Are you looking to level up your data game for AI in 2023? You‘ve come to the right place! Properly collecting and preparing data is the key to training accurate AI systems.
As an experienced data scientist, let me walk you through 7 data collection best practices I‘ve found critical for AI success. Follow these, and you‘ll be on the road to building top-performing models!
1. Clearly Define the AI Objectives
First things first – before you gather a single piece of data, be crystal clear on the purpose of your AI system.
What types of decisions will it automate? Will it classify images? Forecast sales? Recommend products? Understanding the objectives informs what data you need.
For example, a system classifying customer feedback for sentiment analysis requires very different data than one predicting equipment failures from IoT sensor data.
Pro Tip: Document the exact tasks the AI will perform. Review regularly to stay on track collecting suitable data.
2. Pick the Right Data Types and Formats
The algorithms behind AI models rely on consuming data in specific formats.
For computer vision, you need images and video. Natural language processing (NLP) models use text corpora. Time series forecasting depends on numerical data indexed by time.
In addition to format, consider attributes like units, resolution, metadata/annotations, aggregations, and granularity.
Pro Tip: Catalog all the data types and formats you‘ll need based on your AI model algorithms.
3. Blend Internal and External Data Sources
Very few companies have sufficient data within their four walls to properly train AI.
Combining internal data like customer information, sales transactions, website activity, and sensor logs with external data from providers, partners, and public sources creates a robust data foundation.
For instance, supplement internal sales data with third-party data on market trends, competitive activity, demographics, and economic indicators.
Pro Tip: Audit what internal data is available, then determine what gaps external data can fill to get the diversity and scale you need.
4. Build a Data Pipeline with Automated Collection
Manually gathering one-off data sets is extremely inefficient. You want a data pipeline that automatically extracts data on an ongoing basis.
Key steps in a pipeline include:
- Extracting data from APIs, applications, websites, sensors, etc.
- Transforming it into formats usable by AI models
- Loading it into cloud storage like data lakes
- Refreshing with new data on a scheduled cadence like daily or hourly
This enables continuous self-service data collection at scale.
Pro Tip: Architect data pipelines for efficiency and reliability. Cloud services like AWS, GCP, and Azure offer fully-managed options.
5. Institute a Structured Data Collection Process
Ad hoc, scattered data collection results in low-quality and unreliable data.
Instead, take a structured approach including:
- Assembling a cross-functional data collection team with IT, data, and business domains
- Documenting data requirements and sources
- Selecting extraction tools and methods
- Developing transformation logic
- Defining storage strategy and environments
- Building a central catalog or knowledge base
- Creating calendars for pipeline runs and data updates
Formalize this into policies, procedures, and workflows to ensure consistency as data needs evolve.
Pro Tip: Treat data collection as an ongoing managed process, not one-off projects.
6. Keep Data Privacy Top of Mind
With the rise of regulations like GDPR and CCPA, the risks around mishandling personal and sensitive data are higher than ever.
Work closely with legal and compliance teams to:
- Classify data appropriately
- Anonymize datasets where possible
- Gain consent for collecting and using data
- Limit retention periods
- Secure PII and financial data
- Ensure transparency through disclosures and rights management
Pro Tip: Make privacy a priority, not an afterthought. Being good data stewards builds customer trust.
7. Carefully Curate Your Data
More data isn‘t necessarily better data. Taking the time to carefully evaluate and curate datasets leads to higher quality model inputs.
Techniques like statistical analysis, visualization, and sampling can help identify issues like:
- Missing or incomplete data
- Outliers and errors
- Biases and skewed distributions
- Irrelevant or correlated attributes
Resolve identified problems to improve data veracity. Maintain metadata like data dictionaries to track lineage and meanings.
Pro Tip: Data quality is everything. Prioritize curation time to remove cruft.
There you have it – my top 7 data collection best practices for AI success in 2023:
- Clearly define objectives
- Select suitable data types
- Blend diverse data sources
- Automate collection with pipelines
- Take a structured approach
- Make privacy a priority
- Curate carefully for quality
Properly collected data serves as the rocket fuel to boost your AI initiatives to new heights. Now get out there, start collecting, and let the machines learn!