Maximizing the Power of AI Assistants in Data Analysis
Posted: Mon Dec 09, 2024 5:43 am
At Zeo, we strongly believe in taking a structured, methodical approach when using AI assistants for data analysis. Without proper data preparation, context, and rapid curation, these tools will fail to provide meaningful insights. In this comprehensive blog post, we will share the step-by-step methodology our team follows to successfully use AI to deliver robust data analysis results, building on what we learned from İren Saltalı ’s enlightening video. To further your knowledge of the different use cases of AI , you can check out our AI blog page , or visit our YouTube channel to watch the full video .
Step 1: Understand Your Data Deeply
The first step is to fully understand the data you already have. Here are some key questions to ask yourself:
What data sources do we currently have access to? Rank tracking? Social media APIs? BigQuery?
Knowing exactly what data sources are available allows you to make the most of the data you already have before looking for additional sources. Don’t assume AI will only know what you have.
What specific fields or attributes are available for each data source?
Catalog full details of all fields in each data source. These represent attributes that can be queried and analyzed.
What does each field represent semantically? Don't assume the AI will automatically understand.
Semantics are very important. For example, a field called “type” in your data may not refer to product types without a clear description.
What industry or domain-specific terminology is used in the data? Clearly define these.
Avoid industry jargon and define acronyms. AI doesn’t have overseas chinese in europe data inherent knowledge of your business or industry.
What are the relationships between fields or entities in the data?
Documenting relationships such as one-to-many, foreign keys, etc. helps AI understand the interconnectedness of data.
What data validation, cleaning or preprocessing has been done previously?
Informing the AI about any data cleansing provides context as to what transformation is occurring.
Document all of this information into a “data dictionary” that fully describes your existing data for the AI assistant.
Source: Dall-E 3
Step 2: Break the Analysis Down into Discrete Steps
Once the data landscape is clear, break down the actual analysis into separate modular steps:
What is the ultimate analytics goal we want to enable?
Having a very clear goal guides the entire analysis workflow.
Can any basic data extraction or preparation be done before incorporating AI?
Use standard SQL queries on raw data before requiring AI input.
What parts of the analysis rely on understanding the semantic meaning of fields and what parts of the analysis rely on simply querying the data?
Separate the parts of AI that require natural language understanding from simple data extraction.*
What intermediate outputs will be useful for next steps?
Design modular steps so that the output of one stage can feed into the next stage.
Essentially, avoid asking AI to do everything at once. Turn it into a workflow of discrete steps, some of which involve pure data manipulation and others that touch AI.
Source: Dall-E 3
Step 3: Frame Contextual Prompts and Iteratively Refine
Where the AI assistant will be used, frame prompts carefully to provide sufficient context and examples:
Clearly outline goals and data dictionary in initial prompts.
This summary grounds AI in what data sources are available and the end goal.
Provide plenty of examples of desired inputs and outputs.
Examples help the AI infer appropriate analysis logic.
Use natural language - treat the assistant as if you were a colleague and engage in dialogue.
Colloquial language facilitates clarification of ambiguities.
Expect failures at the beginning! Be prepared to iteratively improve prompts.
Preparing a prompt is an art that evolves through trial and error.
Provide feedback to the assistant when results are incorrect and guide him interactively.
Active learning through reciprocal exchange strengthens prompt quality.
Chain prompts in a logical workflow, piping output from one prompt to another.
Connecting the prompts together creates an automated end-to-end analysis.
Well-framed prompts are critical to success with AI assistants. Treat prompt creation as an iterative skill that needs to be developed.
Source: Dall-E 3
Step 4: Stress Test with Unusual Data
Any analytics workflow developed needs to be stress-tested with extreme cases and unusual data inputs:
What inputs can cause problems and break things?
Consider potential inputs that could be problematic and actively evaluate them.
Adopt a “don’t break” mentality – be the enemy. Actively try to fail the analysis by presenting strange test cases.
Provide intentionally incorrect and problematic data to test the assistant’s capabilities. Using incorrect data can help make prompts more robust.
When errors occur, refine the prompts to make them stronger. Use errors as feedback to strengthen the logic and exceptions of the prompts.
Plan to evolve prompts over time as new anomalies arise.
Prompts continue to be improved as new situations arise.
Testing models in simulated stress scenarios prepares them to deal with the complexity of real-world data.
Step 5: Expand Data Sources
Don’t limit your analysis to a single data source, like rank tracking. The more diverse signals you have, the better insights AI can derive:
Step 1: Understand Your Data Deeply
The first step is to fully understand the data you already have. Here are some key questions to ask yourself:
What data sources do we currently have access to? Rank tracking? Social media APIs? BigQuery?
Knowing exactly what data sources are available allows you to make the most of the data you already have before looking for additional sources. Don’t assume AI will only know what you have.
What specific fields or attributes are available for each data source?
Catalog full details of all fields in each data source. These represent attributes that can be queried and analyzed.
What does each field represent semantically? Don't assume the AI will automatically understand.
Semantics are very important. For example, a field called “type” in your data may not refer to product types without a clear description.
What industry or domain-specific terminology is used in the data? Clearly define these.
Avoid industry jargon and define acronyms. AI doesn’t have overseas chinese in europe data inherent knowledge of your business or industry.
What are the relationships between fields or entities in the data?
Documenting relationships such as one-to-many, foreign keys, etc. helps AI understand the interconnectedness of data.
What data validation, cleaning or preprocessing has been done previously?
Informing the AI about any data cleansing provides context as to what transformation is occurring.
Document all of this information into a “data dictionary” that fully describes your existing data for the AI assistant.
Source: Dall-E 3
Step 2: Break the Analysis Down into Discrete Steps
Once the data landscape is clear, break down the actual analysis into separate modular steps:
What is the ultimate analytics goal we want to enable?
Having a very clear goal guides the entire analysis workflow.
Can any basic data extraction or preparation be done before incorporating AI?
Use standard SQL queries on raw data before requiring AI input.
What parts of the analysis rely on understanding the semantic meaning of fields and what parts of the analysis rely on simply querying the data?
Separate the parts of AI that require natural language understanding from simple data extraction.*
What intermediate outputs will be useful for next steps?
Design modular steps so that the output of one stage can feed into the next stage.
Essentially, avoid asking AI to do everything at once. Turn it into a workflow of discrete steps, some of which involve pure data manipulation and others that touch AI.
Source: Dall-E 3
Step 3: Frame Contextual Prompts and Iteratively Refine
Where the AI assistant will be used, frame prompts carefully to provide sufficient context and examples:
Clearly outline goals and data dictionary in initial prompts.
This summary grounds AI in what data sources are available and the end goal.
Provide plenty of examples of desired inputs and outputs.
Examples help the AI infer appropriate analysis logic.
Use natural language - treat the assistant as if you were a colleague and engage in dialogue.
Colloquial language facilitates clarification of ambiguities.
Expect failures at the beginning! Be prepared to iteratively improve prompts.
Preparing a prompt is an art that evolves through trial and error.
Provide feedback to the assistant when results are incorrect and guide him interactively.
Active learning through reciprocal exchange strengthens prompt quality.
Chain prompts in a logical workflow, piping output from one prompt to another.
Connecting the prompts together creates an automated end-to-end analysis.
Well-framed prompts are critical to success with AI assistants. Treat prompt creation as an iterative skill that needs to be developed.
Source: Dall-E 3
Step 4: Stress Test with Unusual Data
Any analytics workflow developed needs to be stress-tested with extreme cases and unusual data inputs:
What inputs can cause problems and break things?
Consider potential inputs that could be problematic and actively evaluate them.
Adopt a “don’t break” mentality – be the enemy. Actively try to fail the analysis by presenting strange test cases.
Provide intentionally incorrect and problematic data to test the assistant’s capabilities. Using incorrect data can help make prompts more robust.
When errors occur, refine the prompts to make them stronger. Use errors as feedback to strengthen the logic and exceptions of the prompts.
Plan to evolve prompts over time as new anomalies arise.
Prompts continue to be improved as new situations arise.
Testing models in simulated stress scenarios prepares them to deal with the complexity of real-world data.
Step 5: Expand Data Sources
Don’t limit your analysis to a single data source, like rank tracking. The more diverse signals you have, the better insights AI can derive: