As a data analyst with over 10 years of experience in web scraping and data extraction, I‘ve witnessed the rising prominence of data collection across industries. More companies now realize the value of becoming data-driven to gain competitive advantage. However, many still struggle to collect the right data for the right reasons.
In this post, I‘ll provide my insider perspective on the top 5 most common use cases for data collection based on current trends. Understanding the core applications of collected data will help businesses focus their efforts for maximum impact.
1. Training AI and Machine Learning Models
Hands down, gathering quality data to train AI and ML algorithms is one of the top reasons behind the data collection boom. The more diverse, representative data used for training, the better models become at delivering accurate insights and predictions.
In fact, studies demonstrate a direct mathematical relationship between the size of training data and model accuracy, as the below graph illustrates:
Figure 1: Larger training data leads to higher model accuracy. Source: AIMultiple Research
As you can see, the image classification model improves from 50% to over 90% accuracy as the number of training images rises into the millions. I‘ve observed similar performance gains across computer vision, NLP, recommendation engines, and other AI applications.
However, some key factors go into data preparation for effective ML model training:
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Volume: Models need thousands to millions of training examples, depending on complexity. For instance, GPT-3 was trained on 45 TB of text data.
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Diversity: Data must cover the full range of expected inputs without bias. For image recognition, this means varied images of each object from different angles, lighting, backgrounds, etc.
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Quality: Training data needs thorough cleaning, labelling, augmentation etc. Missing or inaccurate labels severely impact model learning.
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Relevance: Data should closely match real-world examples the model will handle. For Customer Support chatbots, training conversations must resemble human queries.
As an expert practitioner, I always emphasize addressing these factors over simply amassing large uncurated datasets. The latter provides minimal learning value.
Though gathering quality training data is challenging and resource-intensive, the accuracy payoff makes it imperative. I advise clients to estimate data needs upfront for the required model performance. For scalable, cost-effective data collection, leveraging reliable data partners is highly recommended. They can source contributors, validate data, handle labelling, and meet specialized needs like multimodal data.
2. Deploying AI/ML Models
Many incorrectly assume model training is the final lap. In reality, thorough testing on new data is vital before deployment. Around 20% of the collected dataset should be reserved as a holdout set for model validation.
This testing phase checks how well the model generalizes to real-world data it hasn‘t encountered during training. If performance metrics dip below thresholds, the model lacks readiness for prime time.
Consider an ML model trained to detect cancer from MRI scans. High training accuracy isn‘t sufficient if it fails to detect tumors on fresh scans consistently. Rigorous validation on a diverse test dataset prevents such unreliable models from reaching patients.
I like to use the workflow above to explain model development stages to clients. The validation/testing loop after training ensures models only get deployed once they demonstrate robust performance on new data.
Post-deployment, models undergo continuous real-world testing. If anomalies surface, models are retrained using additional collected data before re-deployment. Failing to test models on fresh data is the fastest path to failure.
3. Maintaining and Improving AI/ML Models
Here‘s a common misconception – once deployed, models will continue functioning optimally indefinitely. In reality, model performance decays over time without maintenance.
Why does this happen? In two words – concept drift.
Figure 2: Model performance decays over time without retraining or new data. Source: Towards Data Science
As the graph shows, model accuracy can deteriorate by over 20% within months of deployment. Why? Because data distributions tend to shift over time in the real world.
For example, new slang emerges changing the relationship between words and sentiments. Customer preferences evolve so past product ratings become outdated. User behaviors change with new apps altering usage patterns.
Such external shifts render models less effective unless they are retrained using fresh data that incorporates these changes. Continuously monitoring performance, collecting updated data, and retraining is crucial.
Many companies connect to live data streams for real-time model adjustment. For instance, an ecommerce site can retrain its recommendation model daily on new user data. Others opt for less frequent quarterly or annual retraining cycles.
Either way, ongoing data collection is non-negotiable to keep AI/ML models delivering maximum business value. Partnering with data experts is an easy way to obtain fresh, relevant data for this purpose.
4. Enhancing Marketing
Marketing has also grown more data-dependent, be it understanding audiences better or optimizing campaigns. Specific use cases where data collection provides a competitive edge include:
4.1 Conducting Market Research
For marketing-critical activities like new product development, brand health tracking, and message testing, primary market research is irreplaceable.
Survey data reveals customer needs and preferences to guide development. Brand health metrics track evolving consumer perceptions over time. A/B testing copy and creatives identifies what resonates best.
And data is the fuel that powers each of these activities. For instance, a 100 person survey yields more reliable insights than feedback from 10 people. Testing 10 ad variations informs better than just 2.
Generating high-quality, statistically-significant data does have costs attached. I advise clients to carefully scope research data needs – enough to get the insights they seek, but not overkill. Working with expert research partners can help strike this balance.
4.2 Monitoring Customer Sentiment
In our hyper-connected world, understanding what customers feel about your brand on social media and community forums is invaluable. But doing so manually for thousands of daily mentions is impossible.
This is where AI steps in. Sentiment analysis tools automatically parse customer conversations across platforms to surface the voice of the customer. The AI models behind them are first trained on vast datasets of text examples labeled by sentiment.
I‘ve helped several brands implement sentiment tracking programs. The data yields real-time insights on evolving customer issues, product feedback, brand perception shifts and more. But results are only as good as the underlying data – poor training data causes tools to miss or misinterpret crucial posts.
The key is continuously collecting relevant social media and forum content, cleaning it, and labeling sentiments – joy, frustration, sarcasm etc. Refreshing the training data improves accuracy on detecting language nuances. Brands can manage this internally or via expert partners.
5. Search Engine Optimization
SEO success also relies heavily on data collection. On-page elements like titles, headers, meta descriptions, alt text, etc. require carefully crafted text optimized to target keywords.
For example, an outdoor equipment manufacturer selling globally needs translated product page content tailored to rank for local search terms. Done manually, this proves unscalable.
I advise using a data-driven approach instead. First identify keywords by country using tools like Google Keyword Planner and SEMrush. Next, collect translated text optimized per keyword for each product. This helps create pages tailored to rank higher in local markets.
Ecommerce sites also need structured data like product specs, ratings, availability and pricing added to their pages. This provides search engines richer data to index products better. Again, manually aggregating such data across thousands of products is unrealistic.
Outsourcing data needs to experts is an optimal solution. Reliable data partners source location-specific content, translate text, add structured data, and optimize pages to boost traffic and conversions.
The top five applications of data collection are:
- Developing accurate AI and ML models
- Deploying and maintaining effective models
- Retraining models on new data continuously
- Conducting primary market research
- Optimizing web content for higher rankings
As discussed, data powers each of these critical business functions. While the data deluge presents challenges, the benefits call for investment in robust data strategies. For scalable, continuous data flows, partnering with managed data collection services is the smartest approach.
I hope this guide based on my hands-on expertise helps provide a clearer perspective on data collection applications and best practices. The insights can help your business identify and prioritize high-value data sources to drive better decisions and results. As always, I welcome your comments and questions.
