How to Prepare Data for AI Powered Analytics & Copilots?

4 min read
Jun 6, 2024 11:03:19 AM

Step by Step Process to Prepare your Data for AI Powered Analytics and Copilots?

AI is all the rage but most organizations are not ready to leverage artificial intelligence on top of their existing data assets. Before we dive into the steps to prepare your data to be optimized for AI copilots let's discuss why preparing data is more important than the actual AI use case.

First, organizations need to have trusted data to make decisions. To have trusted data you must ensure the data is clean, conformed, and standardized. Merging data from multiple systems can be complex without the right tools to ensure clean and well curated data.

Second, having a critical mass of data to provide insights is imperative to support good decision making. For example, if you only load sales data into your data repository, when a user asks what customers are at risk of churning you will not get an accurate answer. To properly respond to a natural language AI Copilot question about churn, the system would need to have sales data, customer satisfaction data, payment history, support history, forecasts, and additional external information about the health of your customers' businesses. Hence, it's important to ensure you have the necessary amount of data to get answers of value.

While these two aforementioned points were brief, books have been written on how big data helps drive better and fuller decision spectrums. Now that we understand why having enough and well curated data is important for AI, let's look at the necessary steps to prepare data for AI.

We'll conclude by showing you how eyko is designed as one integrated platform to help you accelerate these steps to prepare data for AI, as eyko not only uses AI to prepare and clean the data, but it also has an AI Assistant for users to pose questions such as, what customers are at risk of churning.

How to prepare data for AI?

Here is a step by step guide to prepare your data to get the most out of AI copilots.

  1. Define the problem and data requirements
  2. Data Collection
  3. Data Cleaning
  4. Data Transformation and Enrichment
  5. Data Structuring
  6. Data Annotation
  7. Data Splitting
  8. Data Validation
  9. Data Storage
  10. Continuous Monitoring and Updates

Preparing data for AI copilots involves several critical steps, including data collection, cleaning, transformation, and validation. Here’s a detailed, step-by-step guide to help you through this process:

1. Define the Problem and Data Requirements

a. Identify Objectives

  • Determine the specific tasks the AI copilot will perform.
  • Set clear objectives for the data to meet these needs.

b. Specify Data Needs

  • Decide the types of data required (e.g., what data sources structured and unstructured will you require).
  • Determine data sources (e.g., databases, web scraping, SaaS, Cloud, files, APIs).

2. Data Collection

a. Gather Data

  • Collect data from specified sources.
  • Ensure data variety to cover different scenarios the AI might encounter.

b. Ensure Data Quality

  • Check data for completeness.
  • Ensure data accuracy by cross-referencing with reliable sources.

3. Data Cleaning

a. Remove Inconsistencies

  • Identify and correct errors (e.g., typos, incorrect values).
  • Remove duplicate records.

b. Handle Missing Values

  • Decide on a strategy (e.g., imputation, removal) for dealing with missing data.
  • Implement the chosen strategy to fill or remove gaps.

4. Data Transformation and Enrichment

a. Normalize Data

  • Convert data to a consistent format (e.g., date formats, measurement units).
  • Scale numerical values if necessary for uniformity.

b. Data Enrichment

  • Enrich data with new calculations, classifications, hierarchies, time intelligence, and more to that might enhance model performance.
  • Select relevant enrichment to improve efficiency.

5. Data Structuring

a. Choose a Suitable Format

  • Select an appropriate data format (e.g., in-memory, LLM, XML) based on the AI copilot's requirements.

b. Organize Data

  • Structure the data logically with clear labels and organization.
  • Ensure relational integrity if using databases.

6. Data Annotation (if needed)

a. Label Data

  • Manually or automatically annotate data (e.g., tagging images, categorizing text).
  • Ensure high-quality annotations by using reliable annotators or tools.

b. Validate Annotations

  • Cross-check annotations for consistency and correctness.
  • Use a sample review to ensure annotation quality.

7. Data Splitting

a. Divide Data

  • Split data into training, validation, and test sets.
  • Ensure random and representative sampling for each subset.

b. Ensure Non-Leakage

  • Check that no data leakage occurs between training and test sets.

8. Data Validation

a. Verify Data Integrity

  • Check data consistency across the dataset.
  • Validate data against known benchmarks or standards. (for example, pose a question using the AI Assistant and compare the answer to a query you define using a traditional query building process).

b. Conduct Quality Assurance

  • Perform statistical analysis to ensure data quality.
  • Visualize data to detect any hidden issues.

9. Data Storage and Accessibility

a. Store Data Securely

  • Choose a reliable storage solution (e.g., cloud storage, databases).
  • Ensure data security with appropriate access controls.

b. Maintain Accessibility

  • Organize data for easy retrieval.
  • Ensure compatibility with AI copilot systems.

10. Continuous Monitoring and Updating

a. Implement Monitoring

  • Set up systems to monitor data quality and performance regularly.
  • Detect and address issues promptly.

b. Update Data

  • Regularly update the dataset to keep it relevant and accurate.
  • Incorporate new data and remove obsolete information.

By following these steps, you can ensure that the structured data you prepare is of high quality and suitable for training and deploying effective AI copilots.

Why you need to follow these steps?

In the use cases of natural language processing (NLP) and artificial intelligence (AI), the importance of clean and structured data cannot be overstated. Clean data ensures that AI models receive accurate and relevant information, leading to more reliable outputs. When dealing with natural language chats, unstructured data—such as text from various conversations—can be rife with inconsistencies, noise, and errors. By organizing and cleaning your data, you will enhance the quality of the inputs, which directly influences the efficacy and precision of AI-driven insights. These foundational steps are crucial for developing models that understand and process human language effectively, enabling more sophisticated interactions and decision-making.

Tools and Technologies

There are many tools on the market that support the steps to prepare your data for AI from ETL, Web Scraping, Cleaning, Transformation, Storage, and others. eyko was designed from to combine the entire data pipeline process into one platform to help you accelerate your adoption of AI. 

To learn more about eyko's ability to prepare your data please read this post.

Best Practices

In addition to the steps to prepare your data, we conclude with some additional points on best practices for curating your data.

  • Ensure Data Privacy: Comply with data protection regulations (e.g., GDPR, CCPA).
  • Document Processes: Maintain thorough documentation for all data processing steps.
  • Collaborate and Communicate: Work with stakeholders to ensure data meets their needs and expectations.
  • Iterate and Improve: Continuously refine the data preparation process based on feedback and results.


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