one-time activity and systems change, enhance, or retire:

• Continuously monitor the data quality and relevance and update the data sources and preprocessing pipelines as needed to keep the AI models up to date.
• Develop mechanisms to detect data drift and trigger model retraining or fine-tuning to maintain the model's performance over time.

By following these steps, you can effectively gather, manage, and maintain high-quality data to support the development and deployment of robust AI algorithms that deliver meaningful business insights and drive value for your organization.

Challenges in Preparing Data for AI Systems

Data mining and data collection for AI projects present several common challenges:

• Data Quality:
  • Data from diverse sources may have varying formats, units, or levels of accuracy, making it difficult to integrate and analyze.
  • Datasets often contain missing values, requiring imputation or other techniques to handle the gaps.
  • Extreme values can skew analysis and impact model performance.
• Data Bias:
  • The data collected may not be representative of the overall population, leading to biased models.
  • Existing datasets may reflect historical prejudices or inequalities, perpetuating them in AI systems.
• Data Privacy and Security:
  • Complying with regulations like GDPR and CCPA requires careful consideration of data collection, storage, and usage.
  • Protecting systems and sensitive data from unauthorized access is crucial.
• Data Scalability and Cost:
  • AI models often require massive amounts of data, which can be costly to collect, store, and process.
  • Labelling data for supervised learning can be time-consuming and expensive, especially for complex tasks.
• Data Accessibility:
  • Accessing relevant datasets for specific tasks can be challenging, especially for niche areas.
  • Obtaining permission to use data from external sources can be complex.
• Data Changes:
  • Real-world data can change over time, requiring model retraining to maintain performance.
  • Data quality can degrade over time, or data generation may cease, requiring data maintenance and cleaning.
• Data Ethics:
  • Ensuring that AI systems are fair, unbiased, and transparent is paramount.
  • Guarding against the misuse of data for unethical or harmful purposes.

Conclusion

The journey towards AI is complex, requiring a holistic approach that extends beyond mere technological implementation. The challenges we have discussed – from data quality and bias to privacy concerns and ethical considerations – underscore the critical need for thorough preparation of AI-ready systems.

Key takeaways:

• Ensure your applications and data infrastructure can support AI initiatives before starting.
• Establish processes for continuous monitoring and updating of your data and AI models.
• Foster cross-functional collaboration among domain experts, data scientists, and business leaders.
• Prioritize ethical considerations, including fairness and transparency, in all AI projects.
• View AI readiness as an ongoing strategic process aligned with long-term organizational goals.

As you move forward, regularly revisit the question: "Is your application ready for AI?" This encompasses not just technical capabilities, but also ethical implications and strategic alignment. By addressing these aspects, you will be better positioned to harness AI's potential responsibly and effectively.